Geomatics doi: 10.3390/geomatics6050102

Authors: Quang Huy Nguyen Alberta Albertella

The utilisation of computer vision in urban studies has become common practice due to its capacity to diminish the financial burden associated with field surveys. Monocular Depth Estimation (MDE) is a recent branch of computer vision that has been shown to be capable of predicting three-dimensional information from a single image. Street View Imagery (SVI) refers to the collection of a substantial dataset comprising urban images. The purpose of this paper is to analyse the potential of applying MDE to Volunteered SVI (VSVI) in urban studies. Following the processes of acquisition and screening, a total of 102 MDE and 42 studies employing SVI are utilised to delineate this potential association. The number of MDE models, training strategies and the volume of training, validation and evaluation datasets have all increased over the years. Notably, MDE models have significantly improved in accuracy through the KITTI benchmark test. Despite the gap between MDE developers and urban study practitioners, as well as the misuse of VSVI, the application of MDE in SVI-based urban studies is substantial. Based on observable potentials, this study further proposes a future framework for using MDE in VSVI-based urban studies, which can contribute to the field of computer vision in a built environment.

Geomatics doi: 10.3390/geomatics6050101

Authors: Evangelia Vaitsi Dimitra Kitsiou Eirini Marinou Betty Charalampopoulou

Coastal waters are vulnerable ecosystems that are increasingly affected by sediment and nutrient inputs, as well as pollution resulting from both natural processes and anthropogenic pressures. Monitoring the pattern of chlorophyll-a and turbidity is essential for understanding the dynamics of coastal ecosystems and supporting environmental management. This study investigates the spatial and seasonal patterns of water quality in the coastal zone of Heraklion, Crete (Greece), for the period 2016–2024 using OCEANIDS Data Cube products (GA 101112919). Water quality variability was assessed using satellite-derived spectral indices, including the Normalized Difference Chlorophyll Index (NDCI) and the Normalized Difference Turbidity Index (NDTI). Monthly observations were analyzed using a GIS-based workflow to assess seasonal spatiotemporal variability. The results revealed strong seasonal variability, with higher winter NDCI values in all coastal zones and persistent hotspots concentrated near river estuaries and waters influenced by port activities. Meanwhile, the analysis was used to prioritize the region for further monitoring and to support decision-making for stakeholders. In summary, this study demonstrates the potential of OCEANIDS Data Cube products (NDCI, NDTI) for long-term monitoring of surface coastal water quality in a Mediterranean environment with limited data and supports evidence-based coastal management.

Geomatics doi: 10.3390/geomatics6050100

Authors: Aida Avdić Marić Ivan Marić Tena Božović

Planning future electric vehicle charging infrastructure requires assessment of spatial suitability, demand, network gaps, and expected accessibility benefits. This study developed a national-scale framework for Bosnia and Herzegovina integrating machine-learning (ML) suitability modelling, post-modelling prioritisation, reproducible candidate selection, and scenario-based population accessibility assessment. A dataset of 188 EVCS locations and spatially balanced pseudo-absences was analysed using 28 spatial predictors. Five classifiers were evaluated through spatial cross-validation, with XGBoost providing the most balanced performance. After feature reduction, the final model retained five predictors: road density, travel time to hotels, travel time to parking, distance to major roads, and travel time to tourist attractions. The priority index combined modelled suitability with population demand, LU/LC opportunity, and the travel-time gap to existing EVCSs. Candidate locations were derived using a deterministic settlement- and road-constrained procedure followed by network-based spacing, and scenarios with 10, 20, 40, 50, and 60 new EVCSs were evaluated. At the 10 min threshold, population coverage increased from 57.9% for the existing network to 65.4% with 10 new EVCSs, 68.7% with 20, 73.8% with 40, 75.6% with 50, and 77.0% with 60. The framework provides a reproducible basis for national EVCS investment screening while distinguishing occurrence-based suitability, strategic deployment priority, and expected accessibility gains.

Geomatics doi: 10.3390/geomatics6050099

Authors: Robert Gradka Andrzej Kwinta Zbigniew Muszyński

Ground deformations induced by underground mining significantly affect the geometric condition and serviceability of buildings located in mining areas. Conventional assessments are commonly based on ground deformation indicators, which do not necessarily reflect the actual structural response. This study presents a terrestrial laser scanning (TLS)-based methodology for assessing the three-dimensional deformation of an eleven-storey residential building located in the Legnica–Głogów Copper District (LGCD), Poland. The analysis was performed using a high-density point cloud acquired from ten scanning positions. Following registration and filtering, building geometry was reconstructed and corner positions were determined from 123 horizontal cross-sections. Horizontal displacements, tilt profiles, and rotation about the vertical axis were subsequently analysed within a local coordinate system. The results revealed pronounced spatial variability in both displacement magnitude and direction. The maximum horizontal displacement reached approximately 0.18 m, corresponding to a local tilt of 5.9 mm/m. Corner displacements at the highest common observation level ranged from 8.6 mm to 178.3 mm, indicating that the observed geometry is inconsistent with a simple rigid-body model subjected to uniform tilting. Analysis of geometric changes with height further identified an overall increase in torsional rotation with height, accompanied by local variations. Comparison of TLS-derived geometry with a theoretical mining-induced ground deformation model showed that the measured structural response does not directly reproduce the underlying ground deformation pattern. The largest discrepancies occurred along the building longitudinal axis, indicating that structural stiffness and soil–foundation–structure interaction significantly modify the transfer of ground movements to the superstructure. These results demonstrate the capability of TLS for detailed assessment of mining-affected buildings and provide quantitative insight into the relationship between ground deformation and actual structural response.

Geomatics doi: 10.3390/geomatics6050098

Authors: Zofia Kuzevicova Stefan Kuzevic Diana Bobikova Michal Roman Miroslava Stolična Vancova

Surface mining significantly alters land cover and vegetation, making long-term monitoring essential for assessing its environmental impacts. The objective of this study was to analyze the long-term development of the active Dargov quarry (Slovakia) during the period 2009–2025 using Landsat image time series. Changes in vegetation cover and exposed surfaces were assessed using the NDVI, BSI, and NDBI spectral indices in combination with the Mann–Kendall test, Sen’s slope estimator, and the LandTrendr algorithm. The results revealed a gradual decline in NDVI values accompanied by a concurrent increase in BSI and NDBI values within the active quarry, reflecting the expansion of exposed rock and soil surfaces associated with ongoing mining. Statistically significant trends (p < 0.05) were identified in 76.36% of NDVI pixels, 65.45% of BSI pixels, and 63.64% of NDBI pixels within the deposit boundary. The relationships between spectral indices and quarry production were also evaluated using Pearson’s correlation analysis. The spectral indices showed a substantially stronger relationship with cumulative quarry production than with annual production. The LandTrendr algorithm identified the most pronounced vegetation-cover changes primarily during 2019–2024, with 70.91% of the pixels in the Time of Largest Change output within the deposit boundary falling within this period. The proposed methodology provided a comprehensive assessment of the spatial and temporal patterns of changes associated with surface mining at the Dargov quarry and demonstrated its potential to support environmental monitoring, environmental impact assessment (EIA), and mineral resource management.

Geomatics doi: 10.3390/geomatics6050097

Authors: Simon Donike Enrique Portalés-Julià Cesar Aybar Luis Gómez-Chova

Super-resolution (SR) of remote-sensing imagery is commonly assessed through spatial fidelity or visual sharpness, although index-based geomatics requires preservation of cross-band spectral relationships. We compare five Sentinel-2 full-band SR configurations (LDSR-S2, SRGAN, SPAN, Mamba, and SWIN) in two hazard-mapping cases: flood-water detection during the 2024 Valencia flood and burn-scar mapping after the 2025 Palisades wildfire. Each model refines four native RGB–NIR bands, while SEN2SR reconstructs the remaining bands to produce a ten-band product at 2.5 m. We evaluate native-grid reconstruction, the introduction of high-frequency details, and downstream thematic, boundary, and edge-region metrics against a bilinear-interpolation baseline. Dynamic-threshold MNDWI and dNBR detectors are applied independently to each output. Among the learned configurations, SWIN achieves the strongest native-grid reconstruction and task-specific spectral consistency and the strongest fire agreement, but adds the least high-frequency content. Flood full-ROI gains are modest, with LDSR-S2 increasing the F1-score from 0.085 for bilinear interpolation to 0.091. All learned configurations increase flood-edge recall, F1-score, and IoU while reducing edge precision and balanced accuracy. LDSR-S2 gives the strongest final edge F1-score and IoU in both tasks, Mamba gives the lowest learned-model symmetric flood-boundary distance. SPAN yields the best spatial consistency and lowest learned flood-edge spectral error and SRGAN adds the most high-frequency content and the largest combined edge-region gain, alongside the greatest task-specific spectral deviation and the largest symmetric boundary-distance increases. Thus, increased edge activation does not establish uniformly improved delineation or recovered sub-pixel detail. These cases demonstrate feasibility rather than generalization. Operational validation requires more diverse, time-synchronous, high-resolution, spectrally compatible references.

Geomatics doi: 10.3390/geomatics6050096

Authors: Martin Enrique Romero-Sanchez Gustavo Manuel Cruz-Bello Fernando Carrillo-Anzures Miguel Acosta-Mireles

Peri-urban forest ecosystems play a crucial role in sustaining biodiversity, regulating climate, and providing essential ecosystem services; however, they are increasingly threatened by rapid urban expansion. Despite advances in remote sensing-based forest monitoring, long-term reconstruction of continuous forest canopy cover dynamics remains challenging in highly fragmented peri-urban landscapes. This study developed a machine-learning workflow to reconstruct forest canopy cover dynamics within the “Suelo de Conservación” of Mexico City between 1994 and 2024 using Landsat imagery and forest canopy cover information derived from the Hansen Global Forest Change dataset. A balanced training dataset comprising 5000 samples distributed across five forest canopy cover classes was used to compare four regression algorithms (Multiple Linear Regression, Random Forest, Gradient Boosting, and Multilayer Perceptron) under five-fold spatial cross-validation. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), bias, Pearson’s correlation coefficient (r), coefficient of determination (R2), and Lin’s Concordance Correlation Coefficient (CCC). The best-performing model was applied to generate forest canopy cover maps for 1994, 2003, 2014, and 2024, and forest-cover change was quantified using propagated uncertainty and threshold sensitivity analysis. The reconstructed forest canopy cover maps revealed an initial decline between 1994 and 2003, followed by partial recovery during 2003–2014 and relatively stable forest canopy cover conditions through 2024. Independent comparison with the National Forest and Soils Inventory (INFyS) and Global Forest Watch forest canopy cover products indicated moderate agreement in the spatial distribution of canopy cover while highlighting uncertainties associated with differences in reference datasets and acquisition periods. The proposed workflow provides a transparent and reproducible framework for long-term forest canopy cover reconstruction using freely available satellite imagery and supports forest monitoring and conservation planning in peri-urban landscapes.

Geomatics doi: 10.3390/geomatics6050095

Authors: Elena Martínez-Mateo Ana B. Ruescas

In this study, we used the DeepESDL framework to exploit multivariate data cubes for the identification and characterization of frontal zones in the Alboran Sea throughout the year 2023. By operating directly within a unified data hypercube architecture, we seamlessly evaluate the spatial coherence and temporal evolution across highly disparate environmental variables. We adapted the Gradient Histogram Method (GRADHIST) algorithm to run efficiently on data cube tracks of sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), and chlorophyll-a concentration (CHL) fields. This multi-dimensional approach enabled a detailed, synchronized description of the spatial distribution and evolution of fronts. The GRADHIST algorithm proved highly adaptable to the data cube format, utilizing a dynamic threshold that optimizes front detection across fields of fundamentally different physical natures and dynamic ranges.

Geomatics doi: 10.3390/geomatics6050094

Authors: Emil-Cătălin Șchiopu Oliviu-Mihnea Gămulescu Florin Grofu Roxana-Gabriela Popa Irina-Ramona Pecingină Adrian Runceanu

Smartphone GNSS metadata remains an underexplored source for evaluating navigation-signal integrity in real-world conditions. We investigated GPS behavior recorded by a Samsung Galaxy A72 smartphone across two European flights (EXP03: Rome–Bucharest, n = 377; EXP10: Bucharest–Lisbon, n = 78) and 275 terrestrial reference photographs (Cabo da Roca, Portugal). A total of 730 photographs were analyzed using a seven-indicator taxonomy, conceptually inspired by Receiver Autonomous Integrity Monitoring (RAIM) principles, covering anti-spoofing, anti-sniffing, and anti-tampering checks. GPS capture rates reached 100% (Timestamp Camera) and 83.3% (native camera) up to 11,439 m WGS84, among the highest EXIF altitude profiles reported to date. Velocity spikes (1560–1875 km/h), one at cruise altitude and one during landing, were indistinguishable from GPS spoofing at the EXIF level, and their physical origin is undetermined. Phantom geolocation was absent in flight (0/442) versus 37.3% on the ground, consistent with GPS constellation visibility as the primary factor. In total, 88% (n = 920) of JPEG files lacked the standard EOI marker, generating false positives in integrity validators. Findings are device-specific, derived from non-independent observations, and require replication before generalizing to other GNSS receivers or latitudes. The framework offers a methodological basis for EXIF integrity analysis relevant to EU AI Act Article 10(3) data quality and UAV precision-agriculture georeferencing.

Geomatics doi: 10.3390/geomatics6050093

Authors: Ivar Kapsi Tarmo Kall Kristina Türk Aive Liibusk

Shoreline determination is fundamental to coastal research, spatial planning, and legal boundary delineation but remains challenging in low-relief coastal areas where small sea-level variations can produce substantial horizontal shoreline displacements. This study compares shoreline determination methods based on tide gauge (TG) observations, LiDAR data, Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical satellite imagery using the low-relief coast of Pärnu Bay, Estonia, as a case study. The comparison was based on shorelines derived from Sentinel-1 and Sentinel-2 imagery acquired on selected common acquisition dates within the 2015–2025 study period, rather than on a temporally continuous annual dataset, and compared with temporally matched LiDAR-derived shorelines extracted from a Digital Terrain Model (DTM) generated from a 2021 LiDAR survey. The LiDAR-derived shorelines were extracted using the mean sea level (MSL) observed at the Pärnu and Häädemeeste TGs at the satellite overpass time, while the satellite-derived shorelines were additionally validated against RTK GNSS measurements. The results demonstrate that the evaluated methods produce substantially different shoreline positions. Sentinel-2-derived shorelines generally corresponded more closely to the temporally matched LiDAR-derived shorelines than Sentinel-1-derived shorelines and most accurately represented the instantaneous land–water boundary during field validation. These findings demonstrate that different shoreline determination methods represent different shoreline definitions. Consequently, shoreline datasets should be interpreted according to their intended purpose rather than treated as directly interchangeable.

Geomatics doi: 10.3390/geomatics6040092

Authors: Jiting Tang Zhiwei Liang Suli Guo Bin Tong Jun’an Chen Guoliang Sun Jiaxing Liu Can Wang Dong Li Xin Zhou

Small- and medium-sized landslides frequently occur in clusters and exhibit fragmented morphologies, irregular boundaries, and spectral characteristics similar to surrounding roads, bare soil, and sparsely vegetated surfaces, making their automated extraction from remote sensing imagery challenging. Although the Segment Anything Model (SAM) provides strong general-purpose segmentation capabilities, its direct application to landslide mapping is limited by the geoscience domain gap and its dependence on external prompts. This study proposes the Asymmetric Boundary-aware Segment Anything Model (AB-SAM), a parameter-efficient adaptation of SAM for automated landslide semantic segmentation. AB-SAM integrates three task-specific components. First, the offline Multi-Feature Variation-Guided Prompting (MF-VGP) module generates cached auxiliary bounding boxes from registered pre- and post-event images without accessing ground-truth masks. Second, the Asymmetric Feature Augmentation (AFA) strategy combines geometric perturbation, CutMix, and asymmetric dual-branch supervision, in which a Hint-free branch serves as the primary optimization pathway and a lower-weight box-guided branch provides auxiliary spatial supervision. Third, the Boundary-Aware Morphological Prompting (BAMP) module injects trainable boundary-aware morphological information into the largely frozen SAM image encoder. During validation, testing, and application, only the Hint-free branch is retained, enabling inference using post-event imagery without external point, box, or mask prompts. On the fixed, spatially disjoint Zixing test set, AB-SAM achieved an overall accuracy of 96.171%, a precision of 68.149%, a recall of 60.011%, an F1-score of 63.822%, a landslide-class Intersection over Union of 46.867%, and a mean Intersection over Union of 71.452%. Repeated experiments with three random seeds showed low run-to-run variation. Direct evaluation without retraining on the Hokkaido Iburi-Tobu dataset yielded a mean Intersection over Union of 66.136%, providing evidence of cross-region and cross-event transferability. These results demonstrate that AB-SAM provides a practical parameter-efficient framework for automated, hint-free landslide segmentation, although further evaluation across additional regions, sensors, and landslide-size distributions remains necessary.

Geomatics doi: 10.3390/geomatics6040091

Authors: Ryota Miyazaki Hiroki Naito Fumiki Hosoi

Accurate identification of the spatial distribution of agricultural greenhouses is essential for regional agricultural management in Japan. However, an efficient method for systematically mapping greenhouse locations onto existing farmland maps has not yet been established, and field surveys over large areas require substantial time and labor. This study aims to develop an automated approach for detecting agricultural greenhouses and integrating their locations into farmland maps by combining PlanetScope satellite imagery with farmland polygon data developed by the Japanese government. To improve the efficiency of the extraction process, farmland polygons were used to restrict the analysis to known agricultural areas, thereby reducing false detections originating from non-agricultural land. Within these predefined regions, three machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and Isolation Forest (ISF)—were applied to classify and extract greenhouse features from satellite imagery. After optimizing the hyperparameters of all models, RF and SVM achieved an equivalent peak performance, with an F1-score of 0.86, while ISF reached 0.72. RF was, however, markedly more robust to the polygon-level decision threshold, demonstrating a practical advantage in situations where the threshold cannot be optimized in advance. In addition, an ablation experiment confirmed that without pre-masking with farmland polygons, 81.9% of the pixels predicted as greenhouse were distributed outside the agricultural parcels. The proposed method is expected to serve as an effective approach for efficiently identifying the distribution of agricultural facilities and integrating them with existing farmland information in regions characterized by small and fragmented agricultural fields, such as Japan.

Geomatics doi: 10.3390/geomatics6040090

Authors: Natascha Christina Pichler Muckenhuber Stefan Friehmelt Holger Wagner Bastian Läßer Andreas Okorn Robert Wallner Stefan Gölles Thomas Wasserfaller Hannah Dunke Leonie Schlager Birgit Klasnic Stefan Herzog Franziska Maierbugger Marie-Christine Bagladi Peter Breitwieser Stefan

Remote-controlled UAS-based soil sampling offers great potential for scientific research and practical applications in various fields, including agriculture, environmental monitoring, hazardous waste, radiation measurements, chemical plants, snow sampling and glaciology. The added value of automated sampling using a UAS (unmanned aircraft system) compared to manual sampling lies primarily in improved accessibility to hazardous or remote locations, increased operational safety, and the potential to improve efficiency in applications requiring repeated or difficult-to-access sampling. This article introduces two different 3D-printed constructions (grab arm and screw pipe) for UAS-based, remote-controlled sample collection of different soil types. The grab arm construction is based on an adapted version of an open-source CAD from GrabCAD. The screw pipe construction is a new design. During an expedition to Greenland in August 2025, the two constructions were manually evaluated during several days of field work near the Danish and Austrian research stations at the Sermilik Fjord to assess their mechanical sampling performance on challenging Arctic surface materials, including dry and wet sand, gravel, glacial sediment, snow, and glacier surfaces. Because flight testing was not possible during the expedition due to unavailable UAS batteries, these experiments were limited to manual ground evaluation of the constructions. Independent flight tests were subsequently conducted in Austria using a DJI Matrice 300 to evaluate the integration of both the grab arm and the screw pipe with the UAS platform and their operational handling during flight. In Greenland the performance of manual sampling was documented precisely. Further the coordinates of each sampling point were recorded by using GPS, sample images were taken on site, and the collected material was weighed. The results show that UAS in combination with 3D printed constructions is a flexible and location-independent solution for obtaining soil samples of varying composition. In addition to the design, materials, and electronics of the systems, the article also describes the connection to the UAS and the field work results.

Geomatics doi: 10.3390/geomatics6040089

Authors: Lorenza Bovio Victor Miherea Jannis Fath Piero Boccardo Enrico Borgogno-Mondino

Remote sensing is widely recognized as a key technology across a wide range of technical and scientific domains, especially in agriculture. Although satellite data have long supported crop monitoring, their limitations in spatial resolution, revisit frequency and cloud coverage have often constrained their applications. High-resolution satellites, available from the beginning of the 2000s, have improved performance, particularly in the field of precision agriculture, but they remain expensive and inflexible. Unmanned Aerial Vehicles perform better in precision agriculture, offering flexibility and high levels of detail; however, their limited operational areas and short endurance flight times constrain their effectiveness. In this evolving landscape, High Altitude Pseudo Satellites (HAPSs), particularly high-altitude balloons, are emerging as a promising new technology that could fill the gaps between satellite and drone remote sensing. These platforms provide large area coverage with high-resolution imagery and long endurance flights at low operational expenses and ease of deployment. This study investigates the operational characteristics, strengths, and geometric limitations of data acquired by the CubeHAPS® platform, a high-altitude pseudo-satellite system, as a prerequisite for its application in precision agriculture. Focusing on experimental campaigns conducted in northern Italy in summer 2024 and 2025, the research characterizes platform stability, image block consistency, and photogrammetric quality through internal metrics. The results demonstrate measurable improvements between the two campaigns, attributed to the introduction of a stabilization system in 2025 and establishing the conditions under which the platform can support reliable photogrammetric reconstruction.

Geomatics doi: 10.3390/geomatics6040088

Authors: Kyriakos Michaelides Athos Agapiou

Cultural heritage sites are affected by environmental and anthropogenic pressures that require decision-analysis methods capable of combining heterogeneous datasets while accounting for uncertainty. Multi-Criteria Decision Analysis (MCDA), particularly when integrated with Geographic Information Systems (GIS) and Earth Observation (EO) data, is widely used in geospatial analysis involving multiple, often conflicting criteria. This review examines the evolution, application domains, and methodological challenges of MCDA in cultural heritage risk assessment. The literature indicates a predominant reliance on weighting-based methods, especially the Analytic Hierarchy Process (AHP) combined with GIS-based weighted overlay techniques, while uncertainty treatment, temporal monitoring, validation, and multi-threat applications remain limited. Three illustrative applications show that asset-level, regional susceptibility, and historic-urban frameworks address complementary decision needs but differ in their data, expertise, and institutional requirements. Recent developments show a trend to combine MCDA with fuzzy logic, machine learning, and uncertainty modeling, although methodological consistency across these approaches remains uneven. The findings suggest that multi-criteria risk assessment for cultural heritage may depend less on introducing new analytical techniques and more on improving the integration of existing methods. Incorporating repeatable environmental observations, sensitivity analyses, multi-threat assessment, and stakeholder participation may support a more coherent and reproducible approach to heritage-risk assessment.

Geomatics doi: 10.3390/geomatics6040087

Authors: Siphokazi Ruth Gcayi Samuel Adewale Adelabu Wonga Masiza George Johannes Chirima

Effective control and management of the encroaching and invasive Vachellia karroo (V. karroo) in grassland and savanna biomes depends on accurate information about its spatial distribution, making remote sensing approaches essential for mapping the extent of affected areas. Although Sentinel-2 satellite data are widely used for land use and land cover applications, they often lack the spatial details required to distinguish woody species like V. karroo. The fusion of Sentinel-2 data with high-resolution UAV imagery offers a promising approach to enhance spectral information for species-level discrimination. This study evaluated UAV, Sentinel-2, and fused UAV–Sentinel-2 imagery for discrimination of V. karroo in grassland and savanna biomes of the Eastern Cape, South Africa. Field data and imagery were collected in October 2022 and classified using Random Forest (RF) and Support Vector Machine (SVM) algorithms to distinguish V. karroo. The findings showed that V. karroo was more prevalent in the savanna biome. SVM marginally outperformed RF in classifying V. karroo in the grassland biome, achieving overall accuracies ranging from 68.9% to 97.4%, compared to 57.8% to 97.4% for RF. Among the datasets, the fused UAV–Sentinel-2 images yielded the highest classification accuracy, with an overall accuracy of 97.4% and a kappa coefficient of 0.96. The UAV images also demonstrated high classification accuracy, with an overall accuracy of 91.67% and a kappa coefficient of 0.77, confirming its value for fine-scale mapping and reference data support. In contrast, the Sentinel-2 images produced lower classification accuracy, with an overall accuracy of 84.6% and a kappa coefficient of 0.75, mainly due to their coarser spatial resolution. Classification was more challenging in the savanna site, where mixed vegetation structure increased confusion between V. karroo and grass. These findings show that fused UAV–Sentinel-2 images can improve species-level discrimination, while UAV and Sentinel-2 data remain complementary for fine-scale mapping and broader monitoring of bush encroachment in grassland and savanna ecosystems.

Geomatics doi: 10.3390/geomatics6040086

Authors: Rubén Santamaría-Maestro María Sánchez-Aparicio Andrea Martín-Crespo Luis Javier Sánchez-Aparicio

Digital platforms for cultural heritage are increasingly expected to document the geometric and material properties of sites. Furthermore, such platforms are increasingly expected to preserve and communicate the intangible practices and community knowledge that give these places cultural significance. In this context, this paper presents a hybrid virtual reality platform for the documentation, communication, and dissemination of both tangible and intangible heritage in underground wine landscapes. The framework integrates 360° panoramic imagery, 360° videos, lightweight object visualisations, and georeferenced 3D point clouds within a unified interface adapted to different device capabilities. The system’s key contributions include seasonal navigation, participatory recording of community practices, multiscale representation of artefacts and architecture, and a guided narrative system that improves orientation for non-expert users. The platform has been validated through the underground wineries of Baltanás (Spain), thereby demonstrating a novel approach that brings together metric documentation and immersive storytelling. This enhances accessibility, facilitates heritage interpretation, and ensures the digital preservation of living cultural practices in complex heritage settings with broad public dissemination potential.

Geomatics doi: 10.3390/geomatics6040085

Authors: Dimitrios Ampatzidis Aristotelis Vartholomaios Dionysia-Georgia Ch. Perperidou Nikolaos Demirtzoglou

Parcel area sits at the intersection of urban planning, land administration and land surveying. It underpins development intensity, floor area allocation, minimum lot thresholds, land readjustment and value capture mechanisms. Yet discrepancies between modern measurements and ownership titles are usually evaluated through fixed tolerance formulas rather than quantified confidence intervals. While coordinate precision is routinely specified, the uncertainty of the derived parcel area is seldom expressed explicitly, limiting the traceability of planning calculations based on cadastral geometry. This paper presents a variance-based formulation for estimating parcel area uncertainty from boundary coordinates. Using the Gauss area function and first-order propagation, vertex precision is translated into parcel-level confidence intervals based on horizontal RMS parameters commonly reported in cadastral practice, including documented transformation accuracy. The Greek cadastre provides an illustrative case combining a national GNSS infrastructure, a unified reference system and formula-based area screening embedded in statutory workflows. Illustrative examples show how area uncertainty varies with parcel geometry and measurement origin. Absolute uncertainty increases with parcel size and boundary elongation, while relative uncertainty decreases with parcel size. A Monte Carlo analysis of the error-correlation structure shows that the diagonal, independent model is not a universal bound: depending on the structure of the transformation error and on parcel geometry it may either overstate or understate the true area uncertainty, by factors between about 0.4 and 3.5 in the cases examined. The results clarify how coordinate precision propagates into regulatory-relevant area values and support more transparent interpretation of area discrepancies in planning and land administration contexts.

Geomatics doi: 10.3390/geomatics6040084

Authors: Lizbeth G. Santiago-Sánchez Rosendo Romero-Andrade Ana I. Vidal-Vega Evangelina Ávila-Aceves Naccieli Bojorquez-Pacheco

Global Navigation Satellite Systems (GNSS) have emerged as a reliable and cost-effective tool for estimating atmospheric precipitable water vapor (PWV), particularly in regions with limited meteorological instrumentation. In this study, the performance of three tropospheric mapping functions—the Global Mapping Function (GMF), Niell Mapping Function (NMF), and Vienna Mapping Function 1 (VMF1)—was evaluated for PWV estimation using GPS observations collected during the 2009–2011 period in southwestern Mexico, a region characterized by high atmospheric variability and frequent extreme weather events. GPS data from three stations (TECO, COL2, and PENA) were processed using the GAMIT/GLOBK 10.71 software, and the resulting PWV estimates were validated against independent radiosonde observations and the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth-Generation Reanalysis (ERA5) data. The results show that GPS-derived PWV successfully captures the seasonal variability of atmospheric water vapor, with maximum values during the summer rainy season. High correlations were obtained with both radiosonde and ERA5 data, particularly at the TECO station (R = 0.95–0.99), where RMSE values ranged from 3.27 to 5.46 mm and BIAS values from −2.73 to −1.47 mm. In contrast, larger discrepancies were observed at COL2 and PENA, mainly due to horizontal separation and altitude differences relative to the radiosonde site, highlighting the importance of spatial representativeness during validation. Among the evaluated mapping functions, no single model consistently outperformed the others across all stations, years, and reference datasets. Nevertheless, GMF and NMF generally exhibited more stable and consistent performance, whereas VMF1 showed greater variability under the adopted processing strategy. Additionally, a clear relationship was identified between PWV and precipitation records, indicating that increases in PWV coincided with periods of intense rainfall and suggesting its potential as an indicator of atmospheric conditions favorable for precipitation events. Overall, this study shows that GPS-derived PWV can reproduce the seasonal variability of atmospheric water vapor under the adopted processing strategy and demonstrates the importance of mapping function selection and spatial representativeness for accurate PWV estimation.

Geomatics doi: 10.3390/geomatics6040083

Authors: Luca Morelli Neil Sutherland Francesco Ioli Alfonso Vitti Stuart Marsh Jon Mills Paul Bryan Fabio Remondino

InfraRed Thermography (IRT), a non-invasive, non-contact, and non-destructive testing (NDT) technique, has become an established tool in the assessment of a building’s behavior and energy performance. However, the inherent low spatial resolution of thermal infrared (TIR) cameras has led recent work to fuse thermographic and geometric data to generate accurate 3D representations of buildings encapsulating temperature information. Whilst existing data fusion methods have relied on sensors in fixed relative orientation (RO), the co-registration of independent TIR and RGB blocks using ground control points (GCPs), or the reprojection of TIR images onto additional geometric or parametric models, approaches that directly match multi-modal images remain limited. In principle, if multi-modal tie points were available, it would be possible to directly align the RGB block with the TIR block; however, such matching is extremely challenging due to the substantial differences in radiometric properties. The main contribution of this paper is to demonstrate the applicability of off-the-shelf deep learning-based image matching algorithms, originally trained on mono-modal datasets, to multi-modal matching tasks for InfraRed Thermography 3D-Data Fusion (IRT-3DDF). We conduct a comparative evaluation of the principal algorithms developed in recent years, with particular emphasis on 3D accuracy and computational efficiency, under the hypothesis that, owing to the inherently local nature of the problem they address, these algorithms can generalize from a mono-modal training domain to a multi-modal application domain. The results are benchmarked against existing hand-crafted open-source multi-modal reference methods. Importantly, the proposed method is fully-automatic, obviating the need for sensor pre-calibration, manual co-registration, or associated positioning information. Results demonstrate that DL-based image matching, using pre-trained neural networks outside of their expected training domain, provides a viable approach for IRT-3DDF capable of co-registering blocks of multi-modal images across varying scales, settings, sensors, and subjects. Our results indicate accuracy in 3D is up to seven times better than multi-modal hand-crafted algorithms, while hand-crafted mono-modal methods fail to co-register images in their entirety.

Geomatics doi: 10.3390/geomatics6040082

Authors: Hendry Yuli Wibowo Trias Aditya Nurrohmat Widjajanti

Geospatial data quality assurance is fundamental to effective land administration, yet regulatory frameworks vary significantly in their incorporation of internationally recognized quality concepts. This study conducts a systematic regulatory gap analysis assessing the extent to which Indonesian land registration regulations incorporate the quality concepts, measures, evaluation procedures, and reporting requirements defined by ISO 19157:2013. Employing a mixed-methods approach combining qualitative document analysis with a quantitative four-level scoring rubric across ten quality dimensions, we benchmark six Indonesian regulatory instruments against ISO 19157 and five international geospatial quality frameworks (INSPIRE, FGDC, ANZLIC, OGC, and OSM). Results reveal that the Indonesian regulatory framework achieves only 37% overall compliance (11/30 points), compared to 100% for ISO 19157 and INSPIRE, 77% for ANZLIC, 73% for FGDC, 67% for OGC, and 53% for OSM. Critical regulatory gaps exist in temporal quality, usability, lineage documentation, metadata integration, conformance testing, and quality assurance documentation. Inter-rater reliability analysis (Fleiss’ Kappa = 0.78–0.82) confirms the robustness of the scoring process. Based on these findings, we propose a phased, context-appropriate reform roadmap comprising five recommendations with short-term (Year 1), medium-term (Years 2–3), and long-term (Years 4–5) implementation timelines for achieving regulatory alignment with international standards. This regulatory baseline provides the essential foundation for subsequent empirical assessments of cadastral data quality in Indonesian land administration.

Geomatics doi: 10.3390/geomatics6040081

Authors: Edoardo Ronco Mirco Balin Samuele De Petris Salvatore Tuand Marco Gianinetto Enrico C. Borgogno-Mondino

Reference evapotranspiration (ET0) is essential for irrigation management, but its spatial estimation is limited by sparse meteorological observations. Satellite products such as MOD16 potential evapotranspiration (PET) provide spatial continuity, yet they are not directly comparable to FAO-56 ET0 due to structural differences in model parameterization. This study proposes a topography-constrained framework to convert MOD16 PET into FAO-consistent ET0. The approach was tested in two heterogeneous regions of northern Italy (Piemonte and Veneto) using ground-based ET0 derived from the FAO Penman–Monteith equation (2010–2022). PET–ET0 transformation coefficients, estimated via station-wise linear regression, showed no significant temporal drift over the study period and strong spatial structure. Among the tested topographic predictors, elevation was retained as the main topographic proxy for modelling the spatial variability of the correction coefficients. The locally calibrated correction reduced the systematic overestimation of raw MOD16 PET and improved agreement with station-based ET0 in both regions. Its performance was comparable to an IDW interpolation benchmark, although IDW slightly outperformed the topography-based model in Piemonte. A cross-region test showed that the correction reduced MOD16 PET errors when transferred between Piemonte and Veneto, but residual bias remained. The proposed framework should therefore be interpreted as a parsimonious regional topographic correction approach that requires local calibration and validation before application to other areas.

Geomatics doi: 10.3390/geomatics6040080

Authors: Ayoub Daiz Abderrazak El Harti El Hassania El Hamzaoui Jaouad El Atiq Soufiane Hajaj

Climate variability represents a major threat to agricultural systems, particularly in arid and semi-arid regions such as the Beni-Amir irrigated perimeter, located in the Tadla plain in central Morocco. In this perimeter, olive trees are exposed to multiple environmental and management-related factors that are associated with variations in phenology and vegetation vigor, such as successive drought episodes. This study represents a spatio-temporal assessment of drought impact on olive using satellite- derived vegetation indices from Sentinel-2 imagery and precipitation satellite data from CHIRPS over the period 2015–2024. The Standardized Precipitation Index (SPI-12) was used to identify wet and dry phases over this period. The results indicate an alternation of dry and wet periods between 2015 and 2021, followed by a predominance of dry conditions from September 2021. Over the same period, the time series of the Normalized Difference Vegetation Index (NDVI) and the other vegetation indices reveals marked interannual variability and a progressive degradation of olive tree phenological cycles. A land cover map derived from a supervised support vector machine (SVM) under three classification scenarios achieved high overall accuracies exceeding 94%. Post-classification change detection highlights a substantial reduction in mapped olive-growing areas between 2016 and 2024, with an estimated 72% loss of the initial area. The findings reported in this study indicate that the succession of drought episodes may have contributed to olive grove degradation, including disruptions in phenological cycles and a decline in maximum NDVI values. Even the most resilient olive groves appeared affected following the severe drought period after 2021. The study underscores the usefulness of satellite-derived vegetation indices and drought indicators for the effective monitoring of drought-related stress and supporting improved management practices under climate change.

Geomatics doi: 10.3390/geomatics6040079

Authors: Gabriele Nolè Antonio Lanorte Giuseppe Cillis

Wildfires are an increasing threat to Mediterranean ecosystems and populated areas. This study proposes an innovative static wildfire risk assessment methodology for the Apulia Region (southern Italy). The framework produces a Fire Risk Global Index (FRGi) by spatially combining two sub-indices related to risk, which are hazard and vulnerability, in line with European Community and United Nations guidelines. Hazard is quantified through five sub-indices—vegetational, historical, climatic, morphological, and anthropogenic—combined into a Long-Term Danger Index (LTDi). Vulnerability integrates ecological, economic, and wildland-urban interface components into a Fire Vulnerability Index (FVi). All processing was performed in an open-source GIS environment (QGIS) at a spatial resolution of 20 m, ensuring full reproducibility for public administrations. Preliminary validation confirms the internal consistency of the model, demonstrating a statistically significant relationship between risk classes and historical fire occurrence. Beyond its scientific contribution, the methodology serves as an operational tool for civil protection planning at the regional scale.

Geomatics doi: 10.3390/geomatics6040078

Authors: Mostafa Mashal Doaa Amin Mona A. Hagras Ashraf M. Elmoustafa

Flash floods are among the most destructive natural hazards, often causing substantial loss of life and severe damage to infrastructure and property. Predicting flood-prone areas remains challenging because flood generation is controlled by complex interactions among topographic, hydrological, climatic, and environmental factors. In this study, six machine learning algorithms—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree Classifier (DTC), AdaBoost, and Artificial Neural Network (ANN)—were developed to predict flash-flood inundation locations using satellite-derived flood inventories from two major rainfall events in Wadi El-Darb and Wadi El-Allaqi, Egypt. Model performance was evaluated using accuracy, precision, recall, and F1-score. During model development, Random Forest and Decision Tree Classifier achieved the highest prediction accuracy (94%), followed by AdaBoost and ANN (92%), while Logistic Regression (89%) and SVM (88%) also produced satisfactory results. To evaluate model generalization, the trained models were independently validated using a rainfall event in Wadi Hodein (Egypt) and a major flash-flood event that occurred in Oman during April 2024. The external validation showed that AdaBoost achieved the highest predictive performance in both validation basins, with accuracies of 87% for Wadi Hodein and 83% for Oman, providing encouraging initial evidence of applicability across hydrologically similar arid watersheds, While AdaBoost and Logistic Regression maintained satisfactory performance during external validation, other algorithms exhibited noticeable reductions in recall and F1-score, particularly in the Oman case study, indicating variability in model generalization across independent watersheds These findings suggest that the proposed framework may support flood susceptibility assessment in ungauged arid environments with comparable hydrological characteristics, although further validation across a wider range of climatic and geological settings is needed. Overall, the results highlight the value of integrating satellite remote sensing with machine learning to support flood hazard assessment, disaster preparedness, early warning systems, and flood risk management in data-scarce regions.

Geomatics doi: 10.3390/geomatics6040077

Authors: Sondos Omar Reza Shahidi Masoud Mahdianpari Fariba Mohammadimanesh

High-resolution land cover classification is critical for monitoring environmental change and managing natural resources. This study presents an unsupervised framework with externally guided feature prioritization that integrates Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery at 10 m spatial resolution. A cloud-native export protocol in Google Earth Engine (GEE) enables the generation of consistent, cloud-free, and snow-free seasonal composites across Ontario, Canada. A comprehensive feature engineering pipeline combines spectral indices, radar backscatter metrics, terrain derivatives from digital elevation models (DEMs), and temporal statistics to create a rich multi-sensor input space. Dimensionality reduction is performed using Sparse Principal Component Analysis (SparsePCA) and mutual-information-based feature selection. Clustering is conducted using three complementary algorithms: centroid-based K-means, density-based Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and reachability-based Ordering Points To Identify the Clustering Structure (OPTICS). Final land cover labels are assigned via a majority-voting ensemble, with prediction ties resolved deterministically using OPTICS. OPTICS is particularly effective for modeling heterogeneous landscapes due to its ability to detect clusters of varying density without requiring a global threshold. This study is designed as a pilot-site methodological demonstration using three representative 2 km × 2 km regions in Ontario, rather than a full provincial-scale land cover product. The resulting classification maps are validated against reference land cover data, demonstrating the effectiveness and potential scalability of the proposed external-label guided unsupervised mapping approach.

Geomatics doi: 10.3390/geomatics6040076

Authors: Natnael Agegnehu Ayele Robert Tenzer Franck Eitel Kemgang Ghomsi Andenet Ashagrie Gedamu Muralitharan Jothimani

Robust and accurate lithological parameters are essential in engineering, geology, geophysics, geodesy, and resource exploration. Among these parameters, rock density plays a fundamental role in gravimetric geodesy and geophysics. However, the systematic collection, analysis, and categorization of rock density data remain insufficient in many countries around the world, including Ethiopia. Ethiopia is characterized by extreme topographic variations (exceeding 4500 m) and complex geology, dominated by Cenozoic volcanic formations associated with the East African Rift System. Consequently, the commonly adopted upper continental crustal density of 2670 kg/m3 is inadequate for precise geodetic applications (e.g., the definition and realization of the geodetic vertical datum) as well as for gravimetric modeling and interpretation (e.g., the compilation of Bouguer, isostatic, and mantle gravity maps) in the country. To address these limitations, we prepared the first comprehensive digital rock density model of Ethiopia, with a particular focus on its applications in gravimetric geodesy and geophysics. The rock density model has been prepared by integrating the Ethiopian geological database, comprising 88 lithological units, with established global rock-density databases to assign representative density values and their uncertainties to each geological unit. The height-weighted average densities, accounting for the mass contribution of elevated terrain, were computed from a 90-m-resolution digital elevation model. The rock density map shows significant density variations across Ethiopia, ranging from 1528 to 2892 kg/m3. The average height-weighted density of Ethiopia is 2430 ± 352 kg/m3, which is 9% lower than the standard density of 2670 kg/m3. We expect that the use of the rock density model instead of assuming only a constant density value for the whole country will improve the accuracy of gravimetric geoid modeling and orthometric height determination, both essential for the modernization of the geodetic vertical datum. This demonstrates the necessity of region-specific density models for countries in tectonically active and/or geologically complex settings. The study also provides a transferable methodological framework for developing similar products in other data-sparse regions.

Geomatics doi: 10.3390/geomatics6040075

Authors: Abdullah Kamel Yehia Miky Ahmed Al Shouny

Digital terrain models (DTMs) are essential elevation datasets that represent the morphology of the Earth’s surface and play a critical role in applications, such as urban planning, civil engineering, infrastructure design, and environmental assessment. However, the excessive cost remains the major challenge in obtaining accurate terrain models. Recent advancements in low-cost inertial navigation and motion-sensing technologies offer significant potential to enhance the cost-effectiveness of surveying projects. This study investigates the vertical accuracy and operational usability of a handheld inertial measurement unit (IMU) device (Moasure 2) for DTM generation in urban environments through the comparison with traditional total station and digital levels procedures. It also assesses the device compliance with The American Society for Photogrammetry and Remote Sensing (ASPRS) Positional Accuracy Standards. For this purpose, a comprehensive field survey was conducted in a small urban area characterized by varied terrain morphology. The vertical accuracy of the Moasure 2 was acceptable for many urban mapping applications based on a rigorous analysis of checkpoint data and error patterns, which were quantitatively assessed relative to reference surfaces. Profile-based validation showed that the elevation differences between similar terrain types were mainly within ±25 cm, with minimal bias and symmetric error distributions. The findings indicate that Moasure 2 can be a viable alternative tool for fast DTM generation in low-cost urban projects. It offers significant advantages in terms of portability, ease of use, and reduced fieldwork time compared to conventional methodologies. Furthermore, this study addresses the critical gap in the validation of the new IMU-based surveying technology and provides evidence for choosing appropriate equipment for urban terrain modeling.

Geomatics doi: 10.3390/geomatics6040074

Authors: Félix Díaz Nhell Cerna Rafael Liza Bryan Motta

Artificial intelligence is increasingly applied to earthquake-related seismo-ionospheric analysis with total electron content (TEC), but whether this literature is converging methodologically remains unresolved. We conducted a mapping review of 56 English-language journal articles retrieved from Scopus and Web of Science to characterize how artificial intelligence and computational intelligence methods are used with TEC in seismo-ionospheric and multi-precursor frameworks. The corpus shows recent growth in scientific production, strong concentration in a limited set of countries, institutions, and journals, and a stable conceptual backbone centered on earthquake, ionosphere, TEC, GPS-TEC, precursors, prediction-related terminology, anomaly detection, machine learning, and deep learning. However, full-text synthesis of the included studies shows that this thematic coherence coexists with substantial methodological divergence. We identified a transition from classical TEC anomaly detection toward AI-assisted decision systems, including models that forecast expected TEC behavior, flag candidate anomalies, classify precursor-like or disturbance-related states, and support monitoring-oriented outputs. We also identified a distinct operational strand focused on near-real-time detection of coseismic and tsunami-related ionospheric disturbances rather than deterministic earthquake prediction. Across these formulations, anomaly definitions, TEC representations, confounder control, baselines, uncertainty handling, and validation strategies remain pipeline-dependent, which limits cumulative comparability and physical interpretability across studies. These findings indicate that the field is thematically focused but not yet methodologically unified. Future progress will depend less on adding isolated case studies and more on clearer anomaly criteria, stronger control of solar and geomagnetic effects, explicit baselines, event-wise and region-wise validation, systematic false-alarm reporting, uncertainty-aware outputs, and transparent documentation of preprocessing and modeling decisions.

Geomatics doi: 10.3390/geomatics6040073

Authors: Bienvenue Christela Finounou Mizele Modeste Meliho Vinasetan Ratheil Houndji Semevo Arnaud R. M. Ahouandjinou Collins A. Orlando

Reference evapotranspiration (ET0) represents the atmospheric demand for water from a well-watered vegetated surface and is a key component of the hydrological cycle and agricultural water management. This study evaluated the performance of seven machine learning (ML) models: linear regression (LR), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Decision Trees (DT), and Cubist, for predicting monthly FAO-56 Penman–Monteith ET0 in Benin. The target variable was calculated from data collected at six synoptic stations over the 2017–2021 period. Ten remote-sensing and topographic predictors were used: MODIS Land Surface Temperature (LST), six Sentinel-2 optical vegetation indices (NDVI, EVI, NDMI, NDWI, MSI, NDRE), elevation, and cyclic month encoding. Models were trained on the 2017–2019 period and evaluated on an independent temporal test set (2020–2021). All models showed positive predictive performance, with the BMA ensemble achieving the highest accuracy (RMSE = 7.0% of mean ET0, R2 = 0.802), followed by Cubist (RMSE = 7.3%, R2 = 0.787) and DT (RMSE = 7.5%, R2 = 0.776). The seven models were combined via Bayesian Model Averaging (BMA) with posterior weights estimated by the EM algorithm to produce 1 km monthly ET0 maps for Benin for 2025. BMA-derived inter-model standard deviation provided spatially explicit uncertainty estimates, revealing that prediction uncertainty is greatest in the northern Sudanian zone during the dry season. The ET0 target variable was constructed as a hybrid product combining station temperature observations with solar radiation, wind speed, and vapor pressure deficit extracted from the TerraClimate gridded reanalysis dataset; this methodological choice is discussed as a study limitation.

Geomatics doi: 10.3390/geomatics6040072

Authors: Abdulmajid A. Alnoamani Tawfiq Hasanin

To track land use and land cover transformation in Makkah, techniques that allow steep relief, spectral confusion, and dense sacred–commercial mosaics, and can be justified in terms of planning, should be used. Satellite images are tedious and prone to uneven labeling on mixed-pixel boundaries, particularly in urban regions and Haram borders. Using multi-temporal Landsat-8 data (2013 and 2024), a hybrid deep learning architecture comprising U-Net, DenseNet201, and a Vision Transformer was trained. U-Net retained the geometry of the boundaries, DenseNet201 reinforced feature transfer across heterogeneous textures, and the transformer modeled long-range context. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to incorporate interpretability during spatial attention mapping, and Shapley Additive exPlanations (SHAP) during spectral topographic attribution, after which paired class-level statistical tests were performed. Modern residential increased from 15% to 20% (180 million to 240 million m2); roads from 5% to 10% (60 million to 120 million m2); industrial facilities from 3% to 5% (36 million to 60 million m2). The vegetation expanded by 1 to 5% (an addition of 48 million m2), and agriculture declined by 2 to 1% (a loss of 12 million m2). Its tension with urban development and preservation of productive land was growing. The proposed U-Net–DenseNet201–ViT hybrid system achieved over 98% overall accuracy on the test data for both study years, with kappa coefficients of 0.978 and 0.981 for 2013 and 2024, respectively. Grad-CAM identified attention focused on development fronts and transport corridors, whereas SHAP identified SWIR, thermal response, and slope as the main drivers. Significant class-level gains were statistically validated (p < 0.01), confirming an interpretable and auditable account of land transformation in Makkah.

Geomatics doi: 10.3390/geomatics6040071

Authors: Zoltán Farkas-Németh Zsolt Győző Török Dániel Balla

GPS-derived floating car data (FCD) provide spatially continuous urban traffic observations without fixed-sensor infrastructure. This study develops a spatiotemporal baseline framework jointly modelling day type and precipitation for 1189 junction-level nodes in Budapest. A six-phase pipeline—GPS preprocessing, coordinate reprojection, FME (Feature Manipulation Engine, Safe Software Inc., Surrey, BC, Canada)-based map-matching, junction-level aggregation, Voronoi meteorological allocation, and dataset assembly—was applied to 44.1 million 10 s records from approximately 1100 probe vehicles (November 2024–December 2025). Public holidays form a structurally distinct traffic flow pattern compared to Sundays (r = 0.71) and to regular workdays (r = 0.42); morning peak shifts to 09:00–11:00 and pooling holidays with Sundays introduces reference errors of 15–25%. Precipitation raises morning peak volumes by 6–17% across all zones while afternoon peaks remain statistically unchanged, consistent with commuter inertia; Saturday volumes fall by 7–15%. Rainy Wednesdays reach 109–112% of the Monday dry reference in inner zones, attributed to hybrid workers advancing their office day. Pairwise junction correlations show a non-monotonic distance-decay pattern, and time-lagged cross-correlation identifies 23 anticipative junction pairs with 60–90 min lead times. The results could potentially help decision making when developing city-wide infrastructure and tuning traffic signals so that traffic can be optimised and adapt to both real-time natural and social effects. The resulting baselines map onto DATEX II (Data Exchange standard, CEN EN 16157) ElaboratedDataPublication fields, supporting metadata publication on the Hungarian National Access Point under EU Regulation 2022/670/EU.

Geomatics doi: 10.3390/geomatics6040070

Authors: Tamás Tari Géza Király Gyula Sándor András Náhlik

The research analysed the space-use and habitat-preference characteristics of red deer (Cervus elaphus) in the Sopron Mountains, Hungary, utilising high-resolution Global Positioning System (GPS) telemetry data and two distinct land-cover databases. Hourly location data from 10 individuals were processed using the minimum convex polygon (MCP) and kernel home range (KHR) methods. Additionally, a relative stability index (RSI) was developed to describe seasonal shifts in area use. Significant sexual dimorphism was identified in the extent of annual home ranges: the mean space use of stags (3381 ha) significantly exceeded that of hinds (1391 ha). Geomatical analyses highlighted the seasonality of space use: the smallest extent was recorded in June, and shifts in home ranges within a single year were significant, while the winter period exhibited the least seasonal variation. Regarding habitat selection, significant seasonality was observed in hinds, reflecting temporal changes in resource availability, whereas this pattern was not observed in stags. The study concluded that the applied methods are appropriate for gathering baseline information; however, integrating high-precision databases is essential for accurate modelling of deer–forest interactions.

Geomatics doi: 10.3390/geomatics6030069

Authors: Aritz Urruela Àlex Sendrós Albert Casas Mahjoub Himi Luciano Galone Lluís Rivero

The growing need for sustainable wastewater treatment highlights the importance of low-energy solutions such as horizontal subsurface flow constructed wetlands (HSSF CWs). While effective, these systems often face clogging issues that reduce performance and lifespan. This study investigates clogging dynamics in a Water Treatment Plant (Lleida, Spain) using a multidisciplinary approach. Non-invasive geophysical methods such as Electrical Resistivity Tomography (ERT) and Induced Polarization (IP) were combined with high-resolution drone imagery to characterize surface and subsurface indicators of clogging. Drone data captured surface anomalies, while geophysical measurements revealed subsurface obstructions. The integrated analysis identifies clogged zones and shows a strong spatial correlation between surface features and geophysical anomalies. These results validate the use of drone imagery as a rapid, non-invasive diagnostic tool and demonstrate the effectiveness of combining remote sensing with geophysical techniques for wetland assessment. This approach supports improved monitoring, targeted maintenance, and optimized long-term performance of HSSF CWs.

Geomatics doi: 10.3390/geomatics6030068

Authors: Jiazheng Liu Xiaolin Ji Binfeng Yang Jiaojiao Guo Yukun Li Hanbing Wang

High-precision geomagnetic reference maps are essential for reliable geomagnetic field modeling and accurate geomagnetic matching navigation, especially in regions with sparse observations and complex magnetic anomaly variations. However, conventional map construction methods often exhibit limited precision and robustness, particularly when geomagnetic observations are sparse or spatial variations are complex. To address these challenges, this study proposes an improved artificial bee colony-optimized random forest model (IABC-RF) for reconstructing geomagnetic reference maps using magnetic anomaly data. The proposed method integrates an enhanced artificial bee colony strategy to optimize the hyperparameters of the random forest model, improving its predictive accuracy and stability in nonlinear geomagnetic environments. The experiments conducted on geomagnetic anomaly data from the South China Sea region, specifically between 5–25′ N and 100–120′ E, derived from the World Digital Magnetic Anomaly Map, show that the IABC-RF method outperforms traditional approaches. The IABC-RF method achieves the lowest root mean square error (RMSE) of 1.46 nT and the smallest standard deviation of 1.58 nT, while also maintaining a competitive computational time of 3.4 s. In comparison, Kriging interpolation produces an RMSE of 2.47 nT, inverse distance weighting (IDW) results in an RMSE of 14.45 nT, and improved Shepard interpolation gives an RMSE of 11.68 nT. The IABC-RF method excels at preserving global geomagnetic trends and accurately recovering localized anomaly details, offering enhanced robustness to outliers. Further evaluation of the IABC-RF method under noisy conditions (5% and 10% noise) revealed that although all methods experienced a decrease in performance due to the added noise, the IABC-RF method continued to show superior robustness. These findings demonstrate that the IABC-RF method provides a highly effective and reliable solution for constructing high-precision geomagnetic reference maps, with strong performance even in noisy environments. The method is particularly valuable for improving geomagnetic matching navigation in complex operational settings.

Geomatics doi: 10.3390/geomatics6030067

Authors: Adrian Traian Rădulescu Virgil Mihai Rădulescu Gheorghe M. T. Rădulescu Corina M. Rădulescu

Very tall slender structures are constructions that, due to their exceptional structural characteristics, are most exposed to environmental factors, especially wind and uneven sunlight. We refer specifically to smoke chimneys over 200 m and tall television towers, which, due to their truncated conical structure, exhibit behavior different from residential-type structures. Environmental stresses manifest as forces that can induce reversible tilts and oscillations—when their value significantly exceeds design values, they can cause damage or even destruction of the construction. Monitoring the preservation of structural integrity under the influence of environmental factors—a fundamental component of Structural Health Monitoring (SHM)—is essential for safety and maintenance. In the SHM of very tall slender structures, many studies employ various theories, methodologies, and technologies that have advanced rapidly due to the expansion of information technology. The objective of this study is to identify areas lacking research in the existing literature regarding environmental factors influencing the reversible displacement of very tall slender structures, along with the analysis of techniques and technologies used for monitoring these structures. To achieve this objective, the most critical environmental factors and technologies, especially sensor-based ones, were identified through a systematic search of the most popular databases. Subsequently, the study employs a bibliometric analysis, exploring challenges and prospective research areas reflected in the specialized literature. An extensive analysis of the State-of-the-Art on the subject in the specialized literature—particularly that published by the most prestigious journals in the field—was conducted. The findings indicate a lack of scientific investigations on environmental factors influencing SHM of very tall slender structures, especially studies on the effect of uneven sunlight on structures. The research provides a comprehensive understanding of SHM of very tall slender structures and has practical implications for developing effective monitoring methodologies.

Geomatics doi: 10.3390/geomatics6030066

Authors: Junpeng Fan Zao Zhang Anbang Dai Hongxi Yin Yasushi Ikeda

The accelerating deterioration of Chinese historical villages necessitates advanced digital approaches for systematic documentation and conservation. The present research proposes a novel Digital Heritage Framework that integrates UAV-based 3D oblique photogrammetry, LiDAR point cloud modeling, and computer vision. Unlike single-technology approaches, our methodology solves modeling issues for complex terrain mapping. This especially applies to the interior and roof works of buildings. The framework implements a customized Rhino-Grasshopper. The 3D model is able to resolve issues of shadow occlusion and spatial discontinuity by integrating aerial and ground-based datasets into spatially coherent formats. This makes use of the Meta-AI-SAM2 deep learning model for semantic segmentation and identification of materials. The computer vision (CV) approach gives semi-automated façade analysis. It enables documentation of complex architectural features non-invasively. We developed a Unity-based visualization platform. It features multiscale representations, ranging from village-scale layouts to centimeter-accurate scans of heritage structures such as the Qinchuan Ancestral Hall. Integration with the Unity platform optimizes dataset organization and hierarchical structuring. This significantly enhances database operational efficiency. This integration reduces manual processing complexity and hardware demands. Demonstrating documented efficiency and precision, this workflow presents a scalable solution for endangered heritage sites. Future research will explore AI-assisted detail reconstruction and cross-cultural adaptations. It potentially establishes this framework as a comprehensive tool for sustainable digital conservation.

Geomatics doi: 10.3390/geomatics6030065

Authors: Kyaw Win Eiji Kodani Shinya Tanaka Naoyuki Furuya Hideki Saito Masayoshi Takahashi Fumiaki Kitahara Takuya Hiroshima

Accurate digital canopy height models (DCHMs) derived from historical aerial photographs are essential for reconstructing long-term forest structural dynamics; however, the influence of photogrammetric software on DCHM quality and reliability remains insufficiently evaluated. This study compared the performance of two structure-from-motion (SfM) photogrammetric platforms, Metashape and Pix4Dmatic, for processing old aerial photographs and generating DCHMs in Ishikawa prefecture. Software performance was assessed using image processing efficiency, geometric accuracy based on root mean square error (RMSE), and correlation between derived DCHMs and National Forest Inventory (NFI) measurements. The results revealed that Metashape required shorter image processing times for the digital surface model generation and produced denser point clouds with broader spatial coverage. By contrast, Pix4Dmatic achieved higher geometric accuracy, with RMSE values of 0.571 m, 0.870 m, and 2.120 m in the X, Y, and Z directions, respectively. The Metashape-derived DCHM showed a higher mean value (15.267 ± 5.882 m) than Pix4Dmatic (14.749 ± 5.834 m), but Pix4Dmatic-generated DCHMs showed a closer relationship (r = 0.880) with NFI data (15.322 ± 5.451 m). These findings demonstrate that photogrammetric software selection substantially influences three-dimensional reconstruction from old aerial imagery and affects the reliability of DCHM generation. This study provides practical guidance for selecting SfM software for forest structural analysis and long-term forest monitoring.

Geomatics doi: 10.3390/geomatics6030064

Authors: Matthew P. Dube Brendan P. Hall Tyler Thibeau

The big data revolution transformed how we think of data analytics in many ways. Critical amongst them are the somewhat interconnected ideas of volunteered geographic information, crowdsourcing, and the big data property of variety. The robust literature concerning conceptual neighborhood graphs in two of these cases considers objects whose datatypes are held stable between the relations under consideration. This, however, is a limiting factor in these three application spaces due to the unknown form that data will take. This paper considers two avenues for the conceptual neighborhood graph to take as directions to address current complications facing reasoning tasks within a practically dirty world motivated by various sources of data: discretization conceptual neighborhood graphs (changing between corresponding vector and raster spaces) and cartographic generalization conceptual neighborhood graphs (changing the form of the objects in question). This paper provides insights as to what considerations should be considered when embarking upon this idea and demonstrates these concepts applied to prior conceptual neighborhood graphs.

Geomatics doi: 10.3390/geomatics6030063

Authors: Shahriar Mokhtari Antonio Zanutta Monia Negusini Matteo Cappuccio Giorgio Del Ciondolo Domitilla Forina Alessandro Capra Luca Vittuari

Precise Point Positioning with Ambiguity Resolution (PPP-AR) has emerged as a viable alternative to traditional network-based GNSS processing for crustal deformation monitoring and velocity field estimation. It provides high-precision daily coordinate solutions with simpler logistics, particularly for densifying velocity fields in regions lacking dense GNSS infrastructure. This study evaluates whether long-term velocity estimates derived from independent operational GNSS processing chains remain mutually consistent for regional geodynamic applications. We applied four processing strategies to 79 high-quality continuous GNSS stations in Southern Italy over the period 2017–2024: a Bernese double-difference network solution used as reference, Bernese PPP-AR, PRIDE PPP-AR, and the Nevada Geodetic Laboratory (NGL) PPP-AR solution derived from the GipsyX processing pipeline. The daily coordinate series preserve the realistic differences among the processing chains, while the subsequent velocity estimation was performed with a common HectorP workflow. A Bland–Altman screening identified 10 outlier stations, and the final inter-comparison is based on the remaining 69 stations (87.3% of the network). The results show that horizontal velocity components derived from PPP-AR agree with the network solution at sub-millimeter-per-year levels, with correlation coefficients exceeding 0.95, indicating strong coherence between the PPP-AR and network-derived horizontal velocity fields. In addition, vertical velocity estimates exhibit processing-strategy-dependent differences on the order of 1 mm yr−1 among PPP-AR solutions and relative to the network, indicating that careful interpretation is required for vertical rates. This study presents a systematic comparison of operational PPP-AR velocity solutions and a double-difference reference solution, demonstrating that complete processing-chain differences can introduce vertical effects comparable to those between PPP-AR and network processing. The findings support the practical maturity of PPP-AR for horizontal velocity field densification, while showing that vertical rates remain sensitive to processing strategy at the ∼1 mm yr−1 level.

Geomatics doi: 10.3390/geomatics6030062

Authors: Tedi Banković Marko Pavasović

Stable, homogeneous, and internationally comparable gravimetric reference systems are fundamental components of modern geodetic infrastructure, supporting height system realization, geoid modeling, geodynamics, and the integration of national gravity networks into global reference frames. This paper reviews the historical development of gravity reference systems, from early pendulum-based realizations to modern absolute gravimetry, with particular emphasis on their application in the Republic of Croatia. The evolution of international gravity datums is presented through the Vienna Gravity System, the Potsdam Gravity System, and the International Gravity Standardization Network 1971 (IGSN71), outlining their methodological foundations, accuracy levels, and limitations. The role of IGSN71 in harmonizing national gravity networks is discussed in the context of international cooperation. Within this framework, the development of gravimetric research in present-day Croatia is outlined, from surveys conducted during the Yugoslav period to the establishment of an independent national gravimetric datum. The realization of the Croatian gravimetric reference system through absolute gravity measurements between 1996 and 2000, the formation of the Zero-Order Gravimetric Network, and the establishment and densification of the First- and Second-Order Gravimetric Networks are described. The Croatian Gravimetric Reference System 2003 (HGRS03), based on IGSN71, is presented as the official national gravity reference. In addition to documenting its historical development, the paper provides a critical assessment of the current status of HGRS03, including limitations inherited from its historical reference framework, the absence of repeated absolute observations, and the uneven spatial distribution of Zero-Order stations. The paper also discusses future modernization perspectives, particularly in the context of advances in absolute gravimetry and the long-term maintenance of the Croatian gravimetric reference infrastructure.

Geomatics doi: 10.3390/geomatics6030061

Authors: Abdelillah Otmane Cherif Malika Abbes Rim Missaoui Anouar Hachmaoui Habib Mahi Nour El Houda Fethellah Nabil Beloufa Matteo Gentilucci Domenico Aringoli Gilberto Pambianchi Younes Hamed

Land-use and land cover (LULC) change is a key driver of environmental dynamics in semi-arid Mediterranean watersheds, strongly influencing hydrological processes, soil degradation, and ecosystem stability. In this context, understanding and predicting spatial–temporal land transformations is essential for sustainable watershed management. This study proposes a nonlinear spatial–temporal modeling framework integrating a hybrid Artificial Neural Network (ANN), Cellular Automata (CA), and Markov chain approach to simulate LULC dynamics in the Sebdou watershed, northwestern Algeria. Multi-temporal Landsat imagery (1985, 2005, and 2025), combined with topographic, socio-economic, and accessibility variables (slope, population density, distance to roads, and hydrographic network), was used to reconstruct historical land-use patterns and identify key driving forces of change. A supervised Maximum Likelihood classification achieved high accuracies, with overall accuracy ranging from 92.87% to 96.26% and Kappa coefficients between 0.85 and 0.91. The ANN model was trained to estimate nonlinear transition potentials, while the CA component incorporated spatial neighborhood effects to simulate land allocation processes. Markov chain analysis provided temporal transition probabilities, enabling the construction of a coupled ANN–CA–Markov framework for scenario-based prediction. Model validation against observed 2025 LULC maps indicated strong agreement in quantity distribution (Kappa histogram = 0.767), while spatial agreement (Kappa = 0.3566) reflected inherent spatial displacement typical of CA-based stochastic allocation. Simulation results for 2045 indicate continued urban expansion along major transport corridors, progressive decline of dense forest cover, and increasing bare soil areas, while agricultural land remains dominant but increasingly fragmented. These trends highlight the growing influence of anthropogenic pressure and accessibility factors on landscape restructuring in semi-arid environments. The proposed hybrid framework provides a robust decision-support tool for anticipating land-use dynamics and assessing future environmental pressures in Mediterranean drylands. Its integration with hydrological and erosion models can further support sustainable watershed planning under combined socio-economic and climatic changes.

Geomatics doi: 10.3390/geomatics6030060

Authors: Yuefan He Yanxin Wang Xiaoning Su Yuzhao Li Shuguang Wu Guigen Nie

Environmental loading affects periodic variation in the Global Navigation Satellite System (GNSS) vertical coordinate time series. This study extracted periodic signals from the global GNSS vertical coordinate time series using Singular Spectrum Analysis (SSA) and parameterization methods. Then, the accuracy of the GNSS time-varying periodic signal obtained by the SSA method compared to the GNSS periodic signal fitted by the parameterization method was statistically analyzed. The results show that the stations with a positive RMS reduction ratio account for 97.46% of the total 630 stations worldwide. Subsequently, this article conducted a comparative study on the correlation between time-varying periodic signals obtained by the SSA method, periodic signals fitted by the parameterization method, and the GNSS original coordinate time series with environmental loading displacement. The results indicate that the correlation between the time-varying periodic signal obtained by the SSA method and the environmental loading is highly consistent with the correlation between the original GNSS coordinate time series and the environmental loading. The time-varying periodic sequence obtained using the SSA method is used to analyze the impact of environmental loading corrections (ELCs) on the global GNSS vertical coordinate time-series periodic signal. Research has shown that 79.52% of global stations have reduced time-varying periodic signals and the nonlinear amplitude of the GNSS coordinate time series is weakened after ELCs.

Geomatics doi: 10.3390/geomatics6030059

Authors: Juan Castrillo Mario Soilán Natalia Caparrini Jesús Balado

Vulnerable Road Users (VRUs) are involved in a significant proportion of traffic fatalities, and they are highly exposed to severe injuries in urban traffic environments. For detecting and tracking VRUs, LiDAR technology offers precise 3D perception capabilities, overcoming challenges posed by their small size, dynamic behavior, and frequent presence in occluded or congested areas. This work aims to conduct a scoping review of LiDAR-based solutions for preventing and reducing accidents involving VRUs, synthesizing current methodologies, evaluating detection and tracking approaches, and identifying strategies to improve urban safety through data-driven interventions. An analysis of 49 publications indicates that effective monitoring of VRUs depends on a strategic balance between technological performance and practical limitations, such as system costs, calibration complexity, and hardware constraints. Privacy-preserving techniques, such as anonymization and LiDAR-based sensing, are essential to enable ethically responsible large-scale data collection.

Geomatics doi: 10.3390/geomatics6030058

Authors: Xin Meng Ruijie Xi Bin Xiao Jinsong Gao Aijun Li Xintao Yang Kui Gao Nianlong Han Xianyong Dong Mengdi Yao

In densely built urban environments, GNSS signals frequently undergo diffraction at building edges, and the resulting errors can severely degrade positioning accuracy and reliability. Previous studies have shown a strong correlation between diffraction error and the carrier-power-to-noise-density ratio (C/N0). Building on this observation, this study proposes a GNSS diffraction-mitigation method based on segmented down-weighting and exclusion of affected observations. First, an open-sky reference model of the elevation–C/N0 relationship is established for each satellite class. A robust strategy is then introduced to adaptively down-weight moderately contaminated observations and remove severely affected ones during stochastic modeling. The proposed method is evaluated using both static and kinematic datasets collected in dense urban environments. In the static experiment under severe building obstruction, the ambiguity-fixing rate (AFR) reaches 95.5%, with horizontal and vertical accuracies of 4 mm and 8 mm, respectively, substantially outperforming conventional weighting strategies. In the vehicle-based kinematic experiment, the fixed-solution rate exceeds 80%, and the float solution is also noticeably improved relative to traditional weighting and exclusion methods. Overall, the proposed method effectively mitigates diffraction-induced errors and improves positioning performance in dense urban environments, with potential applications in automated inspection, intelligent construction, and high-precision deformation monitoring.

Geomatics doi: 10.3390/geomatics6030057

Authors: Kirim Lee Wonhee Lee

Accurate registration of UAV-derived thermal infrared orthophotos and RGB orthophotos is essential for multi-sensor geospatial analysis, but it remains challenging because thermal imagery generally has lower spatial resolution, weaker texture, and less distinct structural information than RGB imagery. This study comparatively evaluated five histogram equalization methods—histogram equalization (HE), contrast-limited adaptive histogram equalization (CLAHE), brightness-preserving bi-histogram equalization (BBHE), dualistic sub-image histogram equalization (DSIHE), and minimum mean brightness error bi-histogram equalization (MMBEBHE)—for improving AKAZE-based registration of land surface temperature (LST) orthophotos to reference RGB orthophotos. High-accuracy RGB orthophotos generated using GNSS-surveyed ground control points were used as the geometric reference. Thermal data were acquired twice at each of two study sites with contrasting surface characteristics and processed into LST orthophotos. Each histogram equalization method was applied to the LST orthophotos, after which keypoints and descriptors were extracted using AKAZE, tentative correspondences were established, outliers were removed using RANSAC, and an affine transformation was estimated from the inlier correspondences. Here, an inlier denotes a tentative match that remained geometrically consistent after RANSAC-based outlier rejection. The estimated transformation was then applied to the source LST raster to preserve radiometric values in the final corrected product. Performance was assessed using the number of detected keypoints, tentative matches, RANSAC-verified inliers, matching efficiency, reproducibility, and exploratory statistical analysis. Among the five methods, BBHE consistently produced the highest number of inliers and the best matching efficiency at both study sites, while also showing the lowest variability between repeated acquisitions. These results indicate that brightness-preserving histogram equalization is particularly effective for thermal–RGB orthophoto registration and can improve the reliability of UAV-derived thermal mapping products for geomatics applications.

Geomatics doi: 10.3390/geomatics6030056

Authors: Patrik Peťovský Ondrej Tokarčík Branislav Topitzer Peter Blišťan Ľudovít Kovanič Jana Lopatníková

Terrestrial laser scanning (TLS) represents an efficient method for acquiring spatial data in urban environments, while the quality of resulting geometric outputs is significantly influenced by subsequent point cloud processing. This article focuses on analyzing the accuracy of automatic building footprint extraction from classified TLS point clouds, with an emphasis on the role of data cleaning methods. The study area is located in the city center of Žiar nad Hronom, where urban structures were monitored using TLS. For detailed analysis, three objects were selected—an apartment building, a garage, and an industrial building—representing different levels of geometric complexity. To simulate realistic processing conditions, classification results obtained from different software (Leica Cyclone 3DR, Trimble RealWorks, and LiDAR360) were used. Their quality was evaluated using standard metrics such as Precision, Recall, and F1-score. These classifications also served as input scenarios containing typical errors, such as point clusters, vegetation near buildings, or misclassified terrain elements. Subsequently, selected point cloud cleaning methods were applied to these datasets, specifically statistical outlier removal, noise filter, and label connected components. The accuracy of the extracted building footprints was evaluated by comparison with reference data obtained from geodetic measurements. The results show that automatic classification alone is not sufficient to achieve accurate building footprints, and that data cleaning plays a decisive role. For example, in the case of the apartment building, statistical filtering reduced the area from 1052 m2 to approximately 854 m2 (reference value: 706 m2) and significantly improved positional accuracy (centroid shift reduced from 0.455 m to 0.077 m). Similarly, for the industrial building, the area was reduced from 215 m2 to approximately 165 m2 (reference: 148 m2) while maintaining the correct number of corner points. In contrast, noise filter method proved to be less reliable, as removing up to 25–30% of points often did not lead to improvements in footprint geometry. The results highlight the importance of systematic point cloud cleaning as a key step in automated building footprint extraction and demonstrate that a properly selected combination of methods can significantly improve accuracy even in noisy datasets. The article also provides practical guidance for efficient TLS data processing in geoinformatics applications.

Geomatics doi: 10.3390/geomatics6030055

Authors: Nataliya Stankova Daniela Avetisyan

Forest fires are an increasing environmental challenge in Southern Europe, requiring reliable tools for assessing both fire-induced disturbances and subsequent ecosystem recovery. This study presents an integrated satellite-based system for automated monitoring of post-fire forest dynamics. The system combines multispectral data from Sentinel-2 and Landsat (TM, ETM+, OLI, OLI-2) with thermal anomaly information from MODIS and VIIRS within a unified processing framework. It is structured into two modules: Post-Fire Disturbance (PFDMO) and Post-Fire Recovery (PFRMO). The methodology builds on a validated algorithm integrating the Disturbance Index (DI), Vector of Instantaneous Condition (VIC), and Direction Angle (DA), enabling automated multi-temporal analysis from fire detection to recovery assessment. The system was applied to three wildfire-affected areas in Bulgaria under different environmental conditions. Results reveal substantial spatial variability in disturbance and recovery, with PFDMO values ranging from −5.17 to +10.16 and PFRMO values from −2.25 to +7.40. The results demonstrate the applicability of the proposed system for monitoring post-fire forest dynamics and illustrate its potential to support informed decision-making in forest management, biodiversity conservation, and sustainable resource use. The main contribution of the system lies in the integration of disturbance and recovery assessment within a single automated and scalable workflow based on freely available satellite data.

Geomatics doi: 10.3390/geomatics6030054

Authors: Sharifa Karwandyar Thomas J. Pingel Alex Nikulin

The detection and classification of scatterable landmines present a significant challenge for humanitarian demining, particularly in resource-constrained regions. This paper evaluates the use of a deep learning-based strategy using RGB imagery and the YOLOv11 algorithm to detect the most commonly deployed PFM-1 landmines, with the overarching goal of applying this approach to the broad category of scatterable landmines. RGB image-based YOLOv11 detection showed strong precision (78–91%) and recall (76–88%) against validation data for several model variants. Additionally, 3D-printed, paint-matched replicas of PFM-1 landmines were used provisionally as part of out-of-sample (OOS) testing to assess the realistic value of this methodology in the field, along with an inert PFM-1 mine. This demonstrated the potential for 3D-printed replicas to be used as part of the training and assessment process due to their low-cost, scalable, and safe approach, highlighting strong precision (74–80%) but weaker recall (14–24%). Additional edge deployment was tested using the model to demonstrate its capability in locating a minefield using trigonometric relationships and kernel density relationships, further supporting this method in non-technical, first-pass landmine sweeps. These results demonstrate that OOS evaluation is critical in humanitarian demining research to ensure that detection systems are truly field-ready and operationally reliable. This study provides a replicable workflow for deep learning tasks related to surface-laid landmines that can be deployed on edge devices for use in non-technical surveys.

Geomatics doi: 10.3390/geomatics6030053

Authors: Ľubomír Kseňak Katarína Pukanská Karol Bartoš

Synthetic aperture radar (SAR) data from Sentinel-1 enable land cover classification independent of cloud cover and illumination; however, classification performance is affected by inherent speckle noise. This study evaluates the influence of eight speckle filtering algorithms on classification accuracy using Sentinel-1 Ground Range Detected (GRD) data across five contrasting terrain types in eastern Slovakia (mountain, forest, urban, cropland, and water). Speckle suppression was assessed using Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Equivalent Number of Looks (ENL). Classification performance was quantified using Support Vector Machine (SVM), Random Forest (RF), and Histogram-based Gradient Boosting (HistGB) under VV, VH, and dual-polarization (VV + VH) configurations with repeated balanced sampling. Classification accuracy varies across terrain types. In croplands, Lee Sigma combined with SVM in VV + VH mode achieved Overall Accuracy (OA) = 0.746 ± 0.010, whereas in mountainous areas, OA = 0.838 ± 0.005 was achieved with Intensity-Driven Adaptive Neighborhood (IDAN) filtering. Urban areas achieved OA = 0.890 ± 0.006, whereas forest classification remained limited (best OA = 0.582 ± 0.011). Water surfaces approached saturation accuracy (OA ≈ 0.9998). Dual polarization improved performance in heterogeneous environments but had a limited effect in homogeneous classes. The results show that terrain structure influences the interaction between speckle filtering and classification performance.

Geomatics doi: 10.3390/geomatics6030052

Authors: Dania Isaura Pasillas-Pasillas Juvenal Villanueva-Maldonado Carlos Bautista-Capetillo José Ricardo Gómez Rodríguez Erick Dante Mattos-Villarroel Cruz Octavio Robles Rovelo

Technological advancements have revolutionized photogrammetry, with the implementation of unmanned aerial vehicles for capturing images from different angles and the ease of obtaining sensor position information at the time of capture. This study evaluates the accuracy of direct georeferencing via Networked Transport of Radio Technical Commission for Maritime Services Via Internet Protocol, in the orthomosaic as a photogrammetric product in a large urban area with steep and highly variable topography, comparing it with the coordinates of nine checkpoints obtained with GNSS equipment connected to the National Active Geodetic Network, managed by the National Institute of Statistics and Geography of Mexico. An orthomosaic of the historic center of Zacatecas was obtained with a resolution of 2.70 cm/pixel. The orthomosaic coordinates, compared to those of the GNSS equipment, show a root mean square error (RMSE) of 0.78 m in the horizontal coordinates and an RMSE of 1.22 m in the vertical coordinates. Previous studies prove the efficiency of the Continuously Operating Reference Station module and network with other aircraft; this study determines that this is true for large areas with high coverage and quality in the internet network, but with rugged topography, the results are not accurate.

Geomatics doi: 10.3390/geomatics6030051

Authors: Siyuan Sun Lin Su Xukun Yang Chunyu Qi Xinyu Liu Licheng Pan

Large-scale engineering of the 3D scenes used in BIM, GIS, digital twins, and geospatial web delivery frequently suffer from significant geometric redundancy after export to mesh-based delivery formats, arising in part from the inconsistent reuse of geometry, where many repetitive components are stored as independent meshes rather than being fully instantiated. This paper proposes an affine–covariant mesh instancing framework designed to achieve a lightweight representation of watertight triangular solids. The core of the method lies in a canonicalization pipeline: each mesh is normalized via volume-centroid translation, principal-axis alignment derived from volume covariance, and anisotropic covariance whitening. This process effectively decouples the influence of translation, rotation, and non-uniform scaling, projecting diverse geometries into a unified canonical space. Within this space, geometric similarity is quantified by evaluating compact descriptors against user-defined tolerances. A greedy clustering strategy is then employed to group affine–similar models based on these descriptors. Finally, the scene is efficiently reconstructed by applying inverse affine transformations to the representative instance of each cluster. The output stores one shared geometry per cluster alongside per-instance 4×4 transform matrices, preserving the original spatial layout while reducing redundant geometry storage. Experiments on four real-world engineering scenes demonstrate varying compression benefits. The results prove particularly effective for scenes containing unlinked repetitive parts and affine–similar parametric components, while also revealing a controllable trade-off between fidelity and compression rate. The method is therefore suitable as a post-export geometry-lightweighting step in mesh-based BIM/GIS integration, infrastructure digital twins, and large-scale 3D mapping workflows.

Geomatics doi: 10.3390/geomatics6030050

Authors: Laura Marconi Matteo Cutugno Raffaella Brigante Giovanni Pugliano Fabio Radicioni Umberto Robustelli Aurelio Stoppini

This study provides a comparative performance evaluation of commercial Precise Point Positioning Real-Time Kinematic (PPP-RTK) and public Network RTK (NRTK) services for vehicle-based positioning in urban and suburban environments. Using low-cost u-blox ZED-F9 receivers, the research assesses the accuracy, availability, and robustness of the u-blox PointPerfect service against a regional NRTK network across diverse real-world scenarios, including high-speed highway conditions and signal-challenging urban corridors. The experimental framework utilizes a rigid-bar setup for high-precision ground-truth validation and incorporates an independent vertical accuracy assessment against a LiDAR-derived digital elevation model (DEM). The results demonstrate that all tested configurations achieve decimeter-level accuracy. Notably, the integration of PPP-RTK with an inertial measurement unit (IMU) delivers performance nearly equivalent to NRTK, effectively mitigating vertical biases and ensuring positioning continuity in GNSS-denied areas such as tunnels. These results confirm that low-cost GNSS solutions, when paired with modern augmentation services and IMU integration, can meet the stringent demands of mass-market applications like Cooperative Intelligent Transport Systems (C-ITS) and autonomous mobility.

Geomatics doi: 10.3390/geomatics6030049

Authors: Luma K. Jasim Athraa Hashim Mohammed Hussein Alwan Mahdi Bashar Alsadik

This paper presents a deterministic system that combines textual semantic data from panoramic images with LiDAR point clouds in a mobile mapping setup. Urban scenes often include textual elements, such as signs and business names, that provide key details typically missing from LiDAR-based urban digital twins. The presented method uses deep learning-based OCR to extract text from street panoramas and then categorizes it into urban types using a rule-based classifier. Text regions are geometrically projected into the LiDAR environment by converting image coordinates into viewing rays that intersect LiDAR surfaces, such as facades. Data from multiple panoramas are merged with confidence-weighted spatial clustering to produce consistent semantic markers for urban features. Extracted business names enable text-based searches of the LiDAR point cloud, allowing facility location by category, keyword, or brand. Tests on datasets from European and U.S. cities support plausible facade-level localization and demonstrate the framework’s ability to enhance LiDAR point clouds with searchable semantic information. The main contribution is not a new standalone OCR or LiDAR-processing algorithm, but a deterministic multimodal integration framework that combines deep-learning OCR, geometric back-projection, and cross-view spatial fusion to convert street-level textual cues into reliable, queryable 3D semantic markers within mobile-mapping LiDAR data.

Geomatics doi: 10.3390/geomatics6030048

Authors: Csongor Gedeon Tünde Takáts János Mészáros Ferdinand Bego Ben Swallow Tamás Tóth Ákos Ekrik Adrián Berta László Pásztor Vilmos Steinmann

We present a concise methodology to model and visualise mole-rat burrows by integrating 3D ground-penetrating radar (GPR) volumes, high-resolution 3D surface texture, and interpretative 3D visualisation with open-code software, such as Blender and Houdini. The workflow shows the processing and conversion steps for converting surface and subsurface raw datasets into point clouds, then the amalgamation of those 3D objects into a voxelised volume. The voxelisation script creates a text file, a *.CSV file, that masks the voxels with the values of 0 and 1 depending on whether they are inside or outside a burrow. This parametrisation resulted in a total of 7,730,587 voxels generated, of which 48,952 have a value of 1 within them. This indicates the presence of one burrow system, in which there were about 60–80 burrow segments that were initially identified by GPR but remained rather interpretative than a verified geometry. The entire process enables handling and combining different, complex, 3D datasets into a simple text file and thus enables merging with covariates for further spatial modelling of burrow systems from incomplete, indirect, noisy measurements.

Geomatics doi: 10.3390/geomatics6030047

Authors: José Antonio Gámez García Giacomo Lazzeri Deodato Tapete

HyperCoreg is an automated, end-to-end pipeline for geometric co-registration of spaceborne hyperspectral imagery (PRISMA L2D and EnMAP L2A) to Sentinel-2 Level-2A reference data. The workflow addresses scene-dependent geolocation errors that hinder reliable data fusion and multi-temporal analyses, particularly in cloud-affected acquisitions. HyperCoreg builds on the AROSICS framework without replacing its image-matching engine and extends it at the workflow level through four operational functions: automated Sentinel-2 candidate selection, hyperspectral-to-multispectral band pairing, sequential alignment logic, and quality-controlled acceptance. The main output is a co-registered hyperspectral cube along with comprehensive metrics, per-scene reports, and optional diagnostic products that support accessible quality control. Performance is evaluated on a long time series of PRISMA images collected from 2019 to 2025 and an EnMAP test set acquired in 2025, over the Metropolitan City of Rome (Italy). The multi-sensor dataset encompasses heterogeneous acquisition conditions, including variable cloud cover, illumination, and seasonal variability. The results show systematic reductions in mean residual error compared with a controlled basic AROSICS-based pipeline configuration. The largest gains are achieved in challenging conditions where tie points are sparse or unevenly distributed. By improving geometric consistency, this pipeline facilitates spatial layering and integration of hyperspectral data with higher-resolution urban layers and supports a range of downstream applications where data integration and spatiotemporal consistency are cornerstones of further analysis.

Geomatics doi: 10.3390/geomatics6030045

Authors: Karen Escalona Johnny Valencia-Calvo Gerard Olivar-Tost Valentín Alexis Solís Olave

Accurate Land Use and Land Cover (LULC) mapping in high-latitude mountain regions faces critical challenges from persistent cloud cover and complex topography, which limit the utility of passive optical sensors. To address the absence of evidence-based guidelines for these data-scarce environments, this study employs a systematic ablation design to quantify the marginal and synergistic contributions of optical data (Sentinel-2), Synthetic Aperture Radar (Sentinel-1 SAR), topography, and intra-seasonal phenological metrics within the Aysén River basin, Chilean Patagonia, developing a geospatial workflow with high transferability potential. Using a Random Forest classifier, five progressive configurations were compared: a seasonal optical baseline (A), and configurations incorporating intra-seasonal percentiles (A + P), topography (A + T), SAR (A + R), and their full integration (A + P + T + R). The baseline model achieved an Overall Accuracy (OA) of 89.2% and a Macro-F1 of 80.5%; the fully integrated model reached OA = 92.5% and Macro-F1 = 86.0%. Macro-F1 was adopted as the primary metric because it assigns equal weight to all 11 classes regardless of spatial prevalence, capturing gains in minority but ecologically critical classes that OA would mask. SAR and topographic variables were the largest contributors, generating non-redundant improvements in structurally complex and relief-conditioned classes, respectively. Furthermore, annual SAR composites demonstrated superior cartographic spatial consistency over seasonal aggregations, which introduced purely cartographic geometric artifacts at class ecotones despite achieving marginally higher point-based statistical metrics, a divergence explained by the spatial blindness of confusion-matrix validation to boundary-zone classification errors.

Geomatics doi: 10.3390/geomatics6030046

Authors: Masachika Akage Daisuke Yoshida Wakana Fujimoto

UAV-based crack inspection of port quay walls is promising for efficient infrastructure maintenance, but its practical deployment remains hindered by frequent false positives caused by debris, stains, and irregular surface textures. This study proposes a false-positive reduction framework for a crack inspection system based on aerial images acquired by a small general-purpose UAV. The proposed method introduces anomaly detection after object detection so that detected crack candidate regions are re-evaluated based on their deviation from the learned feature distribution of crack images. A Vision Transformer (ViT)-based anomaly detection model is employed, and both standard-threshold and low-threshold object detection settings are investigated. Experimental validation across five verification areas showed that the combination of standard-threshold object detection and anomaly detection consistently improved F1 and F2 scores over the conventional baseline, demonstrating stable suppression of false positives while maintaining crack detectability. Under the low-threshold setting, Frangi filter-based preprocessing was more effective than grayscale-based preprocessing, achieving a favorable balance between broader crack extraction and false-positive suppression in some 5 m cases. However, this advantage decreased as image resolution deteriorated. Overall, the results indicate that the most robust configuration in the current framework is the combination of standard-threshold object detection and anomaly-based false-positive suppression. In contrast, the benefit of low-threshold operation depends strongly on image resolution. The findings also suggest that practical deployment requires calibration of the anomaly detection threshold based on site conditions and GSD.

Geomatics doi: 10.3390/geomatics6030044

Authors: Simiso Siphenini Ntuli Mayshree Singh

Cross-domain semantic classification of 3D point clouds remains challenging due to strong domain shifts between heterogeneous sensing modalities. Most existing classification frameworks are domain-specific, limiting their use in integrated land–water mapping applications. This study evaluates the transferability of classical geometric machine learning classifiers between terrestrial and underwater point cloud domains without target-domain retraining. Experiments were conducted using terrestrial data acquired with a Leica BLK360 terrestrial laser scanner (TLS) and underwater point clouds collected with a Blueview BV5000 mechanical scanning sonar (MSS). Two dimensionality-based frameworks, CANUPO–Support Vector Machine (SVM) and 3DMASC–Random Forest (RF), were implemented in CloudCompare and assessed under intra-domain and cross-domain configurations. Strong intra-domain performance was achieved, with terrestrial–terrestrial accuracies of 0.99 for CANUPO–SVM and 0.97 for 3DMASC. In underwater evaluation, CANUPO maintained high accuracy (0.97), whereas 3DMASC decreased to 0.86 due to increased variability in the submerged data. Under cross-domain transfer, CANUPO achieved 0.93 accuracy for terrestrial-to-underwater and 0.89 for underwater-to-terrestrial classification, while 3DMASC demonstrated stable generalisation with 0.95 accuracy in both directions. Overall, dimensionality-based geometric descriptors capture stable structural cues across sensing environments, providing an interpretable and efficient pathway for applications such as hydrographic surveying, coastal monitoring, and underwater search-and-rescue detection. Future work will extend validation to larger datasets and explore domain adaptation strategies to further reduce cross-modality domain shift.

Geomatics doi: 10.3390/geomatics6030043

Authors: Hacer Kubra Sevinc Ismail Rakip Karas

Airborne LiDAR (Light Detection and Ranging) data is widely used in urban modelling and three-dimensional spatial analysis studies. However, the irregular structure of LiDAR point clouds, varying point densities, and class imbalances observed in the datasets make semantic segmentation problematic. This study addresses the four-class semantic segmentation problem (unclassified, vegetation, ground, and building) on aerial LiDAR point clouds, with a particular focus on multi-class segmentation. The Oregon LiDAR Program dataset was obtained through the OpenTopography platform for use in this study. The point cloud data were resampled to 4096 points to ensure a fixed input size; for each point, the X, Y, and Z coordinates, along with the RGB and intensity features, were utilized. Experimental studies compared the proposed method with both baseline models (PointNet, PointNet++ MSG, and VoxelNet Lite) and recent state-of-the-art architectures, including Point Transformer, KPConv, and RandLA-Net. Additionally, the PointNet2 MSG Transformer model was developed based on the PointNet++ MSG architecture and includes a transformer-based feature fusion module. Different loss functions and training configurations were evaluated, and the effects of ensemble learning and test-time augmentation strategies on model performance were analyzed. The experimental results show that the proposed approach achieved a mean Intersection over Union (IoU) of 51.74% and an accuracy of 61.50% on the test dataset. These results demonstrate that combining multi-scale feature extraction with transformer-based feature fusion is an effective approach for semantic segmentation of LiDAR point clouds and multi-class segmentation tasks.

Geomatics doi: 10.3390/geomatics6030042

Authors: Zainab N. Jasim Nagham Amer Abdulateef Zahraa Ezzulddin Hussein Bashar Alsadik

Ground Control Points (GCPs) are essential for accurate georeferencing of optical imagery; however, their selection is often heuristic and affected by temporal changes in image geometry. This challenge is particularly acute for Google Earth imagery, where acquisition conditions and mosaicking processes vary over time. This paper presents a multi-epoch robust framework for the automatic design of GCP networks to precisely georeference multi-temporal Google Earth images. GCP selection is formulated within an affine optimal experimental design setting, in which candidate configurations are evaluated against the most challenging acquisition epoch to promote consistency over time. A hybrid DI-optimality criterion balances transformation stability and interior prediction accuracy without requiring interior control points. The framework also includes an automated method for determining the optimal number of GCPs using marginal-gain stopping and cost-regularized μ-sweep analysis. Experiments on two urban case studies show that compact, well-conditioned GCP networks can match the accuracy of larger heuristic networks and achieve top 10% root-mean-square error (RMSE) performance on a random feasible subset benchmark. Results demonstrate that a carefully designed GCP network can greatly reduce the number of control points needed while maintaining stable geometric performance across acquisition sessions.

Geomatics doi: 10.3390/geomatics6030041

Authors: Giuseppe Bonifazi Alice Aurigemma José Salas-Cáceres Javier Lorenzo-Navarro Silvia Serranti Federica Paglietti Sergio Bellagamba Sergio Malinconico

The detection and monitoring of asbestos–cement roofing remain a critical public health and environmental challenge, especially in urban and suburban areas where asbestos-containing materials are still widespread due to their extensive use in the 20th century. Although hyperspectral and high-resolution multispectral remote sensing have proven effective for mapping asbestos–cement roofs, many existing approaches rely on proprietary software, limiting transparency, reproducibility, and large-scale adoption. This study presents a fully reproducible, cost-free Python-based workflow for the detection and temporal monitoring of asbestos–cement roofing using high-resolution multispectral WorldView-3 imagery. The workflow integrates atmospheric correction (using the Py6S radiative transfer model), spatial preprocessing, supervised pixel-based classification, postprocessing, and building-level aggregation within an open framework. A Maximum Likelihood Classifier is applied to VNIR and SWIR data using empirically defined roof typologies to enhance class separability. Pixel-level results are aggregated to the building scale through adaptive thresholding enabling the translation of spectral classifications into meaningful building-level information. Tested over the city of Mantua (Italy), the approach achieved reliable classification performance and enabled multi-temporal comparison to identify changes potentially due to roof remediation. Evaluation metrics (precision, recall, and F1-score) highlight the importance of carefully choosing the building-level threshold. By relying exclusively on open-source tools, the workflow enhances transparency, reproducibility, and scalability for long-term monitoring.

Geomatics doi: 10.3390/geomatics6030040

Authors: Antonino Maltese Claudia Pipitone Gino Dardanelli

This study evaluates the robustness and time consistency of GNSS coordinate solutions obtained from a suite of scientific and legacy commercial software packages, with the aim of assessing their suitability for rapid preliminary framing of institutional geodetic networks. The analysis includes Pinnacle 1.0, Topcon Tools v.8, TGOffice 1.63, Leica Geo Office Combined 7.0, NDA Lite, and the scientific-grade NDA Professional, together with PPP solutions generated through the CSRS service. A one-year dataset from the UNIPA GNSS CORS network was processed to derive monthly coordinate estimates, which were compared in terms of geocentric (ΔXYZ), horizontal (ΔEN), and vertical (ΔUp) deviations, as well as temporal behavior and statistical significance (Welch’s t-test). The results show that NDA Professional provides the most stable and time-consistent solutions, with mean horizontal and vertical dispersions typically below 2–3 mm. Topcon Tools and Pinnacle also exhibit good performance, with average ΔEN values of approximately 3–4 mm and ΔH values generally within 5–7 mm. In contrast, Leica LGO and NDA Lite display larger variability, particularly in the vertical component, where monthly deviations may exceed 10 mm. The CSRS solution, due to its PPP-based intrinsic nature, reveals a statistically significant temporal trend (on the order of 5–8 mm/year), which prevents direct comparison with static network solutions; however, once detrended, its dispersion becomes comparable to the best-performing static software, with ΔEN and ΔUp values of 2–4 mm.

Geomatics doi: 10.3390/geomatics6020039

Authors: Jose Eduardo Fuentes Delgado

Satellite-derived bathymetry (SDB) offers a practical alternative for mapping shallow reefs in remote oceanic settings where acoustic surveys are costly and logistically constrained. Here we benchmark PlanetScope 8-band (3 m) surface reflectance—an underused commercial constellation for reef SDB—using ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) ATL03 photon data (Release 006) as independent vertical control. Seventeen ATL03 ground tracks (2019–2025) were processed using geometric filtering, photon classification, and explicit air–water refraction correction. This yielded 5171 candidate seafloor observations, of which 5021 were co-located with valid PlanetScope water pixels after Usable Data Mask screening (UDM2/UDM2.1), sun-glint correction, and reflectance quality screening. Four SDB formulations (Lyzenga, Bierwirth, and Stumpf) were calibrated and independently validated using depth-stratified train/validation partitions (70/30, 80/20, and 90/10). Across partitions, the multiband polynomial model of Lyzenga 2006 generalized best (R2 = 0.843–0.859; RMSE = 1.734–1.813 m; bias = −0.070 to −0.081 m), followed by Bierwirth (R2 = 0.826–0.845; RMSE = 1.818–1.904 m). Lyzenga 1985 reported lower skill (RMSE ≈ 3.1 m), while the Stumpf log-ratio failed in independent validation. ICESat-2 photon bathymetry provides repeatable point-based control in clear waters but remains less precise than echo sounding due to photon classification and spatial-support effects; therefore, uncertainties and applicability limits must be reported. Overall, PlanetScope 3 m, 8-band surface reflectance supports reproducible reef-scale SDB in Seaflower under the evaluated conditions, with Lyzenga 2006 as a robust baseline.

Geomatics doi: 10.3390/geomatics6020038

Authors: Mohamed Elhag Esubalew Adem Aris Psilovikos Wei Tian Jarbou Bahrawi Ahmad Samman Roman Shults Anis Chaabani Dinara Talgarbayeva

This study applies Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to investigate surface deformation dynamics in the hyper-arid Eastern Province of Saudi Arabia, with emphasis on quantifying sand dune migration and identifying areas susceptible to wind erosion. Utilizing Sentinel-1 SAR data and the MintPy toolbox, ground deformation was quantified with millimeter-scale precision. Results reveal significant subsidence, up to 15 cm/year in landfills, linked to waste compaction and groundwater depletion. Localized uplift of ~4 cm/year on northern peripheries is directly attributed to aeolian sand accumulation from seasonal Shamal winds, providing quantitative evidence of dune migration. While direct measurement of wind erosion (net deflation) remains challenging due to the dominance of depositional signals and the spatial heterogeneity of erosion processes, areas of potential erosion are inferred from negative displacement patterns outside landfill zones and from coherence characteristics indicative of surface instability. The integration of SBAS-InSAR with GPS and ERA5 wind reanalysis resolves the combined influence of aeolian deposition, hydrogeological changes, and anthropogenic activity, offering insights into both components of aeolian dynamics and a replicable model for sustainable land management in arid environments.

Geomatics doi: 10.3390/geomatics6020037

Authors: Rubén Nocelo López

Atmospheric refractivity governs the propagation behavior of electromagnetic waves in the lower troposphere. Accurate spatial characterization of this parameter is essential for optimizing communication, radar, and navigation systems. This study presents a geostatistical framework for generating high-resolution refractivity maps using Universal Kriging (UK) applied to meteorological observations from a dense network of automatic weather stations in the Galician region (NW Spain). The methodology explicitly models the non-stationary vertical structure of the atmosphere by decomposing the refractivity field into a deterministic altitude-dependent drift and a stochastic residual component characterized by an exponential variogram. Validation, performed using independent test stations bounding the regional vertical profile, demonstrates that the UK approach significantly outperforms Ordinary Kriging (OK). UK not only reduces mean errors and improves linear agreement, but critically minimizes systematic bias and extreme outlier occurrences (P95). Beyond accurate spatial interpolation, the dynamically estimated vertical drift retrieves the macroscopic refractivity gradient, serving as a direct, real-time diagnostic tool to classify anomalous radio-frequency (RF) propagation regimes (e.g., super-refraction and ducting) and supporting robust decision-making in complex topographies.

Geomatics doi: 10.3390/geomatics6020036

Authors: Abdul Sattar Sheikh Rizwan Shahid Abdullah Shah Aseer Ul Haq Tayyab Shah

This study evaluates the rooftop solar photovoltaic (PV) potential at the building level in the tourism-rich districts of Northern Khyber Pakhtunkhwa (KPK), Pakistan, using advanced geospatial analysis to support renewable energy planning. By combining the Area Solar Radiation tool with detailed building footprint data, the study identified solar energy potential and prioritized areas for PV system installations. Results show that approximately 35% of the 1.29 million buildings analyzed are suitable for solar panels, with energy generation capacity varying by building size and district. Spatial analysis further highlighted Union Councils (UCs) where over 50% of buildings are solar-suitable, enabling precise targeting of renewable energy initiatives. The study underscores the importance of integrating local geographical and socio-economic data to enhance the feasibility and scalability of solar energy solutions in rural and urban settings and can be used to guide policy prioritization and funding decisions. This research demonstrates how geospatial analysis and open data can drive localized clean energy adoption, directly contributing to Sustainable Development Goal 7 by advancing affordable and sustainable energy solutions.

Geomatics doi: 10.3390/geomatics6020035

Authors: Abigail Kelly Ramchandra Rimal Arpan Man Sainju

Accurate identification of road network intersections is essential for urban planning, autonomous navigation, and traffic safety analysis. However, standard approaches relying on local geometric attributes often overlook essential topological information. This limitation is particularly problematic for intersection types that are locally similar but topologically distinct. To address this, we propose a hybrid framework that augments intrinsic node attributes with Generalized Random Dot Product Graph embeddings and neighbor-aggregated features. We utilize tree-based ensemble classifiers, specifically Random Forest and Extreme Gradient Boosting, to process this enriched feature set. Unlike standard spectral methods that assume homophily, this approach explicitly models heterophilous connectivity to capture structural patterns where dissimilar nodes connect. Experiments on a real-world urban road network demonstrate that this topological augmentation yields consistent and robust improvements. The proposed integration with the Extreme Gradient Boosting model achieves a Macro ROC AUC of 0.8966 and a Micro F1 score of 0.7005, outperforming the baseline model (ROC AUC 0.8100, Micro F1 0.5919). Performance gains are most pronounced for topologically ambiguous intersection classes, confirming that local attributes alone fail to capture structural distinctions. These results demonstrate that latent structural context is a critical discriminator for granular road intersection classification.

Geomatics doi: 10.3390/geomatics6020034

Authors: Kristián Bene Julián Tomaštík

This research addresses the increasing demand for low-cost GNSS solutions in natural resources management and geodesy by comparing a dual-frequency RTK receiver and a single-frequency autonomous receiver under identical conditions. The novelty lies in the simultaneous testing of u-blox ZED-F9P and u-blox MAX-M10S receivers connected to a common antenna, eliminating different signal reception effects. The study also evaluates the horizontal accuracy and area determination accuracy and the influence of seasonal foliage. Experiments were conducted on three polygons with varying vegetation canopies during leaf-on and leaf-off periods. The ZED-F9P receiver demonstrated high accuracy and stability when using RTK corrections. Under canopy conditions, the average horizontal errors were 0.17–0.18 m during leaf-on and improved by 58% to approximately 0.07 m during leaf-off season. The average area determination errors remained below 2%, confirming its suitability for precise mapping. In contrast, the MAX-M10S receiver showed substantial variability under vegetation. Its average horizontal errors reached 1.5–3.0 m during leaf-on season, with the maximum errors exceeding 5 m. Its seasonal improvement ranged from 41 to 54%, while its area errors reached up to 14.7%. The study confirms that while vegetation cover and seasonal foliage are limiting factors for both types of devices, low-cost RTK receivers represent a viable alternative to expensive professional instruments, even in more challenging conditions.

Geomatics doi: 10.3390/geomatics6020033

Authors: Stavros Kolios Mariana Mandilara

Monitoring forest density is essential for understanding ecosystem health, wildfire risk, and post-disturbance recovery. This study proposes a robust methodology to extract forest density classes exclusively using Sentinel-2 multispectral imagery combined with vegetation indices (VIs), textural parameters, and spatial clustering metrics. The approach was applied to the northern part of Euboea Island, Greece, as a pilot area severely affected by a wildfire in August 2021. Four cloud-free Sentinel-2 images (2017–2024) were selected to capture pre- and post-fire conditions. A set of nine VIs—representing vegetation vigor, chlorophyll content, soil exposure, and canopy moisture—were calculated and statistically assessed for independence. To enhance classification accuracy, texture measures (homogeneity, correlation, and entropy) and spatial autocorrelation metrics (Moran’s I, Getis-Ord Gi) were derived for selected VIs. Supervised classification was performed using the Maximum Likelihood algorithm, yielding overall accuracies up to 89.4% and kappa coefficients above 0.85 when combining VIs with texture and spatial metrics. Results revealed a dramatic 49.3% reduction in forest cover immediately after the wildfire, with partial recovery (to 77.9% of pre-fire levels) three years later, mainly as a low-density forest. Approximately 12.1% of forest cover failed to regenerate, indicating potential long-term ecosystem degradation. The proposed approach provides a computationally efficient, high-accuracy alternative to data-fusion methods involving (Light Detection and Ranging) LiDAR or (Synthetic Aperture Radar) SAR datasets, making it suitable for operational forest monitoring and fire-risk management.

Geomatics doi: 10.3390/geomatics6020032

Authors: Dániel Márton Kovács István Péter Kovács Levente Ronczyk

Post-mining surface uplift has affected the northeastern part of Pécs, Hungary, since the closure of underground coal mines in the 1990s. This study synthesises 30 years of SAR data (ERS, Envisat, and Sentinel-1) with geodetic surveys, groundwater monitoring, and over 900 residential damage reports to investigate the spatiotemporal evolution of this deformation. In densely built urban environments, Persistent Scatterer Interferometry (PS-InSAR) provides spatially detailed complementary data measurements to traditional levelling, particularly where survey lines offer limited coverage. The performed combined analysis tracked deformation from initial uplift through stabilisation, revealing a clear transition: while early lower-order measurements showed limited correlation, modern Sentinel-1 data and high-order geodetic surveys (post-2014) demonstrate a robust correlation (R = 0.65). The cross-correlation of InSAR results with geodetic and hydrogeological records revealed that aquifer recovery by the 2010s coincided with the onset of surface stability. While over 90% of 1990s residential damage claims fell within measured deformation zones, this relationship weakened over time, with recent claims showing little spatial connection with ground movements. This highlights the complementary strengths of InSAR and geodetic techniques. It demonstrates the value of integrating geotechnical and socio-economic datasets, providing a transferable framework for reliable deformation monitoring and risk management in post-mining urban environments.

Geomatics doi: 10.3390/geomatics6020031

Authors: Gabriele Bitelli Anna Forte Emanuele Mandanici

Archaeological sites characterized by the coexistence of extensive above-ground terrain and hypogeum structures present major challenges for accurate and comprehensive geospatial documentation. Conventional survey approaches—such as static terrestrial laser scanning (TLS), total-station measurements, and aerial photogrammetry—often suffer from operational constraints, particularly in the presence of narrow underground spaces, low or absent illumination, harsh environmental conditions, and restrictions on UAV deployment. Additional complexity arises when both surface and subterranean elements must be consistently georeferenced to a common global reference system, especially where establishing a traditional topographic–geodetic control network is impractical. Within the framework of the EIMAWA Egyptian–Italian Mission conducted by the University of Milano since 2018, the Geomatics group of the University of Bologna designed and implemented a multi-scale multi-technique 3D documentation workflow, with a prominent role assumed by Simultaneous Localization and Mapping (SLAM) mobile laser scanning. The approach was supported by GNSS measurements providing centimetric accuracy. SLAM was employed to document both the surface necropolis and multiple hypogeal tombs, enabling rapid acquisition of dense three-dimensional data in environments where traditional techniques are limited. All datasets were integrated within a unified reference system, resulting in a coherent, multi-layered spatial dataset representing both landscape and underground spaces. The results demonstrate that SLAM can produce dense point clouds that document at few-centimetric level accuracy and continuously both above- and below-ground contexts. Quantitative analyses of the co-registration and mutual alignment of multiple SLAM datasets confirm a high degree of internal consistency, further enhanced through post-processing refinement. Overall, the experience indicates that this solution represents a practical and reliable technique for complex archaeological surveying.

Geomatics doi: 10.3390/geomatics6020030

Authors: Maurizio Barbarella Andrea Lugli

This work presents a simulation-based approach to support the planning of Terrestrial Laser Scanning (TLS) surveys for landslide monitoring. Starting from an approximate digital model of the slope, the method estimates the spatial distribution of positional error induced by scanner characteristics, laser beam divergence and, critically, by the incidence angle between the laser beam and the local surface normal. Because complex morphologies cause rapid local variations in incidence angle, neighbouring points may exhibit markedly different error magnitudes, making a direct classification of raw error values insufficient to delineate homogeneous areas. To address this, a multidimensional variable is defined for each simulated point, combining position, estimated error, distance from the scanner and incidence angle. After dimensionality reduction through PCA, the dataset is clustered using K-means with a sufficiently large number of clusters to preserve spatial resolution. Each cluster is associated with a representative error level, and clusters are then merged into broader error classes that delineate zones of comparable expected precision. The procedure is repeated for alternative scanner positions, enabling a comparative evaluation of achievable accuracy across the slope and the identification of areas requiring multiple scans. The method provides a quantitative, reproducible framework to guide TLS station selection and optimize survey design in complex morphological settings.

Geomatics doi: 10.3390/geomatics6020029

Authors: Sajib Sarker Israt Jahan Tanveer Ahmed Abul Azad Xin Wang

Tropical cyclones rank among the most destructive natural hazards globally, posing significant threats to coastal ecosystems and communities. Mangrove forests, renowned for their ecological importance and coastal protection services, are vulnerable to these disturbances, suffering structural damage, habitat loss, and disruption of vital ecosystem functions. Conventional field-based assessment methods often fall short in capturing the rapid and widespread impacts of cyclones, particularly in remote or cloud-obscured regions. This review aims to provide a comprehensive synthesis of remote sensing applications for monitoring cyclone-induced impacts on mangrove and coastal ecosystems worldwide. Through a systematic literature review of 74 peer-reviewed articles from 1990 to 2025, the study evaluates the utility of optical sensors, radar systems, and multi-sensor platforms in assessing inundation, vegetation damage, and ecosystem service loss. Key methodological advances such as time-series analysis, machine learning, and UAV-based validation are highlighted, alongside critical gaps including limited geographic coverage, weak validation practices, and minimal socio-economic integration. Notably, 75.4% of reviewed studies are concentrated in Asia, with Bangladesh and India alone accounting for 44.6% of the total literature, underscoring a pronounced geographic bias. The findings underscore the need for robust, near-real-time monitoring frameworks that combine satellite technologies with ground data and community engagement. Ultimately, the review advocates for an integrated, multi-sensor, and participatory approach to cyclone resilience, offering valuable insights for future research, disaster response planning, and sustainable mangrove management.

Geomatics doi: 10.3390/geomatics6020028

Authors: Rabina Twayana Karima Hadj-Rabah

Wildfires have become more frequent and intense worldwide due to climate change and anthropogenic activities, which is why accurate and timely burned area mapping is essential for estimating damage and effective post-fire recovery planning. Synthetic Aperture Radar (SAR) data, which operates under all weather conditions and day-night cycles, offers a reliable source for burned area mapping. In this context, several studies have explored the use of dual-polarization SAR imagery and machine learning, yet the influence of multi-date, dual-orbit pass data and texture features remained unexplored. Therefore, this study aims to assess the Sentinel-1 acquisition configurations, varying in temporal depth and orbital direction, for wildfire burned area mapping, considering the recent Palisades wildfire event as a study area. A comparative study was conducted across different scenarios to evaluate the effectiveness of using single-date versus multi-date SAR imagery, the integration of ascending and descending orbit passes, and the contribution of Grey-Level Co-occurrence Matrix texture features. The performance of Random Forest (RF) and Extreme Gradient Boosting classifiers was analyzed through the scenarios mentioned above. The single-date configuration using RF achieved an accuracy of 82.34%, F1-score of 81.43%, precision of 83.07%, recall of 80.84%, and ROC-AUC of 90.88%, whereas the multi-date approach reached 85.78%, 85.15%, 86.45%, 84.56%, and 93.28%, respectively. Our study highlights the importance of acquisition configuration and texture information for reliable SAR-based wildfire burned area assessment.

Geomatics doi: 10.3390/geomatics6020027

Authors: Vinuri Nilanika Goonetilleke Muditha K. Heenkenda Kamil Zaniewski

Climate change is a critical global challenge, and the building sector accounts for nearly 30% of global greenhouse gas (GHG) emissions, remaining a key target for mitigation. Indoor environments contribute significantly to GHG emissions, primarily through heating, cooling, lighting, and occupant-driven energy use. Indoor mapping, serving as the foundation for Digital Twins (DTs), provides a spatiotemporal framework that integrates sensor data with Building Information Modelling (BIM), Geographic Information Systems (GIS), and Internet of Things (IoT) to support energy-efficient, low-carbon building operations. This review examined the role of indoor mapping in understanding, modelling, and reducing GHG emissions in buildings. It synthesized current advancements in indoor spatial data acquisition, ranging from Light Detection And Ranging (LiDAR) and Simultaneous Localization and Mapping (SLAM) to deep learning-based floor plan extraction, and evaluated their contribution to improved indoor environmental analysis. The review highlighted emerging techniques, challenges, and gaps, particularly the limited integration of physical indoor spaces with virtual layers representing assets, occupants, and equipment. Addressing this gap requires embedding spatial modelling as an intermediate analytical layer that structures and contextualizes sensor data to support spatiotemporal decision-making. Overall, this review demonstrated that indoor mapping plays a critical role in transforming spatial information into actionable insights, enabling more accurate energy modelling, enhanced real-time building management, and stronger data-driven strategies for GHG mitigation in the built environment.

Geomatics doi: 10.3390/geomatics6020026

Authors: Wenye Ou Dongqi Wei Hui Guo Yueqin Zhu Wenlong Han Jian Li

Geological hazard data exhibits high-volume and multi-type characteristics, specifically characterized by inherent complexity; measurement uncertainty; cross-source heterogeneity; underdeveloped semantic organization; and fragile inter-entity associations. Consequently, advanced modeling techniques coupled with robust extraction frameworks become imperative for effective unstructured data governance. To address this challenge, we propose a content–knowledge representation framework that decomposes and reconstructs disaster data using fine-grained content entities as base units. This approach allows for a unified description, objectification, ordering, hierarchical storage, and indexed categorization of unstructured information. Furthermore, we develop specialized text extraction algorithms tailored to document imagery and vector maps—facilitating the systematic application of information retrieval techniques while efficiently targeting specific thematic content. Our method outperforms two representative deep learning architectures (Fast CNN and FCN), demonstrating superior performance in segmenting target regions and precisely detecting textual elements, tables, and geographic features within complex datasets. By studying the modeling and extraction technology of unstructured geologic data, this paper establishes the value chain of geologic result data, which can provide strong support for digital management of geologic disaster data and improve work efficiency.

Geomatics doi: 10.3390/geomatics6020025

Authors: Yasushi Shiraishi Takuya Hiroshima Satoshi Tsuyuki

The GCOM-C satellite possesses optimal wavelength bands around 530 nm and 570 nm for monitoring seasonal variations in the photochemical reflectance index (PRI) and chlorophyll–carotenoid index (CCI), which are sensitive to carotenoid contents and its ratio to chlorophyll contents, respectively. As well as NDVI, these indices are excellent indicators for monitoring pigment contents of evergreen trees in winter, which are considered susceptible to climate change impacts. In this study, to investigate the characteristics and usefulness of the GCOM-C-derived indices, the seasonal variations in these indices were analyzed between 2018 and 2024 at two evergreen forest sites in Japan, and compared to CCI and NDVI derived from MODIS, which also has a band near 530 nm. The satellite observation results show that the decreases in all indices for both satellites in winter were observed in the order of PRI, CCI, NDVI. This is thought to indicate that carotenoid contents increased in response to the decrease in land surface temperature to mitigate low-temperature stress, followed by a delayed decrease in chlorophyll contents. GCOM-C showed 0.1 larger NDVI values and 0.2 larger CCI values than MODIS, and the difference was estimated to be largely influenced by the disparity in sensor sensitivity in the red bands. The dispersion of each index was reduced by using data with small sensor zenith angles (below 20 degrees for GCOM-C and 0 to 30 degrees for MODIS); however, MODIS showed a decline in observation accuracy due to satellite drifting in 2024. Spectral measurements of leaves collected at the site also showed similar VI decreases; however, the satellite-derived CCI were 0.12 lower, suggesting that reflection from dead leaves influences the satellite data. This study confirmed that GCOM-C, which can measure both PRI and CCI with high spatial resolution, is suitable for observing seasonal variations in carotenoid and chlorophyll contents in evergreen forests.

Geomatics doi: 10.3390/geomatics6020024

Authors: Ákos Jakobi Márton Prorok Tünde Szabó

Mobile phone usage data inherently involve many spatial elements; therefore, gathering extensive individual mobile phone records can offer unique insights into human spatial behavior at both personal and societal levels. This study contributes to travel behavior research by examining group-level human mobility obtained from millions of Hungarian mobile phone records. After developing mobility metrics from georeferenced cellular data, we applied a computationally efficient two- and three-dimensional Fuzzy C-Means (FCM) unsupervised clustering algorithm to identify groups of people with similar behavioral traits. The resulting membership probabilities—based on combinations of mobility metrics and user attributes—indicated that high travel distances or higher equipment prices could lead to a clear separation in travel behavior, while complex mobility patterns appeared less influenced by human factors such as age. Furthermore, even though the fuzzy outcomes offer probabilistic rather than exact group assignments, the generated maps revealed distinct, non-random spatial patterns.

Geomatics doi: 10.3390/geomatics6020023

Authors: Kyriakos Michaelides Athos Agapiou

Floods represent one of the most frequent and damaging natural hazards in Mediterranean mountain regions, where intense rainfall and complex topography amplify runoff and inundation risk. This study aims to delineate flood-susceptible zones in the Monti Lucretili area of central Italy, an environmentally sensitive and culturally significant landscape that hosts archeological remains and UNESCO listed dry-stone heritage using an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) approach. Fifteen (15) conditioning factors, including elevation, slope, rainfall, soil, lithology, land use/land cover, drainage density, and proximity to rivers and roads, were derived from open-access satellite remote sensing and spatial datasets. The AHP model produced a flood susceptibility index ranging from 1.806 to 4.465, reclassified into five categories from very low to very high zones. The resulting map indicates that low- and moderate-susceptibility zones dominate the study area, while high and very high classes are primarily concentrated along valleys and drainage corridors. Model validation indicates strong regional-scale predictive performance, with 85.36% of modeled flood-prone areas located within high- to very-high-susceptibility zones and an AUC value of 0.82. Overall, the study highlights the potential of open-access AHP–GIS modeling as a practical screening tool for flood susceptibility assessment and heritage-aware spatial planning in Mediterranean environments.

Geomatics doi: 10.3390/geomatics6020022

Authors: Muhammed Enes Atik Mehmet Arkali

Olive groves are an important agricultural component in the Mediterranean region that offers various ecological benefits. The olive tree has tremendous cultural and economic value and is cultivated over a wide geographical range. It is essential to actively implement innovative agricultural practices to achieve efficient, sustainable olive cultivation. Automatic tree identification in olive groves is an essential tool for applications such as tree health monitoring and yield estimation. Deep learning-based approaches, which have recently gained prominence, hold significant potential for this purpose. However, the large amount of training data required by deep learning methods increases their time and effort costs. Data augmentation methods have been developed to solve this problem. In this study, olive tree detection and segmentation from unmanned aerial vehicle (UAV) images were performed using current You Only Look Once (YOLO) architectures (YOLOv8, YOLOv10, YOLOv11, YOLOv12) and transformer-based object detection algorithms (Real-Time DEtection TRansformer (RT-DETR) and Roboflow-DEtection Transformer (RF-DETR)). Two different datasets, one of which was a new dataset generated within the scope of this study, were used in this study. To investigate the effect of data augmentation on algorithm performance, both the original datasets and the augmented datasets were used. As a result of the study, 0.987 mAP was obtained with YOLOv11n, YOLOv11s, and YOLOv12s on the Olive Tree Detection (OTD) dataset, while 0.884 mAP was obtained with YOLOv8l and YOLOV8x on the Yalova dataset.

Geomatics doi: 10.3390/geomatics6010021

Authors: Wei Wang Boyuan Lu Yihan Li Fujiang Ji

Accurate shoreline positioning is critical for coastal monitoring and management, yet deep learning shoreline products are often evaluated using conventional waterbody segmentation metrics that do not explicitly measure boundary alignment. Using 20,689 NAIP aerial images covering the Great Lakes shoreline from the Coastal Aerial Imagery Dataset (CAID), we benchmark five semantic segmentation models and quantify the inconsistency between image-level segmentation accuracy (pixel accuracy, IoU) and shoreline positioning accuracy measured by the Shoreline Intersection Ratio (SIR) and Average Eulerian Distance (AED). Although segmentation performance is consistently high (pixel accuracy typically >98% and IoU often >90%), shoreline agreement is substantially lower and strongly landscape-dependent, with the poorest results in wetlands and urban scenes. Correlation analyses across coastal types and water-surface conditions show that the correspondence between segmentation metrics and SIR varies with shoreline morphology. Multivariate regressions confirm the shoreline-to-water ratio (SWR) as the dominant predictor of both SIR and AED, while shoreline complexity (SCI) and mean water hue (MWH) have weaker, context-dependent effects. These results demonstrate that high segmentation accuracy does not guarantee precise shoreline delineation and motivate shoreline-aware evaluation protocols.

Geomatics doi: 10.3390/geomatics6010020

Authors: Fama Mbengue Mamadou Adama Sarr Egor Prikaziuk Gayane Faye Mamadou Simina Dramé Abdoul Aziz Diouf

Rice field mapping is essential for effective agricultural and water resource management due to high land pressure. This study aims to map paddy rice by combining segmentation techniques and phenological metrics derived from optical time series. Thus, a crop segmentation-based approach was developed using Sentinel-2 imagery (2018–2019) to assess the paddy rice extent in the Senegal River Delta (SRD). Two super-pixel segmentation algorithms were evaluated to optimize the identification of rice plots by integrating spectral and spatial characteristics from the green, red, and near-infrared (NIR) bands. In this study, the Felzenszwalb outperformed the Quickshift algorithm, achieving a median intersection over union (IoU) of 0.25 compared to 0.20 for the segmentation of rice fields. The analysis of NDVI time series enabled the identification of key stages in the rice phenological cycle. Two machine learning algorithms (i.e., Random Forest and XGBoost) were compared for rice crop detection. Random Forest delivered a better performance (AUC = 0.93, OA = 0.98, F1-score = 0.98) than the XGBoost (AUC = 0.92, OA = 0.98, F1-score = 0.98). Overall, the results indicated that the approach could accurately identify paddy rice fields, and thus improve decision making and support food security management in the region.

Geomatics doi: 10.3390/geomatics6010019

Authors: Marwa Wahba Ayman G. Awadallah Nabil A. AwadAllah Maysara Ghaith

Flash floods in arid regions dictate a rapid flood inundation mapping for early warning. However, hydrodynamic models, such as HEC-RAS, provide accurate flood mapping but require extensive topographical data and high computational resources. The GeoFlood method offers a rapid alternative for early warning relying on terrain-driven framework and simple hydraulics. This study examined GeoFlood applicability on two arid catchments and tested its sensitivity for different return periods, Manning coefficients, and wadi length segmentations. The original GeoFlood method showed good consistency with HEC-RAS in well-defined wadis but relatively poor performance in flat areas, with segmentation and slope calculation significantly affecting GeoFlood accuracy and robustness. To overcome these limitations, slope calculation was improved using the Theil–Sen trend, and segmentation was automated using the penalized cost approach Continuous Piecewise Optimal Partitioning (CPOP) to detect slope breakpoints. CPOP provides superior and robust performance without prior knowledge of the best segmentation lengths, producing smoother slopes at accurate breakpoints with a Fowlkes–Mallows (FM) index of 0.88 in flat areas and an error bias of 1.05 compared to a variable FM from 0.72 to 0.88 and an error bias from 0.81 to 1.3 for the original GeoFlood. The enhanced GeoFlood provides reliable robust results in arid regions when data are scarce.

Geomatics doi: 10.3390/geomatics6010018

Authors: Enrico Borgogno-Mondino

Last year signaled a great step forward in my editorial career and, I hope, a good year for the journal [...]

Geomatics doi: 10.3390/geomatics6010017

Authors: Botond Szász Gábor Brolly Géza Király

In this study, we investigated the effects of flight and processing parameters on the accuracy of UAV-based photogrammetric digital terrain models (DTM) generated from RGB imagery in ageing deciduous and mixed forest stands. Four 100 × 100 m sample plots were selected, for which the reference terrain surface was established using terrestrial laser scanning. Photogrammetric DTMs derived from various parameter combinations were compared against this reference, analysing the magnitude of deviations and the influence of individual parameters through SHAP (SHapley Additive exPlanations) analysis. Based on the identified effects, we provide recommendations for optimal workflows and parameter settings. The processing chain also incorporates a targeted raster-level smoothing procedure developed by the authors, which effectively removes DTM errors caused by point cloud noise left by filtering algorithms, thereby reducing extreme deviations from the reference surface. The results show that the absolute mean elevation error is primarily influenced by flight parameters and ground point classification scale (parameter of the lasground algorithm). Optimal flight parameters were determined at a flight altitude of 100 m, with 80% front and 90% side overlap. Furthermore, a ground classification scale of 9 m proved optimal in forested environments. The proposed targeted smoothing significantly reduced extreme errors, yielding DTMs with a mean error of approximately 6 cm and maximum deviations of about 40 cm. These accuracies demonstrate that UAV-based photogrammetry, when carefully parameterised, provides a reliable basis for surface model normalization and subsequent forest structural analyses.

Geomatics doi: 10.3390/geomatics6010016

Authors: Sergio García-Arias Manuel A. Florez Joaquín Andrés Valencia Ortiz

Natural hydrogen has gained attention as a low-carbon energy vector, and some reported surface expressions have been linked to subcircular patterns, or fairy circles (FC), that may be detectable in multispectral satellite imagery. The Carolina Bays region, on the eastern coast of the United States, was selected because it hosts abundant, well-mapped subcircular depressions. This study aims to comparatively evaluate machine learning algorithms for identifying subcircular structures using Landsat-8 data. We processed 105 Collection 2 Level 2 scenes, masking clouds and shadows using the Level 2 quality band. Pixel-level labels were determined using a well-curated public dataset, derived from a high-resolution LiDAR survey. Traditional models (logistic regression, random forest, and multilayer perceptron) achieved precision scores below 0.66 and enabled a variable-importance analysis, which identified Band 3 (green), Band 6 (SWIR1), and five Normalised Unit Indices as the most predictive features. Deep learning models improved detection, and a U-Net architecture allowed for pixel-level segmentation of FC-like structures, producing false positives mostly in cloudy or shadowed areas. Overall, the results suggest that FC detection from multispectral data alone remains challenging due to class overlap and cloud/shadow contamination. Future work could explore integrating additional non-spectral descriptors, such as morphometric variables, to reduce ambiguities.

Geomatics doi: 10.3390/geomatics6010015

Authors: Yuri Gorokhovich Ivan V. Morozov Günay Erpul Chia-Ying Lee Carolynne Hultquist Zola Qingyang Yin

This study applies a qualitative Geographic Information Systems model that integrates satellite-derived wind and rainfall data to predict potential debris-flow locations in Puerto Rico triggered by Hurricane Maria (2017). A key innovation of the model is the use of wind-driven rainfall (WDR), calculated at multiple elevation levels using satellite wind data and Global Precipitation Measurement (GPM) precipitation at three time steps. WDR replaces the conventional use of total rainfall commonly applied in landslide modeling. A second innovation is the use of WDR slope exposure to hurricane direction in place of a standard aspect parameters. The model assumes that WDR was the primary trigger of debris flows during the hurricane. Predicted debris-flow locations were compared with mapped debris-flow inventories using threshold distances of 1000, 500, and 250 m. Prediction rates ranged from 30 to 100%, and success ratios from 10 to 90%, depending on elevation and distance thresholds, with the best performance at 500 and 1000 m ranges. Model performance could be enhanced through higher-resolution satellite observations of wind, soil moisture, and precipitation, supporting potential real-time hazard applications. Model limitations include its empirical nature, qualitative structure, and current applicability to equatorial or sub-equatorial regions affected by hurricanes or typhoons. Further testing and regional calibration are recommended.

Geomatics doi: 10.3390/geomatics6010014

Authors: Andrea Demartis Fabio Giulio Tonolo Francesco Barchi Samuel Zanella Andrea Acquaviva

Illegal dumping poses serious risks to ecosystems and human health, requiring effective and timely monitoring strategies. Advances in uncrewed aerial vehicles (UAVs), photogrammetry, and deep learning (DL) have created new opportunities for detecting and characterizing waste objects over large areas. Within the framework of the EMERITUS Project, an EU Horizon Europe initiative supporting the fight against environmental crimes, this study evaluates the performance of pre-trained prompt-based multimodal (PBM) DL models integrated into ArcGIS Pro for object detection and segmentation. To test such models, UAV surveys were specially conducted at a semi-controlled test site in northern Italy, producing very high-resolution orthoimages and video frames populated with simulated waste objects such as tyres, barrels, and sand piles. Three PBM models (CLIPSeg, GroundingDINO, and TextSAM) were tested under varying hyperparameters and input conditions, including orthophotos at multiple resolutions and frames extracted from UAV-acquired videos. Results show that model performance is highly dependent on object type and imagery resolution. In contrast, within the limited ranges tested, hyperparameter tuning rarely produced significant improvements. The evaluation of the models was performed using low IoU to generalize across different types of detection models and to focus on the ability of detecting object. When evaluating the models with orthoimagery, CLIPSeg achieved the highest accuracy with F1 scores up to 0.88 for tyres, whereas barrels and ambiguous classes consistently underperformed. Video-derived (oblique) frames generally outperformed orthophotos, reflecting a closer match to model training perspectives. Despite the current limitations in performances highlighted by the tests, PBM models demonstrate strong potential for democratizing GeoAI (Geospatial Artificial Intelligence). These tools effectively enable non-expert users to employ zero-shot classification in UAV-based monitoring workflows targeting environmental crime.

Geomatics doi: 10.3390/geomatics6010013

Authors: Nicola Aimane Dimarco Ibtissam Faraji Miriam Wahbi Mustapha Maatouk Hakim Boulaassal Otman Yazidi Aalaoui Omar El Kharki

Wildfires represent a growing environmental and socio-economic threat across Mediterranean landscapes, where prolonged summer droughts and human activity increasingly shape ignition susceptibility. This study presents an open and reproducible modelling framework for comparing the relative influence of anthropogenic and biophysical drivers of wildfire ignition susceptibility across selected Mediterranean regions. Using harmonized 500 m predictors derived from global remote-sensing datasets, we integrate vegetation condition, topography, climatic context, and human pressure indicators within a cloud-based Google Earth Engine workflow. Two tree-based machine-learning models (Random Forest and Extreme Gradient Boosting) are trained and evaluated using spatial cross-validation and cross-region transfer experiments. Results consistently highlight the dominant role of anthropogenic pressure in shaping ignition susceptibility across all study areas, with night-time lights and human modification indices contributing to the largest share of model importance. Both models achieve high predictive performance (AUC > 0.90) and retain stable accuracy under cross-region transfer (mean transfer AUC ≈ 0.85), indicating partial generalization of human-driven ignition patterns across Mediterranean landscapes. Beyond predictive performance, the principal contribution of this work lies in its harmonized cross-regional comparison and explicit evaluation of model transferability using open data and scalable cloud processing. The resulting susceptibility maps provide a transparent and operational basis for comparative wildfire risk assessment and prevention planning within comparable Mediterranean contexts.

Geomatics doi: 10.3390/geomatics6010012

Authors: Kyle Loggenberg Albert Strever Zahn Münch

The mapping of vineyard cultivars presents a substantial challenge in digital agriculture due to the crop’s high intra-class heterogeneity and low inter-class variability. High-dimensional spectral datasets, such as hyperspectral or spectrometry data, can overcome these difficulties. However, research has yet to fully address the need for optimal spectral feature subsets tailored for grapevine cultivar discrimination, while few studies have systematically examined waveband subsets that transfer effectively across different learning algorithms. This study sets out to address these gaps by introducing a Partial Least Squares (PLS)-based ensemble feature selection framework with Weighted Borda Count aggregation for cultivar discrimination. Using in-field spectrometry data, collected for six cultivars, and 18 PLS-based feature selection methods spanning filter, wrapper, and hybrid approaches, the PLS–ensemble identified 100 wavebands most relevant for cultivar discrimination, reducing dimensionality by ~95%. The efficacy and transferability of this subset were evaluated using five classification algorithms: Oblique Random Forest (oRF), Multinomial Logistic Regression (Multinom), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and a 1D Convolutional Neural Network (CNN). For oRF, Multinom, SVM, and MLP, the PLS–ensemble subset improved accuracy by 0.3–12% compared with using all wavebands. The subset was not optimal for the 1D-CNN, where accuracy decreased by up to 5.7%. Additionally, this study investigated waveband binning to transform narrow hyperspectral bands into broadband spectral features. Using feature multicollinearity and wavelength position, the 100 selected wavebands were condensed into 10 broadband features, which improved accuracy over both the full dataset and the original subset, delivering gains of 4.5–19.1%. The SVM model with this 10-feature subset outperformed all other models (F1: 1.00; BACC: 0.98; MCC: 0.78; AUC: 0.95).

Geomatics doi: 10.3390/geomatics6010011

Authors: Liliia Hebryn-Baidy Gareth Rees Sophie Weeks Vadym Belenok

Intensifying urbanisation in the Arctic, particularly in spatially constrained coastal and island cities, requires reliable information on long-term land-use/land-cover (LULC) change to assess environmental impacts and support urban planning. However, multi-decadal, high-resolution LULC datasets for Arctic cities remain limited. In this study, we quantify LULC change on Tromsøya (Tromsø, Norway) from 1984 to 2024 using a Random Forest classifier applied to multispectral satellite imagery from Landsat and PlanetScope, complemented by LiDAR-derived canopy height models (CHM) and building footprints. We mapped LULC change trajectories and examined how these shifts relate to district-level population redistribution using gridded population data. The integration of a LiDAR-derived CHM was found to substantially improve the accuracy of Landsat-based LULC mapping and to represent the dominant source of classification gains, particularly for spectrally similar urban classes such as residential areas, roads, and other paved surfaces. Landsat augmented with CHM was shown to achieve practical equivalence to PlanetScope when the latter was modelled using spectral features only, supporting the feasibility of scalable and cost-effective long-term monitoring of urbanisation in Arctic cities. Based on the best-performing Landsat configuration, the proportions of artificial and green surfaces were estimated, indicating that approximately 20% of green areas were transformed into artificial classes. Spatially, population growth was concentrated in a small number of districts and broadly coincided with hotspots of green-to-artificial conversion The workflow provides a reproducible basis for long-term, district-scale LULC monitoring in small Arctic cities where data constraints limit the consistent use of high-resolution image.

Geomatics doi: 10.3390/geomatics6010010

Authors: Moritz Hütten

Knowledge about vessel activity in port areas and around major industrial zones provides insights into economic trends, supports decision-making for shipping and port operators, and contributes to maritime safety. Vessel data from terrestrial receivers of the Automatic Identification System (AIS) have become increasingly openly available, and we demonstrate that such data can be used to infer port activities at high resolution and with precision comparable to official statistics. We analyze open-access AIS data from a three-month period in 2024 for Tokyo Bay, located in Japan’s most densely populated urban region. Accounting for uneven data coverage, we reconstruct vessel activity in Tokyo Bay at ~30 m resolution and identify 161 active berths across seven major port areas in the bay. During the analysis period, we find an average of 35±17stat vessels moving within the bay at any given time, and 293±22stat+65syst−10syst vessels entering or leaving the bay daily, with an average gross tonnage of 11,860−50+280. These figures indicate an accelerating long-term trend toward fewer but larger vessels in Tokyo Bay’s commercial traffic. Furthermore, we find that in dense urban environments, radio shadows in vessel AIS data can reveal the precise locations of inherently passive receiver stations.

Geomatics doi: 10.3390/geomatics6010009

Authors: Hamzah Tahir Ami Hassan Md Din Thulfiqar S. Hussein Zaid H. Jabbar

One of the most significant impacts of climate change is sea-level rise, which is increasingly threatening to the coastal setting, infrastructure, and socioeconomic systems. Since a change at the sea level is spatially non-uniform and highly modulated by local oceanographic and climatic events, local or regional-scale measurements are necessary—especially in semi-enclosed basins. This paper examines the long-term variability of sea levels throughout the Persian Gulf and illustrates a strong spatial variance of the trends over the past and the future. Using three decades of satellite-derived observations, regional sea-level trends were estimated from monthly sea-level anomaly (SLA) data, which were also used to generate future projections to 2100. The analysis shows that the rate of sea-level rise along the UAE–Oman stretch is 3.88 mm year−1 and that of the Strait of Hormuz is 5.23 mm year−1, with a mean of 4.44 mm year−1 in the basin. Statistical forecasts of sea-level change were projected by a statistical forecasting scheme with high predictive ability with the optimal configuration of an average of 0.0391 m, an RMSE of 0.0492 m, and an R2 of 0.80 when independent validation was conducted. It is estimated that by 2100, the average rise of the sea level in the Persian Gulf is about 0.30–0.40 m, and the peak rise in sea level is at the Strait of Hormuz. Since these projections are based on statistical extrapolation rather than physics-based climate models, they are interpreted within the uncertainty envelope defined by IPCC AR6 scenarios. This study presents a unique, regionally resolved viewpoint on sea-level rise that is relevant to coastal risk management and adaptation planning in semi-enclosed marine basins by connecting robust statistical performance with physically interpretable regional patterns.

Geomatics doi: 10.3390/geomatics6010008

Authors: Demirel Maza-esso Bawa Fousséni Folega Kueshi Semanou Dahan Cristian Constantin Stoleriu Bilouktime Badjaré Jasmina Šinžar-Sekulić Huaguo Huang Wala Kperkouma Batawila Komlan

Accurate estimation of above-ground biomass (AGB) is vital for carbon accounting, biodiversity conservation, and sustainable forest management, especially in tropical regions under strong anthropogenic pressure. This study estimated and mapped AGB in the Atacora Mountain Chain, Togo, using a multi-source remote sensing approach within Google Earth Engine (GEE). Field data from 421 plots of the 2021 National Forest Inventory were combined with Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, bioclimatic variables from WorldClim, and topographic data. A Random Forest regression model evaluated the predictive capacity of different variable combinations. The best model, integrating SAR, optical, and climatic variables (S1S2allBio), achieved R2 = 0.90, MAE = 13.42 Mg/ha, and RMSE = 22.54 Mg/ha, outperforming models without climate data. Dense forests stored the highest biomass (124.2 Mg/ha), while tree/shrub savannas had the lowest (25.38 Mg/ha). Spatially, ~60% of the area had biomass ≤ 50 Mg/ha. Precipitation correlated positively with AGB (r = 0.55), whereas temperature showed negative correlations. This work demonstrates the effectiveness of integrating multi-sensor satellite data with climatic predictors for accurate biomass mapping in complex tropical landscapes. The approach supports national forest monitoring, REDD+ programs, and ecosystem restoration, contributing to SDGs 13, 15, and 12 and offering a scalable method for other tropical regions.

Geomatics doi: 10.3390/geomatics6010007

Authors: Robert Tenzer Wenjin Chen Shengwang Yu Zhengfeng Jin

Global Gravitational Models (GGMs) describe the Earth’s external gravitational field by a set of spherical harmonic (Stokes) coefficients. These coefficients are routinely used to compute the geoid model, while disregarding the upper continental crustal (i.e., topographic) masses above the geoid. Strictly speaking, however, these coefficients can describe only gravity field quantities at (or above) the Earth’s surface to satisfy Laplace’s equation. Consequently, the GGM coefficients cannot be used to define the geoid surface rigorously without accounting for the internal convergence domain and the gravitational effect of topographic masses. In most technical and scientific applications, the computation of the geoid model directly from the GGM coefficients has been accepted under the assumption that errors due to disregarding the internal convergence domain (inside the topographic masses) are typically less than a few centimeters (i.e., at the level of global geoid model uncertainties). In this study, we demonstrate that these errors reach several decimeters and even meters, with maxima in Tibet and Himalayas exceeding ~4 m. Moreover, relatively large errors, reaching decimeters, are already detected in regions with a moderately elevated topography. In scientific applications requiring a high accuracy, such errors cannot be ignored. Instead, GGM coefficients describing the Earth’s external gravitational field have to be corrected for the effect of (topographic) masses distributed above the geoid surface to obtain spherical harmonic coefficients that explicitly define the geoid globally. The explicit definition of the global geoid model in the spectral domain is derived in this study and used to compile spherical harmonic coefficients of the geoid up to degree/order 2160 from the EIGEN-6C4 global gravitational model.

Geomatics doi: 10.3390/geomatics6010006

Authors: Titus Karderic Williams Tarik Belrhaba Abdelahq Aangri Youssef Fannassi Zhour Ennouali John C. L. Mayson George K. Fahnbulleh Aıcha Benmohammadi Ali Masria

Coastal settlements worldwide face increasing threats from erosion, and the Monrovia coastline in Liberia is no exception. This study investigates shoreline dynamics along a 20.5 km stretch of Monrovia’s coast, which is characterized by low-lying elevations, gentle slopes, and sandy beaches. Using Landsat satellite imagery (1986–2025), supported by Sentinel-2 MSI and qualitative validation drone data, we analyzed historical shoreline change with remote sensing and GIS techniques. Shorelines were extracted using a band-ratio thresholding method and quantified with the Digital Shoreline Analysis System (DSAS 5.0), applying end-point rate (EPR), linear regression rate (LRR), and net shoreline movement (NSM). Exploratory projections for 2036 and 2046 were generated using a Kalman Filter model integrated into DSAS. Results show maximum historical erosion rates of up to 3.8 m/yr and accretion rates of up to 5.9 m/yr, with shoreline retreat reaching 150 m and advance up to 194 m. Erosion hotspots are projected for Hotel Africa, Westpoint, New Kru Town, and the JFK–ELWA corridor, while areas near the St. Paul and Mesurado estuaries are expected to accrete. These findings confirm historical trends and suggest that Monrovia will continue to face significant shoreline change, with implications for natural habitats, infrastructure, land loss, and population displacement.

Geomatics doi: 10.3390/geomatics6010005

Authors: Swarnabha Roy Cristhian Roman-Vicharra Hailiang Hu Souryendu Das Zhewen Hu Stavros Kalafatis

This study presents a validated framework to quantify regional sea-level risk on U.S. coasts by (i) extracting trends and seasonality from satellite altimetry (ADT, GMSL), (ii) learning regional dynamical regimes via PCA-embedded KMeans on gridded ADT time series, and (iii) coupling these regimes with socioeconomic exposure (population, income, ocean-sector employment/GDP) and wetland submersion scoring. Relative to linear and ARIMA/SARIMA baselines, a sinusoid+trend fit and an LSTM forecaster reduce out-of-sample error (MAE/RMSE) across the North Atlantic, North Pacific, and Gulf of Mexico. The clustering separates high-variability coastal segments, and an interpretable submersion score integrates elevation quantiles and land cover to produce ranked adaptation priorities. Overall, the framework converts heterogeneous physical signals into decision-ready coastal risk tiers to support targeted defenses, zoning, and conservation planning.

Geomatics doi: 10.3390/geomatics6010004

Authors: Alessia Giaquinto Giampaolo Ferraioli Silvio Del Pizzo

Recent advances in image-based 3D reconstruction have seen a shift from traditional photogrammetric techniques to learning-based methods, with Neural Radiance Fields (NeRFs) emerging as a powerful alternative. This study evaluates NeRF (via Nerfstudio) for accurate 3D reconstruction, comparing its performance to the widely used SfM-MVS pipeline implemented in Agisoft Metashape Professional (v. 2.2.1). This work considers a diverse set of datasets with varying object scales, capture methods (including drone imagery), and lighting conditions. Several assessment analyses were conducted, including evaluation of accuracy, completeness, planarity, and density of the reconstructed point clouds. Special attention was given to the influence of shadows and surface flatness on the fidelity of reconstruction. Results show that, despite not being initially designed for metric accuracy, NeRF demonstrates promising spatial consistency, producing reconstructions in some cases comparable to those of conventional methods when provided with precise camera poses. These findings suggest that NeRF may serve as a viable tool for 3D modelling in controlled settings. The applicability of the approach to more diverse and challenging scenarios remains to be explored, with particular attention to optimizing the reconstruction pipeline in terms of pose estimation, point cloud density, and robustness to varying lighting conditions.

Geomatics doi: 10.3390/geomatics6010003

Authors: Kristóf Kozma-Bognár Angéla Anda Ariel Tóth Veronika Kozma-Bognár József Berke

Climate change and related weather extremes are increasingly having an impact on all aspects of life. The main objective of the research was to analyze the impact of the most important meteorological elements and the image data of various water bodies of the Kis-Balaton wetland, Hungary. The primary question was which meteorological elements have a positive or negative influence on vegetational surface cover. Drones have facilitated the visual surveying and monitoring of challenging-to-reach water bodies in the area, including a lake and multiple channels. The individual channels had different flow conditions. Aerial surveys were conducted monthly, based on pre-prepared flight plans. Images captured by a Mavic 3 drone flying at an altitude of 150 m and equipped with a multispectral sensor were processed. The time-series images were aligned and assembled into orthophotos. The image details relevant to the research were segregated and classified using Maximum Likelihood classification algorithm. The reliability of the image data used was checked by Shannon entropy and spectral fractal dimension measurements. The results of the classification were compared with the meteorological data collected by a QLC-50 automatic climate station of Keszthely. The investigations revealed that the surface cover of the examined water bodies was different in the two years but showed a kind of periodicity during the year. In those periods, where photosynthetic organisms multiplied in a higher proportion in the water body, higher monthly average air temperatures and higher monthly global solar radiation sums were observed.