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.

Geomatics doi: 10.3390/geomatics6010002

Authors: Ondrej Benko Marek Fraštia Marián Marčiš Adrián Filip

The article addresses the issue of panoramic photogrammetry for the reconstruction of interior spaces. Such environments often present challenges, including poor lighting conditions and surfaces with variable texture for photogrammetric scanning. In this case study, we reconstruct the interior spaces of the historical house of Samuel Mikovíni, which represents these unfavorable conditions. The 3D reconstruction of interior spaces is performed using the Ricoh Theta Z1 spherical camera (Ricoh Company, Ltd.; Tokyo, Japan) in six variants, each employing a different number of images and different camera networks. Scale is introduced into the reconstructions based on significant dimensions measured with a measuring tape. A comparison is carried out using a point cloud obtained from terrestrial laser scanning and difference point clouds are generated for each variant. Based on the results, reconstructions produced from a reduced number of spherical images can serve as a basic source for simple documentation with accuracy up to 0.15 m. When the number of spherical images is increased and images from different height levels are included, the reconstruction accuracy improves markedly, achieving positional accuracy of up to 0.05 m, even in areas affected by poor lighting conditions or low-texture surfaces. The results confirm that for interior reconstruction, a higher number of images not only increases the density of the reconstructed point cloud but also enhances its positional accuracy.

Geomatics doi: 10.3390/geomatics6010001

Authors: Sandra Hernandez-Zetina Angel Martin-Furones Alvaro Verdu-Candela Carlos Martinez-Montes Ana Belen Anquela-Julian

Gender-based violence (GBV) in urban contexts is a complex, multifactorial phenomenon shaped by socioeconomic, territorial, and contextual factors. This study aims to develop a predictive model for GBV-related crimes in Valencia (Spain), using open geospatial data and advanced machine learning techniques to support the identification of high-risk areas and guide targeted interventions. A 25 m grid was generated to homogenize crime data and independent variables, including socioeconomic indicators, urban services, real estate information, and traffic intensity. Multiple models were tested—Multiple Linear Regression (MLR), Decision Tree (DT), and Random Forest (RF). Linear models were found to be insufficient for explaining GBV patterns (R2 ≈ 0.45), while RF and DT achieved high predictive accuracy (R2 ≈ 0.97 and 0.95, respectively. The variables with the greatest influence were traffic intensity, average monthly income, unemployment rate, and proximity to nightlife venues. To enhance the interpretability of the most accurate models, we applied SHAP (SHapley Additive exPlanations) to quantify the contribution of each predictor and elucidate the direction and magnitude of their effects on model predictions. These findings demonstrate the utility of geospatial ML techniques in understanding the spatial dynamics of GBV and in supporting urban safety policies. While the current model focuses on static spatial predictors and does not explicitly model temporal dynamics or spatial autocorrelation, future research will integrate these aspects, along with participatory data, and test the model’s applicability in other cities to enhance its robustness and generalizability.

Geomatics doi: 10.3390/geomatics5040081

Authors: Serge Andréfouët Oriane Bruyère Thomas Trophime

Precise lagoon bathymetry remains scarcely available for most tropical islands despite its importance for navigation, resource assessment, spatial planning, and numerical hydrodynamic modeling. Hydrodynamic models are increasingly used for instance to understand the ecological connectivity between marine populations of interest. Island remoteness and shallow waters complicate in situ bathymetric surveys, which are substantially costly. A multisource strategy using historical point sounding, multibeam surveys and well calibrated satellite-derived bathymetry (SDB) can offer the possibility to map entirely extensive and geomorphologically complex lagoons. The process is illustrated here for the rugose complex lagoon of Gambier Islands in French Polynesia. The targeted bathymetry product was designed to be used in priority for numerical larval dispersal modeling at 100 m spatial resolution. Spatial gaps in in situ data were filed with Sentinel-2 satellite images processed with the Iterative Multi-Band Ratio method that provided an accurate bathymetric model (1.42 m Mean Absolute Error in the 0–15 m depth range). Processing was optimized here, considering the specifications and the constraints related to the targeted hydrodynamic modeling application. In the near future, a similar product, possibly at higher spatial resolution, could improve spatial planning zoning scenarios and resource-restocking programs. For tropical island countries and for French Polynesia, in particular, the needs for lagoon hydrodynamic models remain high and solutions could benefit from such multisource coverage to fill the bathymetry gaps.

Geomatics doi: 10.3390/geomatics5040080

Authors: Jurawan Nontapon Neti Srihanu Niwat Bhumiphan Nopanom Kaewhanam Anongrit Kangrang Umesh Bhurtyal Niraj KC Siwa Kaewplang Alfredo Huete

The Northeast region of Thailand covers approximately 16.89 million hectares, with about 6.17 million hectares of seasonal rice cultivation and 2.85 million hectares affected by soil salinity—a major constraint to agricultural productivity in this region. This study develops an integrated data fusion framework combining multi-temporal Landsat-8 and Sentinel-2 imagery to train machine learning (ML) models for the prediction of rice yield and soil salinity, allowing for an analysis of their relationship. The field data comprised 380 rice yield and 625 soil electrical conductivity (EC) samples collected in 2023. Three ML models—Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Regression (SVR)—were applied for variable reduction and optimal predictor selection. RF achieved the highest accuracy for yield prediction (R2 = 0.86, RMSE = 0.19 t ha−1) and salinity estimation (R2 = 0.93, RMSE = 0.87 dS/m) when using fused Landsat–Sentinel data. Spatial analysis of 5000 matched points showed a strong negative relationship between seedling stage EC and yield (R2 = 0.71), with yields declining sharply above 5 dS/m and remaining below 1.5 t ha−1 beyond 15 dS/m. These results demonstrate the potential of multi-sensor fusion and ensemble ML approaches for precise soil salinity monitoring and sustainable rice production.

Geomatics doi: 10.3390/geomatics5040079

Authors: Nagham Amer Abdulateef Zainab N. Jasim Haider Ali Hasan Bashar Alsadik Yousif Hussein Khalaf

Accurate 3D reconstructions of architecture, engineering, and construction AEC structures using UAV photogrammetry are often hindered by occlusions, excessive image overlaps, or insufficient coverage, leading to inefficient flight paths and extended mission durations. This work presents a BIM-aware, autonomous UAV trajectory generation framework wherein a compact, geometrically valid viewpoint network is first derived as a foundation for path planning. The network is optimized via Integer Linear Programming (ILP) to ensure coverage of IFC-modeled components while penalizing poor stereo geometry, GSD, and triangulation uncertainty. The resulting minimal network is then sequenced into a global path using a TSP solver and partitioned into battery-feasible epochs for operation on active construction sites. Evaluated on two synthetic and one real-world case study, the method produces autonomous UAV trajectories that are 31–63% more compact in camera usage, 17–35% shorter in path length, and 28–50% faster in execution time, without compromising coverage or reconstruction quality. The proposed integration of BIM modeling, ILP optimization, TSP sequencing, and endurance-aware partitioning enables the framework for digital-twin updates and QA/QC monitoring, accordingly, offering a unified, geometry-adaptive solution for autonomous UAV inspection and remote sensing.

Geomatics doi: 10.3390/geomatics5040078

Authors: Ion Andronache Ana-Maria Ciobotaru

Monitoring land use and land cover (LULC) transformations is essential for understanding socio-ecological dynamics. This study assesses structural shifts in Romania’s landscapes between 1990 and 2018 by integrating algorithmic complexity, fractal analysis, and Grey-Level Co-occurrence Matrix (GLCM) texture analysis. Multi-year maps were used to compute Kolmogorov complexity, fractal measures, and 15 GLCM metrics. The measures were compiled into a unified matrix, and temporal trajectories were explored with principal component analysis and k-means clustering to identify inflection points. Informational complexity and Higuchi 2D decline over time, while homogeneity and angular second moment rise, indicating greater local uniformity. A structural transition around 2006 separates an early heterogeneous regime from a more ordered state; 2012 appears as a turning point when several indices reach extreme values. Strong correlations between fractal and texture measures imply that geometric and radiometric complexity co-evolve, whereas large-scale fractal dimensions remain nearly stable. The multi-index approach provides a replicable framework for identifying critical transitions in LULC. It can support landscape monitoring, and future work should integrate finer temporal data and socio-economic drivers.

Geomatics doi: 10.3390/geomatics5040077

Authors: Zdeněk Svatý Pavel Vrtal Tomáš Kohout Luboš Nouzovský Karel Kocián

This study examines the possibilities of using hybrid data collection methods based on photogrammetric and LiDAR imaging for documenting traffic accident sites. The evaluation was performed with an iPhone 15 Pro and a viDoc GNSS receiver. Comparative measurements were made against instruments with higher accuracy. The test scenarios included measuring errors along a 25 m line and scanning a larger traffic area. Measurements were conducted under limiting conditions on a homogeneous surface without terrain irregularities or objects. The results show that although hybrid scanning cannot fully replace traditional surveying instruments, it provides accurate results for documenting traffic accident sites. The analysis additionally revealed an almost linear spread of errors on homogeneous asphalt surfaces. Moreover, it was confirmed that the use of a GNSS receiver and control points has a significant impact on the quality of the data. Such a comprehensive assessment of surface homogeneity has not been tested yet. To achieve accuracy, it is recommended to use a scanning mode based on at least 90% image overlap with RTK GNSS. The relative error rate on a linear section ranged from 0.5 to 1.0%, which corresponds to an error of up to 5 cm over a 5 m section. When evaluating a larger area using hybrid data collection, 93.38% of the points had an error below 10 cm, with a mean deviation of 6.2 cm. These findings expand current knowledge and define practical device settings and operational limits for the use of hybrid mobile scanning.

Geomatics doi: 10.3390/geomatics5040076

Authors: Dalia A. Moreno-Egel Alfonso Arrieta-Pastrana Oscar E. Coronado-Hernández

This study presents a detailed analysis of the morphological evolution of beaches in the Bocagrande sector of Cartagena de Indias, Colombia, over more than a decade, based on periodic monitoring of six beach profiles. The beaches in this area are in bays constrained by headlands and promontories located at both ends of each bay. Changes in shoreline position, dry beach widths, and the surf zone were evaluated using aerial photographs, orthophotos, satellite imagery, and field data, together with sediment size determined through granulometric analysis. The results indicate that the beaches exhibit characteristics of wave-dominated, exposed systems, with sediments classified as fine sand that tend to increase in grain size toward the northern sector of the bay. A cyclical variation in the shoreline was observed, with average retreats and advances ranging from 5 to 10 m, depending on the climatic season. Dry beach widths ranged from 10 to 90 m, decreasing toward the north. Differences in morphology between profiles and shoreline variation are attributed to the climatic season, profile location within the bay, and proximity to a coastal structure and its particular type. Beach profiles were fitted to conceptual equilibrium profile models using traditional equations, which yielded a coefficient of determination of 0.76; when machine learning algorithms were applied, this value improved to 0.99. This study provides an important baseline for future morphological assessments and coastal management efforts in the city and places with similar characteristics, particularly considering ongoing shoreline protection projects.

Geomatics doi: 10.3390/geomatics5040075

Authors: Trias Aditya Adrian N. Pamungkas Faishal Ashaari Walter T. de Vries Calvin Wijaya Nicholas G. Setiawan

Synchronous interactions from different locations have become a globally accepted modus of interaction since the COVID-19 outbreak. For centuries, professional cadastral survey activities always required an interaction modus whereby surveyors, neighboring landowners, and local officers were present simultaneously. During the systematic adjudication and land registration project in Indonesia, multiple problems in the land information systems emerged, which, up to date, remain unsolved. These include the presence of plots of land without a related title, incorrect demarcations in the field, and the listing of titles without a connection to a land plot. We argue that these problems emerged due to ineffective survey workflows, which draw on inflexible process steps. This research assesses how and how much the use of augmented and virtual reality (AR/VR) technologies can make land registration services more effective and expand collaboration in a synchronous and at distant manner (the so-called same time, different place principle). The tested cadastral survey workflows include the procedure for a first land titling, the one for land subdivision, and the updating and maintenance of the cadastral database. These are common cases that could potentially benefit from integrated uses of augmented and virtual reality applications. Mixed reality technologies using VR glasses are also tested as tools, allowing individuals, surveyors, and government officers to work together synchronously from different places via a web mediation dashboard. The work aims at providing alternatives for safe interactions of field surveyors with decision-making groups in their endeavors to reach fast and effective collaborative decisions on boundaries.

Geomatics doi: 10.3390/geomatics5040074

Authors: Jing Miao Zhihao Wang Chenbin Liang Dong Yan Zhichao Wang

Rapid and accurate landslide detection is important for minimizing loss of life and property. Supervised machine learning has shown promise for automating landslide mapping, but it often requires thousands of labeled instances, which is impractical for timely emergency responses. Margin sampling active learning (MS) has proven effective for rapid landslide mapping by querying the most “informative” instances. However, it is still unclear how the choice of the landslide modeling algorithm influences the effectiveness of MS. This study assessed MS with four common landslide modeling algorithms, i.e., random forest, support vector machine, a generalized additive model, and an artificial neural network, using an open-source landslide inventory from Iburi, Japan. The results showed that all four combinations obtained > 0.90 the area under the ROC curve (AUROC) with 150 to 400 training instances. In particular, MS integrated with random forest performed best overall, with a mean AUROC of 0.91 and correct delineation of about 60 percent of the mapped landslide area using only 150 training instances. Precision-recall analysis within the ranked susceptibility maps showed that MS integrated with random forest and support vector machine generally outperformed the generalized additive model and artificial neural network. In addition, we developed a graphical user interface using R Shiny that integrates the MS active learning workflow with all four modeling options. Overall, these findings advance machine learning in rapid hazard mapping and provide tools to support decision-makers in emergency response.

Geomatics doi: 10.3390/geomatics5040073

Authors: Kornyliy Tretyak Denys Kukhtar

Classical instrumental technologies still remain important among the geodetic methods of dam monitoring, but periodic observations are often insufficient for timely detection of hazardous deformations. Therefore, the integration of continuous and remote sensing technologies into a multi-level system of observation improves the assessment of a structural condition. This research work evaluates the integrated approach that combines the GNSS data, robotic total station measurements, and satellite radar data processed by the PSInSAR technique for detecting the cyclic thermal deformations of the Dniester HPP concrete dam. The dataset includes 185 ascending and 184 descending Sentinel-1A SAR images (2019–2025, 12-day repeat cycle). PSInSAR processing was performed using StaMPS, with validation through comparison of InSAR-derived vertical displacements and GNSS data from the stationary monitoring system of the dam. The GNSS and InSAR time series have revealed consistent seasonal patterns and a common long-term trend. Harmonic components with amplitudes of 4–5 mm, peaking in late summer and declining in winter, confirm the dominant influence of thermal processes. In order to reduce noise, Fourier-based filtering and approximation were applied, thus ensuring balance between accuracy and data retention. The combined use of GNSS, robotic total station, and InSAR has increased the density of reliable control points and improved the thermal deformation model. Maximum vertical displacements of 6–13 mm were observed on the horizontal sections most exposed to solar radiation.

Geomatics doi: 10.3390/geomatics5040072

Authors: Tomeu Rigo Francesc Vilar-Bonet

Every year, thunderstorms initiating in the eastern Pyrenees cause a wide range of adverse phenomena, not only in the mountainous areas but also in the surrounding regions. Events such as heavy rainfall leading to flash floods, large or giant hail, and strong winds are common in this area. These phenomena cause significant damage and have major impacts on the population. We used remote sensing data, specifically weather radar, to identify areas that are more prone to convection initiation. This initial analysis covers the period from 2022 to 2024 and is intended to serve as the foundation for a more extensive study. The aim of this study is to characterize the diurnal convection cycle over the Pyrenees. Additionally, we plan to develop a technique that can be applied to other mountainous regions where similar data are available. The steps are as follows: (1) identifying events with precipitation over the area; (2) selecting cases associated with diurnal convection; (3) applying algorithms to determine the tracks of convective cells; and finally, (4) selecting the initial points of these trajectories. The result is a map highlighting these “hotspot” areas, which will allow us to incorporate other variables in the future, both meteorological and non-meteorological, to identify the main factors influencing the characteristics of each event.

Geomatics doi: 10.3390/geomatics5040071

Authors: Robert Tenzer Albertini Nsiah Ababio Ismael Foroughi Martin Pitoňák Pavel Novák Wenjin Chen Franck Eitel Kemgang Ghomsi

Orthometric heights are practically determined from levelling and gravity measurements by applying orthometric corrections to levelled height differences. Currently, Helmert’s definition of orthometric heights is mostly used, with the mean gravity computed only approximately from observed surface gravity by applying the Poincaré–Prey gravity reduction. In this study, we apply the state-of-the-art method for the orthometric height determination and demonstrate its practical applicability. The method utilizes advanced numerical procedures to account for the topographic relief and mass density variations, while adopting the Earth’s spherical approximation. The non-topographic contribution of masses inside the geoid is evaluated by solving geodetic boundary-values problems. We apply this method for the first time to practically determine the orthometric heights of levelling benchmarks from levelling and gravity measurements and digital terrain and rock density models. The results obtained after the readjustment of newly determined orthometric heights at the levelling network covering Hong Kong territories are compared with Helmert’s orthometric heights. This comparison revealed that errors in Helmert’s orthometric heights vary between −3.13 and 0.95 cm. Such errors are very significant when compared to accurate values of the cumulative orthometric correction between −1.88 and 0.84 cm. Moreover, large errors (up to 1 cm) already occur at levelling benchmarks at very low elevations (<100 m). These findings demonstrate that the accurate determination of orthometric heights is crucial, even for regions with moderately elevated topography.

Geomatics doi: 10.3390/geomatics5040070

Authors: Anuj Thapa Magar Dmitrii Krasnov Allar Padari Emílio Graciliano Ferreira Mercuri Steffen M. Noe

Eddy covariance measurements are increasingly utilized for assessing the exchange of matter and energy between ecosystems and the atmosphere across various time scales, ranging from hours to years. The flux footprint represents the area observable by flux tower sensors and illustrates how the surface influences the measured flux. Flux footprint models describe both the spatial extent and the specific location of the surface area contributing to the observed turbulent fluxes. In this study, we applied a simple two-dimensional parameterization for flux footprint prediction (FFP), developed by Kljun et al. to identify the location of peak footprint contribution every half hour over a six-year period. Monthly cluster analysis was performed on these data. Using an open-source geographic information system (GIS) software, the resulting clusters were overlaid on a base map of the site obtained from the Estonian Land Board, where different compartments have varying growth stages and species compositions. Our main objective was to integrate forest inventory data with ecosystem exchange and productivity data continuously recorded by the eddy covariance measurement tower at Järvselja, Estonia. This integration enabled spatially explicit visualization of half-hourly flux contributions using geographic information system software.

Geomatics doi: 10.3390/geomatics5040069

Authors: Moritz Hütten

Understanding past and present maritime activity patterns is critical for navigation safety, environmental assessment, and commercial operations. An increasing number of services now openly provide positioning data from the Automatic Identification System (AIS) via ground-based receivers. We show that coastal vessel activity can be reconstructed from open access data with high accuracy, even with limited data quality and incomplete receiver coverage. For three months of open AIS data in the Baltic Sea from August to October 2024, we present (i) cleansing and reconstruction methods to improve the data quality, and (ii) a journey model that converts AIS message data into vessel counts, traffic estimates, and spatially resolved vessel density at a resolution of ∼400 m. Vessel counts are provided, along with their uncertainties, for both moving and stationary activity. Vessel density maps also enable the identification of port locations, and we infer the most crowded and busiest coastal areas in the Baltic Sea. We find that on average, ≳4000 vessels simultaneously operate in the Baltic Sea, and more than 300 vessels enter or leave the area each day. Our results agree within 20% with previous studies relying on proprietary data.

Geomatics doi: 10.3390/geomatics5040068

Authors: Federica Gaspari Rebecca Fascia Federico Barbieri Oscar Roman Daniela Carrion Livio Pinto

In response to the increasing demand for effective bridge management and the shortcomings of current proprietary solutions, this work presents an open-source, web-based platform designed to support bridge inspection and data management, particularly for small and medium-sized public administrations, which often lack personnel or funding for implementing context-specific tools. The system addresses fragmented workflows by integrating multi-format geospatial and 3D data—such as point clouds, CAD/BIM models, and georeferenced imagery—within a unified, modular architecture. The platform enables structured inventory, interactive 2D/3D visualization, defect annotation, and role-based user interaction, aligning with FAIR principles and interoperability standards. Built entirely with free and open-source tools, the P.O.N.T.I. prototype ensures scalability, transparency, and adaptability. A multi-layer navigation interface guides users through asset exploration, inspection history, and immersive 3D viewers. Fully documented and publicly available on GitHub, the system allows for deployment across varying institutional contexts. The platform’s design anticipates future developments, including integration with IoT monitoring systems, AI-driven inspection tools, and chatbot interfaces for natural language querying. By overcoming existing proprietary limitations and providing access to a versatile single space, the proposed solution supports decision-makers in the digital transition towards a more accessible, transparent and integrated infrastructure asset management.

Geomatics doi: 10.3390/geomatics5040067

Authors: Anthony Romilio Paul C. Murphey Neffra A. Matthews Bruce A. Schumacher Lance D. Murphey Marcello Toscanini Parker Boyce Zach Fitzner

Drone photogrammetry and per-step spatial analysis were used to re-evaluate the West Gold Hill Dinosaur Tracksite (Bluff Sandstone, Colorado), which preserves an exceptionally long sauropod pes trackway. Building on earlier segment-based descriptions, we reconstructed the entire succession at millimetre-level resolution and quantified turning and gait asymmetry within an integrated digital workflow (UAV photogrammetry, Blender-based landmarking, scripted analysis). Of 134 footprints previously reported, 131 were confidently identified along a mapped path of 95.489 m that records 340° cumulative anticlockwise reorientation. Traditional end-point tortuosity (direct distance/trackway length; DL/TL) yields a moderate ratio of 0.462, whereas our incremental analysis isolates a fully looped subsection (tracks 38–83) with tortuosity of 0.0001 (DL 0.005 m; TL 34.825 m), revealing extreme local curvature that global (end-to-end) measures dilute. Gauge varies substantially along the trackway: the traditional metric (single pes width) averages 32.2% (wide gauge) with numerous medium-gauge representatives, while footprint-specific (‘incremental’) gauge spans 23.1–71.0% (narrow/medium/wide gauges observed within the same trackway). Our tests for asymmetry quantified that left-to-right paces and steps are longer (p = 0.001 and 0.008, respectively), central trackway width is greater (p = 0.043), and pace angulation is lower (p = 0.040) than right-to-left. Behaviourally, these signals are consistent with right-side load-avoidance but remain speculative (alternative explanations may include habitual laterality, local substrate heterogeneity). The study demonstrates how UAV-enabled, fully digital, sequential analyses can recover intra-trackway variability and enhance behavioural understanding of extinct trackmakers from fossil trackways.

Geomatics doi: 10.3390/geomatics5040066

Authors: Tatiana Alekseeva Vladimir Borodkin Evgeniya Pavlova Ekaterina Afanasyeva Julia Sokolova Vladislav Alekseev Pyotr Korobov Vasiliy Tikhonov Anastasia Ershova

Sea ice dirtiness is an important characteristic that is a marker of many processes occurring in sea ice cover throughout the period of ice formation. Data on dirty ice in the Arctic are scarce; the observations are spatially limited as they usually obtained during ship-based expeditions. There are also automated methods for dirty ice detection from satellite data. The paper presents, for the first time, maps of ice dirtiness in the East Siberian Sea based on four-class classification, drawn manually using satellite images in the visible range for the entire available period of Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000 to 2025. The spatial and temporal variability of dirty ice, as well as the conditions and causes of its formation, are studied. The study reveals that there are sea areas where the ice is always heavily dirty. At the same time, the area and location of dirty ice in the sea varies greatly from year to year. Our analysis of the interannual variability of dirty ice in the East Siberian Sea reveals an increase in dirty ice area, which is associated with the intensification of dynamic processes leading to ice contamination during its formation. The study finds that vast areas of dirty ice are formed immediately after strong wind-wave activity, which induces resuspension of sediments in the shallow water. The influx of ice from the Chukchi Sea also makes a significant contribution to the amount of dirty ice in the East Siberian Sea.

Geomatics doi: 10.3390/geomatics5040065

Authors: Paulo Roberto Mendes Pereira Édson Luis Bolfe Francisco Wendell Dias Costa Taíssa Caroline Silva Rodrigues Marcelino Silva Farias Filho Eduarda Vaz Braga

Land use and land cover (LULC) changes driven by agricultural and livestock expansion pose significant threats to the Brazilian savanna (Cerrado). This study aimed to analyze, map, and simulate LULC changes in the southern mesoregion of Maranhão State by generating geospatial scenarios projected through 2030. LULC changes between 2015 and 2020 were analyzed using Landsat images classified with the Random Forest machine learning algorithm. A spatial model based on cellular automata was employed to simulate land use and land cover scenarios for the year 2030. When comparing the simulated map with the reference map, an overall accuracy of 70.28% and a Kappa index of 0.608 were observed. Results revealed a decrease in native savanna and grassland areas, with a corresponding increase in agricultural and pasturelands, notably in municipalities such as Balsas, Riachão, Tasso Fragoso, Carolina and Porto Franco. The 2030 simulation predicts continued agricultural expansion and a potential reduction of approximately 19% in native Cerrado vegetation cover, highlighting municipalities of Campestre do Maranhão, Porto Franco, São João do Paraíso, Feira Nova, Estreito, Balsas, Tasso Fragoso and Carolina. These findings underscore the value of integrating remote sensing and spatial modeling techniques within the framework of Geomatics to support environmental monitoring and management of land-use dynamics, including expansion, contraction, diversification, and agricultural intensification. This approach provides critical insights into anthropogenic impacts on sensitive ecosystems, informing sustainable planning in tropical savanna regions.

Geomatics doi: 10.3390/geomatics5040064

Authors: Wilfred D. Calapini Fibor J. Tan Cris Edward F. Monjardin Jerome G. Gacu

Floods are among the most devastating natural hazards, causing widespread damage to lives, livelihoods, and infrastructure, particularly in vulnerable river basins. The Cagayan River Basin (CRB), the largest and most flood-prone basin in the Philippines, remains a significant challenge for disaster risk management. This study developed an event-based hydrologic–hydraulic modeling framework by coupling HEC-HMS rainfall–runoff simulations with HEC-RAS 2D unsteady flow routing to produce validated flood hazard maps. Inputs included rainfall from 41 gauge stations and observed inflows from the Magat Dam, processed in HEC-DSS. Validation utilized 137 surveyed flood marks collected from post-flood surveys, community reports, government archives, and household RTK measurements, with a concentration in Tuguegarao City. The coupled model reproduced key hydrograph peaks with moderate accuracy (R2 = 0.56, Bias = +0.32 m, RMSE = 1.61 m, MAE = 1.43 m), although NSE (−2.30) reflected the limits of daily rainfall inputs. Simulated hazard maps identified 767.97 km2 of inundated area (approximately 2.77% of CRB), concentrated along the floodplain and at the Magat confluence. Unlike previous scenario-based or localized efforts, this study delivers the first basin-wide, event-validated flood hazard maps for the CRB using integrated depth and depth–velocity criteria. The resulting hazard layers provide a scientific basis for strengthening evacuation planning, guiding land-use and infrastructure decisions, and supporting long-term resilience strategies in one of the Philippines’ most flood-prone rivers.

Geomatics doi: 10.3390/geomatics5040063

Authors: Saúl Dávila-Cisneros Ana G. Castañeda-Miranda Carlos Francisco Bautista-Capetillo Erick Dante Mattos-Villarroel Víktor Iván Rodríguez-Abdalá Cruz Octavio Robles Rovelo Laura Alejandra Pinedo-Torres Alejandro Rodríguez-Trejo Salvador Ibarra-Delgado

Mining activities are conducted to extract valuable minerals from the Earth, which are used to manufacture many objects. However, these operations generate landform alterations, such as deep excavations, artificial embankments, and landscape reshaping. In this study, landform changes were monitored in a mining area in Mazapil, Zacatecas, Mexico, using geomatic techniques. Multitemporal Landsat satellite images and digital elevation models (DEMs) from different years were used to detect and quantify landform alterations and estimate the volumes of removed material. The results show ground depressions greater than −333 m and waste material accumulations greater than +152 m, with an average standard deviation of ±3.6 m. A total excavation volume of 413.524 million m3 and a total fill volume of 431.194 million m3 were quantified, with an estimated standard deviation of ±810 m3. The proposed methodology proved effective for the remote quantification of large-scale relief disturbances in open-pit mining areas. It can also be used for environmental monitoring and hydrological risk assessment in active and inactive mining areas.

Geomatics doi: 10.3390/geomatics5040062

Authors: Rasool Vahid Mohamed H. Aly

Land use and land cover (LULC) changes significantly shape urban environments and directly impact ecological and socioeconomic systems. This study aims to explore these interconnections by employing the Cellular Automata–Markov (CA–Markov) model to assess and predict LULC dynamics in Arkansas. Historical LULC datasets from 2001 to 2021, obtained from the National Land Cover Database, were simplified from 11 into 5 classes to facilitate analysis and effectively map transitions. The model was validated by predicting LULC for 2016 and 2021 and comparing the predictions with the real maps, achieving an overall accuracy of approximately 91.9%, using model validation metrics, including precision, recall, F1-score, and Kappa Coefficient, and highlighting the strength of the predictions. Predictions for 2026 and 2031 reveal a continuous increase in built-up areas at the expense of vegetation cover, underscoring ongoing urbanization trends. Specifically, built-up areas are projected to increase from 28.39% in 2021 to 30.15% in 2031, while vegetation cover is expected to decline from 49.30% to 47.48%. This research demonstrates the utility of the CA–Markov model in simulating urban growth patterns and provides actionable insights into sustainable urban planning and land management strategies.

Geomatics doi: 10.3390/geomatics5040061

Authors: Dimitris Kaimaris

The study at the Sanctuary of Eukleia in Aigai (Vergina, Greece) evaluates the planimetric and vertical accuracy of Digital Surface Model (DSM) generated by a Hesai XT32M2X LiDAR system mounted on UAS WingtraOne GEN II. The paper begins by outlining the evolution of UAS-LiDAR, then describing the acquisition of RGB, multispectral (MS) images and LiDAR data. Twenty-two Check Points (CPs) were measured using an RTK-GNSS receiver, which also served to establish the PPK calibration base point. This is followed by processing the images to generate DSMs and orthophotomosaics, as well as processing the LiDAR point cloud to produce both DSM and DTM products. The DSMs and orthophotomosaics were evaluated by comparing field-measured CP coordinates with those extracted from the products, computing mean values and standard deviations. RGB images yielded DSMs and orthophotomosaics with planimetric accuracy of 1.4 cm (with a standard deviation σ = ±1 cm) in X, 0.9 cm (with σ = ±0.9 cm) in Y and a vertical accuracy of 2.4 cm (with σ = ±1.7 cm). The LiDAR-derived DSM achieved similar planimetric accuracy and an overall vertical accuracy of 7.5 cm (with σ = ±6 cm). LiDAR’s ability to penetrate vegetation enabled near-complete mapping of a densely vegetated streambank, highlighting its clear advantage over images. While high-precision RGB-PPK products can surpass LiDAR in vertical accuracy, UAS-LiDAR remains indispensable for under-canopy terrain mapping.

Geomatics doi: 10.3390/geomatics5040060

Authors: Amal Abdelsattar Ezz El-Din Hemdan

Predicting subsoil properties accurately is important for engineering tasks like construction, land development, and environmental management. However, traditional approaches that use borehole data often face challenges because the data is sparse and unevenly spread, which can cause uncertainty in understanding the subsurface. This study introduces a novel geostatistical framework employing Empirical Bayesian Kriging 3D (EBK3D) within a Geographic Information System (GIS), which was developed to construct three-dimensional lithological models. The framework was applied to 265 boreholes from the Queen Mary Reservoir in London. ArcGIS Pro was used to interpolate lithology layers using EBK3D, resulting in voxel-based models that represent both horizontal and vertical lithological variations. Model validation was performed with an independent dataset comprising 30% of the boreholes. The results demonstrated high predictive accuracy for layer elevations (Pearson’s r = 0.99, MAE = 0.31 m). The model achieved 100% accuracy in predicting borehole stratigraphy in homogenous zones and correctly identified 77% of lithological layers in heterogeneous zones. In complex regions, the model accurately predicted the whole borehole in 49% of cases. This framework provides a reliable, repeatable, and cost-effective method for three-dimensional subsurface characterization, enhancing traditional approaches by automating uncertainty quantification and capturing both vertical and horizontal variability.

Geomatics doi: 10.3390/geomatics5040059

Authors: Jing Fan Yusufujiang Meiliya Shunchuan Wu

The southwestern region of China is highly susceptible to landslides due to steep terrain, fractured geology, and intense rainfall. This study focuses on the Xiluodu Reservoir area in Yunnan Province and applies Geographic Information System (GIS) techniques together with ten key spatial factors—such as slope, lithology, elevation, and distance to rivers—to perform a quantitative landslide risk assessment. In addition to the individual Certainty Factor (CF) and Logistic Regression (LR) models, we developed an integrated CF–LR coupled model to overcome their respective limitations: the CF model’s sensitivity to specific factor attributes but neglect of factor interactions, and the LR model’s robust weight estimation but weak representation of attribute heterogeneity. By combining these strengths, the CF–LR model achieved superior predictive performance (AUC = 0.804), successfully capturing 92.5% of historical landslide events within moderate-to-high risk zones. The results show that lithology, slope angle, and proximity to rivers and roads are dominant controls on susceptibility, with landslides concentrated on soft rock slopes of 30–40° and within 600–900 m of rivers. Compared with previous coupled approaches in similar mountainous reservoir settings, our CF–LR model provides a more balanced and interpretable framework, enhancing both classification accuracy and practical applicability. These findings demonstrate that GIS-based CF–LR integration is a novel and reliable tool for landslide susceptibility mapping, offering important technical support for disaster prevention and risk management in large reservoir regions.

Geomatics doi: 10.3390/geomatics5040058

Authors: Tuğrul Urfalı Abdurrahman Eymen

This study proposes an integrated, data-driven framework that couples Geographic Information Systems (GIS) with machine-learning techniques to improve fire-department response efficiency in an urban setting. Using an initial archive of 10,421 geocoded fire incident reports collected in Kayseri, Turkey (2018–2023), together with an OpenStreetMap-derived road network, we first generated an “ideal route-time” feature for every incident via Dijkstra shortest-path analysis. After data cleaning and routability checks, 7421 high-quality cases formed the modelling base. Two regression models—eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR)—were trained to predict dispatch-to-arrival times. On the held-out test set, XGBoost yielded the best performance, achieving a mean absolute error of 1.67 min, a root-mean-square error of 2.21 min, a coefficient of determination (R2) of 0.46, and 78.41% accuracy within a ±3 min tolerance. Predicted times were combined with real-time Dijkstra routing to visualize fastest paths and station service areas in GIS, revealing that densely populated districts are reachable within five minutes while peripheral zones exceed ten. The results demonstrate that embedding network-derived features within advanced ML models markedly improves temporal forecasts and that the combined GIS-ML framework can support rapid, evidence-based decision-making, ultimately helping to minimize loss of life and property in urban fire emergencies.

Geomatics doi: 10.3390/geomatics5040057

Authors: Felivalentín Lamas-Torres Joel Artemio Morales Viscaya Leonardo Tenorio-Fernández Rafael Cervantes-Duarte

Accurate bathymetric information is crucial for coastal management, navigation, and ecosystem monitoring, yet conventional hydrographic surveys are costly and logistically demanding. This study introduces a two-stage semiempirical model for satellite-derived bathymetry (SDB) based on log-ratio reflectance indices from atmospherically corrected Landsat 8 imagery. The approach combines the optical sensitivity of the green/blue band ratio and the attenuation properties of the red/blue ratio within a parametric regression framework, enhancing both stability and interpretability. The methodology was evaluated in two contrasting coastal environments: the turbid Magdalena-Almejas Lagoon System (Mexico) and the clear-water coral reef setting of Buck Island (U.S. Virgin Islands). Results demonstrated that the proposed model outperformed traditional semiempirical approaches (Lyzenga, Stumpf, Hashim), achieving R2=0.8155 (RMSE = 1.16 m) in Magdalena-Almejas and R2=0.9157 (RMSE = 1.38 m) in Buck Island. Performance was statistically superior to benchmark methods according to cross-validated confidence intervals and was comparable to an artificial neural network, while avoiding overfitting in data-scarce environments. These findings highlight the model’s suitability as a transparent, cost-efficient, and scalable alternative for SDB, particularly valuable in regions where in situ data are limited.

Geomatics doi: 10.3390/geomatics5040056

Authors: Mohamed Abdelazeem Hussain A. Kamal Amgad Abazeed Mudathir O. A. Mohamed

Precise kinematic positioning using low-cost android smartphones remains a significant research focus, particularly with the growing integration of Global Navigation Satellite System (GNSS) capabilities in these devices. This research explores the accuracy of the single-frequency quad-constellation carrier-phase-based real-time kinematic (RTK) and code-only relative positioning (RP) techniques using Xiaomi 11T smartphone for transportation applications. Kinematic GNSS measurements from Xiaomi 11T are acquired using vehicle trajectory in New Aswan City, Egypt; then, the acquired data are processed utilizing various constellation combinations scenarios including GPS-only, GPS/Galileo, GPS/GLONASS, GPS/BeiDou, and GPS/Galileo/GLONASS/BeiDou. The processing outputs demonstrate that sub-meter and meter-level horizontal position accuracy is achieved for both scenarios using RTK and RP, respectively. The quad-constellation processing scenario has superiority with 0.456 m and 1.541 m root mean square error (RMSE) values in the horizontal component involving RTK and RP, respectively; on the other hand, the GPS-only solution achieved 0.766 m and 1.703 m horizontal RMSE values using RTK and RP, respectively. Based on the attained accuracy, the cost-effective Xiaomi 11T provides sufficient positioning accuracy to support transportation applications such as an intelligent transportation system, urban/public transportation monitoring, fleet management, vehicle tracking, and mobility analysis, aiding smart city planning and transportation system optimization.

Geomatics doi: 10.3390/geomatics5040055

Authors: Assel Satbergenova Dinara Talgarbayeva Andrey Vilayev Asset Urazaliyev Alena Yelisseyeva Azamat Kaldybayev Semen Gavruk

The objective of this study is to quantify and characterize ground deformations induced by underground coal mining in the Karaganda coal basin, Kazakhstan, in order to improve the understanding of subsidence processes and their long-term evolution. The SBAS-InSAR method was applied to Sentinel-1 (C-band) and TerraSAR-X (X-band) data from 2019–2021 to estimate the magnitude, extent, and temporal behavior of displacements over the Kostenko, Kuzembayev, Aktasskaya, and Saranskaya mines. The results reveal spatially coherent and progressive deformation, with maximum cumulative LOS displacements exceeding –800 mm in TerraSAR-X data within active longwall mining zones. Time-series analysis confirmed acceleration of displacement during active extraction and its subsequent attenuation after mining ceased. Comparative assessment demonstrated a strong agreement between Sentinel-1 and TerraSAR-X results (r = 0.9628), despite differences in resolution and acquisition geometry, highlighting the robustness of the SBAS-InSAR approach. Analysis of displacement over individual longwalls showed that several panels (3, 5, 8, 15, and 18) already exceeded their projected maximum subsidence values, underlining the necessity of continuous monitoring for ensuring safety. In contrast, other longwalls have not yet reached their maximum deformation, indicating potential for further activity. Overall, this study demonstrates the value of multi-sensor InSAR monitoring for reliable assessment of mining-induced subsidence and for supporting geotechnical risk management in post-industrial regions.

Geomatics doi: 10.3390/geomatics5040054

Authors: Zofia Kuzevicova Stefan Kuzevic Diana Bobikova

The present study examines the possibilities for developing the use of small hydropower plants (SHP) in Slovakia, focusing on the principles of sustainability and compliance with European and national legislation. At present, there is a tendency for the construction of hydroelectric power plants to intervene in the river environment, with the potential to exert a substantial impact on the flow of the river and disrupt the surrounding ecosystem. A potential strategy for minimizing environmental impact would be the construction of SHPs, which require less construction work. The Hornád river sub-basin, located in eastern Slovakia, was selected as the study area. The spatial and hydrological data were processed using Geographic Information System (GIS) tools. The hydrological characteristics of the area were determined through the utilization of a digital terrain model (DMR 5.0). The results of the hydrological analyses were then combined with environmental constraints to identify suitable locations for small hydropower plants. The theoretical and technical potential and gradient were calculated for individual sections of watercourses. It is estimated that approximately 61% of watercourse sections have a gradient greater than or equal to 10 m, which represents suitable conditions for the development of small hydropower plants. The presence of a stable flow regime engenders optimal conditions for the utilization of hydropower in the designated location. The study emphasizes the importance of environmental protection of the area, the resolution of property rights issues, and the streamlining of permitting processes. The results of the study contribute to energy planning at the regional level and confirm the effectiveness of using GIS in determining locations for small hydropower plants. Concurrently, emphasis is placed on the necessity to incorporate environmental and legislative imperatives within the overarching strategy for water energy development.

Geomatics doi: 10.3390/geomatics5040053

Authors: Tamás Tari Kornél Czimber Sándor Faragó Gábor Heffenträger Sándor Kalmár Gyula Kovács Gyula Sándor András Náhlik

To achieve professional roe deer population management and to mitigate wildlife-related agricultural damage, a wildlife population estimation trial was conducted in Hungary using an ultralight aircraft with dual sensors (thermal and DSLR camera) to assess the method’s applicability, using the roe deer as a model species. The test took place in early spring, at an altitude of 400 m above ground level and a flight speed of 150 km/h. The survey targeted a total count of a 1040 hectare area using adjacent 200 m-wide strips. This strip-based design also allowed for a methodological comparison between total count and strip sample count approaches. Object-based image classification was applied, and species-level validation was performed. During the survey, a total of 213 roe deer were localised. The average group size was 9.17 ± 1.7 (x¯ ± SE), with two prominent outliers (28 and 34 individuals). Compared to the density value of 0.205 individuals/ha established through the full-area census, the simulated estimations (50% and 25%) showed considerable under- and overestimation, primarily due to the aggregative behaviour of roe deer. Based on the test, aerial population estimation using dual-sensor technology proved to be effective in agricultural habitats; however, the accuracy of the results is strongly influenced by the sampling design applied.

Geomatics doi: 10.3390/geomatics5040052

Authors: Jürgen Landauer Sarah Klassen

We investigate the applicability of visual foundation models, a recent advancement in artificial intelligence, for archaeological remote sensing. In contrast to earlier approaches, we employ a strictly zero-shot methodology, testing the hypothesis that such models can perform archaeological feature detection without any fine-tuning or other adaptation for the remote sensing domain. Across five experiments using satellite imagery, aerial LiDAR, and drone video data, we assess the models’ ability to detect archaeological features. Our results demonstrate that such foundation models can achieve detection performance comparable to that of human experts and established automated methods. A key advantage lies in the substantial reduction of required human effort and the elimination of the need for training data. To support reproducibility and future experimentation, we provide open-source scripts and datasets and suggest a novel workflow for remote sensing projects. If current trends persist, foundation models may offer a scalable and accessible alternative to conventional archaeological prospection.

Geomatics doi: 10.3390/geomatics5040051

Authors: Mohamed Mastere Ayyoub Sbihi Anas El Ouali Sanae Bekkali Oussama Arab Danielle Nel Sanders Benyounes Taj Ibrahim Ouchen Noamen Rebai Ali Bounab

The Rif Mountains in northern Morocco are highly exposed to geohazards, particularly earthquakes and mass movements. In this context, the Zoumi region is most affected, showing various mass movement types involving both unconsolidated and solid materials. This study evaluates the region’s susceptibility to mass movements using logistic regression (LR), applied for the first time in this area. The model incorporates eight key predisposing factors known to influence mass movement: slope gradient, slope aspect, land use, drainage density, elevation, lithology, fracturing density, and earthquake isodepths. Historical mass movements were mapped using remote sensing and field surveys, and statistical analysis calculation was conducted to analyze their spatial correlation with these environmental conditioning factors. A mass movement susceptibility (MMS) map was produced, classifying the region into four susceptibility levels, ranging from low to very high. Landslides were the most frequent movement type (36%). The LR model showed strong predictive performance, with an AUC of 88%, confirming its robustness. The final map reveals that 42% of the Zoumi area falls within the high to very high susceptibility zones. These results highlight the importance of using advanced modeling approaches to support risk mitigation and land use planning in environmentally sensitive mountain regions.

Geomatics doi: 10.3390/geomatics5040050

Authors: Mohsen Niroomand Parham Pahlavani Behnaz Bigdeli Omid Ghorbanzadeh

Land Surface Temperature (LST) is a critical parameter for analyzing urban heat islands, surface–atmosphere interactions, and environmental management. This study enhances the temporal resolution of LST data by leveraging machine learning and deep learning models. A novel methodology was developed using Landsat 8 thermal data and Sentinel-2 multispectral imagery to predict LST at finer temporal intervals in an urban setting. Although Sentinel-2 lacks a thermal band, its high-resolution multispectral data, when integrated with Landsat 8 thermal observations, provide valuable complementary information for LST estimation. Several models were employed for LST prediction, including Random Forest Regression (RFR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Gated Recurrent Unit (GRU). Model performance was assessed using the coefficient of determination (R2) and Mean Absolute Error (MAE). The CNN model demonstrated the highest predictive capability, achieving an R2 of 74.81% and an MAE of 1.588 °C. Feature importance analysis highlighted the role of spectral bands, spectral indices, topographic parameters, and land cover data in capturing the dynamic complexity of LST variations and directional patterns. A refined CNN model, trained with the features exhibiting the highest correlation with the reference LST, achieved an improved R2 of 84.48% and an MAE of 1.19 °C. These results underscore the importance of a comprehensive analysis of the factors influencing LST, as well as the need to consider the specific characteristics of the study area. Additionally, a modified TsHARP approach was applied to enhance spatial resolution, though its accuracy remained lower than that of the CNN model. The study was conducted in Tehran, a rapidly urbanizing metropolis facing rising temperatures, heavy traffic congestion, rapid horizontal expansion, and low energy efficiency. The findings contribute to urban environmental management by providing high-temporal-resolution LST data, essential for mitigating urban heat islands and improving climate resilience.

Geomatics doi: 10.3390/geomatics5040049

Authors: Ana Clara de Lara Maia André Luiz dos Santos Monte Ayres Cristhy Satie Kanai Jamille da Silva Ferreira Miguel Reis Fontes Nathalia Moraes Desani Yasmim Carvalho Guimarães Cheila Flávia de Praga Baião José Roberto Mantovani Tulius Dias Nery Jose A. Marengo Enner Alcântara

Landslides are a persistent and destructive hazard in Angra dos Reis, located in the highlands of Rio de Janeiro State, southeastern Brazil, where steep slopes, intense orographic rainfall, and unregulated urban expansion converge to trigger recurrent mass movements. In this study, we applied Multiscale Geographically Weighted Regression (MGWR) to examine the spatially varying relationships between landslide occurrence and topographic, hydrological, geological, and anthropogenic factors. A detailed inventory of 319 landslides was compiled using high-resolution PlanetScope imagery after the December 2023 rainfall event. Following multicollinearity testing and variable selection, thirteen predictors were retained, including slope, rainfall, lithology, NDVI, forest loss, and distance to roads. The MGWR achieved strong performance (R2 = 0.94; AICc = 134.99; AUC = 0.99) and demonstrated that each factor operates at a distinct spatial scale. Slope, rainfall, and lithology exerted broad-scale controls, while road proximity had a consistent global effect. In contrast, forest loss and land use showed localized significance. These findings indicate that landslide susceptibility in Angra dos Reis is primarily driven by the interaction of orographic rainfall, steep terrain, and geological substrate, intensified by human disturbances such as road infrastructure and vegetation removal. The study underscores the need for targeted adaptation strategies, including slope stabilization, restrictions on road expansion, and vegetation conservation in steep, rainfall-prone sectors.

Geomatics doi: 10.3390/geomatics5030048

Authors: Rosendo Romero-Andrade Karan Nayak Rafaela Mirasol Llanes-Hernández Norberto Alcántar-Elizondo Tiojari Dagoberto Guzmán-Galindo Yedid Guadalupe Zambrano-Medina

A quality assessment of Global Navigation Satellite System (GNSS) observations was conducted for 95 Continuously Operating Reference Stations (CORSs) across Mexico over the period 2020–2023 using the ANUBIS software package. The evaluation was carried out according to International GNSS Service (IGS) quality indicators, including the data utilization ratio (R), multipath effect (MP), cycle slips (CSR), and signal-to-noise ratio (SNR). Stations belonging to the National Active Geodetic Network (RGNA), the government-managed geodetic network, exhibited the highest observation quality, with most meeting IGS thresholds for MP, CSR, and SNR. Nevertheless, none of the RGNA stations reached the recommended 95% threshold for data utilization ratio. In contrast, CORS-NOAA and EarthScope stations operating in Mexico generally failed to satisfy IGS standards, although acceptable SNR values were observed at some sites. Upgrades to multi-constellation receivers (GPS, GLONASS, GALILEO) did not consistently improve data quality. These findings highlight the role of processing software and configuration choices in GNSS data quality assessments and emphasize the importance of continued modernization of geodetic infrastructure in Mexico.

Geomatics doi: 10.3390/geomatics5030047

Authors: Debra Laefer Jingru Hua

Remote sensing underpins significant twenty-first century technical capabilities and innovations. Thus, understanding the technical expertise and financial drivers of the field is of national and international importance, as they are inextricably linked with intellectual property generation. Using 126,479 peer-reviewed journal papers and their affiliated funder information from two major publication databases, this study benchmarks current practices, documents historical shifts, and identifies emerging directions in the remote sensing industry and academic publishing. In 70 years, the field has moved from producing only a dozen scholarly papers a year to more than 13,000 annually, without equivalent growth in publication venues but with a rise in the mean number of authors from three to five in less than 25 years. The largest contributor (research and funding) is China, which has rapidly ascended since 2000 to now dominate the field with a near-majority stake. China’s dominance, representing 47% of all remote sensing journal papers worldwide, is mirrored in affiliated patents.

Geomatics doi: 10.3390/geomatics5030046

Authors: Abdulkadir Ozturk Muhammed Enes Atik Mehmet Melih Koşucu Saziye Ozge Atik

Floods are among the most destructive natural disasters and can devastate human life, infrastructure, and mobility in urban areas. It is necessary to develop a simulation model suitable for disaster management to prepare for flooding and facilitate rapid response interventions. The advantage of a three-dimensional (3D) geographic information system (GIS) is that it allows researchers to perform more successful spatial analyses than traditional two-dimensional (2D) systems. In this study, real-time 3D flood simulations were created for the Bozkurt district of Kastamonu, Türkiye, integrating GIS and game engine technologies. Land use land cover (LU/LC) map, digital elevation model (DEM), soil properties and climate data of the study region constitute the input data for the hydrological model. DEM and building footprints are also used to create 3D models of the buildings in the region. Through the Soil and Water Assessment Tool (SWAT) analysis, a hydrological model that included environmental factors such as precipitation, runoff, and soil erosion was created. The average flow rate for the same period, obtained from flow monitoring stations in the Bozkurt district, was 4.64 m3/s, while the flow rate obtained with the SWAT+ model was 4.12 m3/s. Using the flow parameters obtained with SWAT, 3D flood models were developed on Unreal Engine (UE). The flood simulation created with UE and the flood disaster experienced in 2021 in the region were compared on an area basis. The obtained simulation accuracy was 88%.

Geomatics doi: 10.3390/geomatics5030045

Authors: Quinoza Guvil Dudy Darmawan Wijaya Brian Bramanto Kosasih Prijatna Irwan Meilano Cheinway Hwang Rahayu Lestari Arisauna Maulidyan Pahlevi Bagas Triarahmadhana Raa Ina Sidrotul Muntaha Agustina Nur Syafarianty Muhamad Irfan

This study evaluates the impact of incorporating lateral mass density variations into geoid models for Java, Indonesia, aiming to enhance the accuracy of regional geoid determinations. Geoid models have traditionally used a constant density assumption; however, Java’s varied topography and geological complexity suggest that density variability may significantly influence geoid accuracy. Employing the Stokes–Helmert method combined with the remove–compute–restore (RCR) technique, we calculated geoid models using both constant density and laterally variable density from the UNB TopoDens model. The models were validated against GNSS/leveling data, showing that while lateral density variations had limited effects along flat topographic profiles, they introduced notable discrepancies in regions with considerable elevation changes. Specifically, variable density models exhibited discrepancies of up to 30 cm in regions with complex terrain, underscoring the importance of selecting appropriate density models for precise geoid computations in heterogeneous landscapes. Nonetheless, a comprehensive validation using geometric geoid models is required to confirm the accuracy improvements across the entire region.

Geomatics doi: 10.3390/geomatics5030044

Authors: Mehmetcan Soyluoğlu Rahaf Orabi Sorin Hermon Nikolas Bakirtzis

Documenting and preserving cultural heritage assets is increasingly important, with threats from natural disasters, conflicts, climate change, and neglect, and some sites are both contested and physically difficult to access or document, posing the issue of “challenging heritage”. A range of innovative digital methods have emerged, offering practical, low-cost, efficient techniques for the 3D documentation of threatened heritage, including smart phone-based mobile light detection and ranging (LiDAR) and photogrammetry. Such techniques offer quick, accessible, and cost-effective alternatives to terrestrial laser scanners, albeit with reduced accuracy and detail, offering practical solutions in cases with restricted funding, limited time for access, complex architectural geometries, or the unavailability of high-end equipment on site. This paper presents a real-world case study integrating iPhone LiDAR with aerial photogrammetry for the rapid documentation of Sourp Magar Monastery, a Medieval site located in a forested slopes of the Kyrenia Range, Cyprus. Due to its poor state of preservation and years of abandonment, as well as its remote nature and location, the monastery is considered a “challenging heritage” monument. In the context of a recent international restoration initiative, a preliminary digital survey was undertaken to both document the current condition of Sourp Magar and contribute to a better understanding of its construction history. This paper outlines the workflow integrating the use of smartphone LiDAR and aerial photogrammetry, evaluates its efficacy in challenging heritage sites, and discusses its potential implications for rapid, low-cost documentation. Finally, the present paper aims to show the multifaceted benefit of easy-to-use, low-cost technologies in the preliminary study of sites and monuments.

Geomatics doi: 10.3390/geomatics5030043

Authors: Carmine Fusaro Yohanna Sarria-Guzmán Francisco Erik González-Jiménez Manuel Saba Oscar E. Coronado-Hernández Carlos Castrillón-Ortíz

Accurate soil organic carbon (SOC) estimation is critical for assessing ecosystem services, carbon budgets, and informing sustainable land management, particularly in ecologically sensitive mountainous regions. This study focuses on modelling the spatial distribution of SOC within the heterogeneous volcanic landscape of the Nevado de Toluca (NdT), central Mexico, an area spanning 535.9 km2 and characterised by diverse land uses, altitudinal gradients, and climatic regimes. Using 29 machine learning algorithms, we evaluated the predictive capacity of three key variables: land use, elevation, and the Normalised Difference Vegetation Index (NDVI) derived from satellite imagery. Complementary analyses were performed using the Bare Soil Index (BSI) and the Modified Soil-Adjusted Vegetation Index 2 (MSAVI2) to assess their relative performance. Among the tested models, the Quadratic Support Vector Machine (SVM) using NDVI, elevation, and land use emerged as the top-performing model, achieving a coefficient of determination (R2) of 0.84, indicating excellent predictive accuracy. Notably, 14 models surpassed the R2 threshold of 0.80 when using NDVI and BSI as predictor variables, whereas MSAVI2-based models consistently underperformed (R2 < 0.78). Validation plots demonstrated strong agreement between observed and predicted SOC values, confirming the robustness of the best-performing models. This research highlights the effectiveness of integrating multispectral remote sensing indices with advanced machine learning frameworks for SOC estimation in mountainous volcanic ecosystems

Geomatics doi: 10.3390/geomatics5030042

Authors: Hassan Qasim Xiaoli Ding Muhammad Usman Sawaid Abbas Naeem Shahzad Hatem M. Keshk Muhammad Bilal Usman Ahmad

Accurate classification of tree species is crucial for forest management and biodiversity conservation. Remote sensing technology offers a unique capability for classifying and mapping trees across large areas; however, the accuracy of extracting and identifying individual trees remains challenging due to the limitations of available imagery and phenological variations. This study presents a novel integrated machine learning (ML) and Geographic Object-Based Image Analysis (GEOBIA) framework to enhance tree species classification in a botanical garden using multi-temporal unmanned aerial vehicle (UAV) imagery. High-resolution UAV imagery (2.3 cm/pixel) was acquired across four different seasons (summer, autumn, winter, and early spring) to incorporate the phenological changes. Spectral, textural, geometrical, and canopy height features were extracted using GEOBIA and then evaluated with four ML models (Random Forest (RF), Extra Trees (ET), eXtreme gradient boost (XGBoost), and Support Vector Machine (SVM)). Multi-temporal data significantly outperformed single-date imagery, with RF achieving the highest overall accuracy (86%, F1-score 0.85, kappa 0.83) compared to 57–75% for single-date classifications. Canopy height and textural features were dominant for species identification, indicating the importance of structural variations. Despite the limitations of moderate sample size and a controlled botanical garden setting, this approach offers a robust framework for forest and urban landscape managers as well as remote sensing professionals, by optimizing UAV-based strategies for precise tree species identification and mapping to support urban and natural forest conservation.

Geomatics doi: 10.3390/geomatics5030041

Authors: Javier Giovanni Álvarez-Herrera Marilcen Jaime-Guerrero Carlos Julio Fernández-Pérez

Agricultural productivity is closely linked to the spatial variability of soil physical properties. However, high variability makes it difficult to implement effective management strategies, and the constant expansion of eucalyptus plantations in certain areas alters the soil’s physical properties. This study conducted a geostatistical analysis of the physical properties of a soil in Sogamoso, Boyacá (Colombia), which contains areas with different management practices and vegetation cover, among which the presence of Eucalyptus globulus stands out. Ninety-seven points were sampled in an area of 29.1 ha, with multiple land uses. The data were analyzed using descriptive statistics and geostatistical analysis, which determined the semivariogram parameters, the degree of spatial dependence, and the best-fitting interpolation model for mapping. A correlation analysis between variables was also performed. Analysis of variance showed no significant differences among vegetation covers (dense forest, grass-crop mosaic, weedy grassland, and crop mosaic), indicating structural homogeneity. The hydraulic conductivity (Ksat) had the highest coefficient of variation (CV), at 141.9%, while particle density had the lowest CV, at 9.25%. Ksat (exponential model, range = 207 m) and porosity (spherical model, range = 98 m) showed a strong spatial dependence. Ksat was lower in areas with eucalyptus (0.01 to 0.2 m day−1), attributed to hydrophobicity induced by organic compounds emitted by these plantations. Soil moisture contents showed lower values in areas with eucalyptus, corroborating their high water consumption. Soil aggregates were lower when eucalyptus plantations were on slopes greater than 15%. Porosity showed an inverse correlation with apparent density (r2 = −0.86).

Geomatics doi: 10.3390/geomatics5030040

Authors: Ilyass-Essaid Lerhris Hassan Admou Hassan Ibouh Noureddine El Binna

Multispectral remote sensing offers powerful capabilities for mineral exploration, particularly in regions with complex geological settings. This study investigates the mineralization potential of the Tidili region in Morocco, located between the South Atlasic and Anti-Atlas Major Faults, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery to extract hydrothermal alteration zones. Key techniques include band ratio analysis and Principal Components Analysis (PCA), supported by the Crósta method, to identify spectral anomalies associated with alteration minerals such as Alunite, Kaolinite, and Illite. To validate the remote sensing results, field-based geological mapping and mineralogical analysis using X-ray diffraction (XRD) were conducted. The integration of satellite data with ground-truth and laboratory results confirmed the presence of argillic and phyllic alteration patterns consistent with porphyry-style mineralization. This integrated approach reveals spatial correlations between alteration zones and structural features linked to Pan-African and Hercynian deformation events. The findings demonstrate the effectiveness of combining multispectral remote sensing images analysis with field validation to improve mineral targeting, and the proposed methodology provides a transferable framework for exploration in similar tectonic environments.

Geomatics doi: 10.3390/geomatics5030039

Authors: Željko Bačić Danijel Šugar Zvonimir Nevistić

Global navigation satellite systems (GNSSs) are an integral part of modern society and are used in various industries, providing users with positioning, navigation, and timing (PNT). However, their effectiveness is vulnerable to signal interference, since GNSSs are based on received satellite signals from space, and that can severely impact applications that rely on continuous and accurate data. Interference can pose significant risks to sectors dependent on GNSSs, including transportation, telecommunications, finance, geodesy, and others. For this reason, in parallel with the development of GNSSs, various interference protection techniques are being developed to enable users to receive GNSS signals without the risk of interference, which can cause various effects, such as reducing the accuracy of positioning, as well as completely blocking signal reception and making it impossible to obtain positioning. There are various sources and methods of interfering with GNSS signals, and the greatest consequences are caused by intentional interference, which includes jamming, spoofing, and meaconing. This study investigates the effects of jamming devices on static GNSS observations using high-accuracy devices through multiple controlled experiments using both single-frequency (SF) and multi-frequency (MF) jammers. The aim was to identify the distances within which signal interference devices disrupt GNSS signal reception and position accuracy. The research conducted herein was divided into several phases where zones within which the jammer completely blocked the reception of the GNSS signal were determined (blackout zones), as were zones within which it was possible to obtain the position (but the influence of the jammer was present) and the influence of the jammer from different directions/azimuths in relation to the GNSS receiver. Various statistical indicators of the jammer’s influence, such as DOP (dilution of precision), SNR (signal-to-noise-ratio), RMS (root mean square), and others, were obtained through research. The results of this study indicate that commercially available, low-cost jamming devices, when operated within manufacturer-specified distances, completely disrupt the reception of GNSS signals. Their impact is also evident at greater distances, where they significantly reduce SNR values, increase DOP, and decrease the number of visible satellites, leading to reduced measurement reliability and integrity. These results underline the necessity of developing effective protection mechanisms against GNSS interference and strategies to ensure reliable signal reception in GNSS-dependent applications, particularly as the use of jamming devices becomes more prevalent.

Geomatics doi: 10.3390/geomatics5030038

Authors: Wladyslaw Koc

The aim of the research presented in the article is to develop a method for modifying compound curves, i.e., geometric systems composed of two (or more) circular arcs with different radii, directed in the same direction and directly connected to each other. These curves are used when connecting two directions of the railway route where one circular arc is impossible due to permanent terrain obstacles. To solve the problem, an analytical method of designing track geometric systems was used, in which individual elements of these systems are described using mathematical equations. The modification itself involves introducing appropriate transition curves between the connecting arcs. Three possibilities for such a connection were presented, resulting from the method of considering conditions related to horizontal curvature of the track axis. A comparative analysis of the obtained solutions was conducted using the developed geometric test system. The analysis was based on the curvature values determined for the considered transition curves, after assuming varying lengths of these curves. For the recommended solution to the problem, it was necessary to verify the practical feasibility of horizontal ordinate values, which could not be too small relative to the implementation error. As stated, to limit the effects of this error, the transition curve lengths should be adjusted to specific geometric situations and excessively short curves should be avoided. As a result of the conducted research, the transition curve determined with strict curvature conditions was determined to be the most advantageous. It maintains curvature continuity along its entire length, there are no abrupt changes in curvature at the edges, and the changes in curvature along the length are much smoother than in the other curves considered. Therefore, this curve should be recommended for practical use.

Geomatics doi: 10.3390/geomatics5030037

Authors: Zahid Ahmad Dar Saurabh Kumar Gupta Shruti Kanga Suraj Kumar Singh Gowhar Meraj Pankaj Kumar Bhartendu Sajan Bojan Đurin Nikola Kranjčić Dragana Dogančić

Heavy rainfall events, characterized by extreme downpours that exceed 100 mm per day, pose an intensifying hazard to the densely settled valleys of the western Himalaya; however, their coupling with expanding urban land cover remains under-quantified. This study mapped the spatiotemporal exposure of built-up areas to heavy-day rainfall (HDR) across Jammu, Kashmir, and Ladakh and the adjoining areas by integrating daily Climate Hazards Group InfraRed Precipitation with Stations product (CHIRPS) precipitation (0.05°) with Global Human Settlement Layer (GHSL) built-up fractions within the Google Earth Engine (GEE). Given the limited sub-hourly observations, a daily threshold of ≥100 mm was adopted as a proxy for HDR, with sensitivity evaluated at alternative thresholds. The results showed that HDR is strongly clustered along the Kashmir Valley and the Pir Panjal flank, as demonstrated by the mean annual count of threshold-exceeding pixels increasing from 12 yr−1 (2000–2010) to 18 yr−1 (2011–2020), with two pixel-scale hotspots recurring southwest of Srinagar and near Baramulla regions. The cumulative high-intensity areas covered 31,555.26 km2, whereas 37,897.04 km2 of adjacent terrain registered no HDR events. Within this hazard belt, the exposed built-up area increased from 45 km2 in 2000 to 72 km2 in 2020, totaling 828 km2. The years with the most expansive rainfall footprints, 344 km2 (2010), 520 km2 (2012), and 650 km2 (2014), coincided with heavy Western Disturbances (WDs) and locally vigorous convection, producing the largest exposure increments. We also performed a forecast using a univariate long short-term memory (LSTM), outperforming Autoregressive Integrated Moving Average (ARIMA) and linear baselines on a 2017–2020 holdout (Root Mean Square Error, RMSE 0.82 km2; measure of errors, MAE 0.65 km2; R2 0.89), projecting the annual built-up area intersecting HDR to increase from ~320 km2 (2021) to ~420 km2 (2030); 95% prediction intervals widened from ±6 to ±11 km2 and remained above the historical median (~70 km2). In the absence of a long-term increase in total annual precipitation, the projected rise most likely reflects continued urban encroachment into recurrent high-intensity zones. The resulting spatial masks and exposure trajectories provide operational evidence to guide zoning, drainage design, and early warning protocols in the region.

Geomatics doi: 10.3390/geomatics5030036

Authors: Paula Andrea Contreras Rojas Felipe de Lucia Lobo Wesley J. Moses Gilberto Loguercio Collares Lino Sander de Carvalho

The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the spatial and temporal patterns of water quality in the lagoon system using Sentinel-3/OLCI satellite imagery. Atmospheric correction was performed using ACOLITE, followed by spectral grouping and classification into optical water types (OWTs) using the Sentinel Applications Platform (SNAP). To explore the behavior of water quality parameters across OWTs, Chlorophyll-a and turbidity were estimated using semi-empirical algorithms specifically designed for complex inland and coastal waters. Results showed a gradual increase in mean turbidity from OWT 2 to OWT 6 and a rise in chlorophyll-a from OWT 2 to OWT 4, with a decline at OWT 6. These OWTs correspond, in general terms, to distinct water masses: OWT 2 to clearer waters, OWT 3 and 4 to intermediate/mixed conditions, and OWT 6 to turbid environments. In the second part, we analyzed the response of the Patos Lagoon to flooding in Rio Grande do Sul during an extreme weather event in May 2024. Satellite-derived turbidity estimates were compared with in situ measurements, revealing a systematic underestimation, with a negative bias of 2.6%, a mean relative error of 78%, and a correlation coefficient of 0.85. The findings highlight the utility of OWT classification for tracking changes in water quality and support the use of remote sensing tools to improve environmental monitoring in data-scarce regions, particularly under extreme hydrometeorological conditions.

Geomatics doi: 10.3390/geomatics5030035

Authors: Andrew J. Lew Timothy Perkins Ethan Brewer Paul Corlies Robert Sundberg

Integrating diverse image datasets acquired from different satellites is challenging. Converting images from one sensor to another, like from WorldView-3 (WV) to SuperDove (SD), involves both changing image channel wavelengths and per-band intensity scales because different sensors can acquire imagery of the same scene at different wavelengths and intensities. A parametrized convolutional network approach has shown promise converting across sensor domains, but it introduces distortion artefacts when objects are in motion. The cause of spectral distortion is due to temporal delays between sequential multispectral band acquisitions. This can result in spuriously blurred images of moving objects in the converted imagery, and consequently misaligned moving object locations across image bands. To resolve this, we propose an enhanced model, the Physics-Informed Gaussian-Enforced Separated-Band Convolutional Conversion Network (PIGESBCCN), which better accounts for known spatial, spectral, and temporal correlations between bands via band reordering and branched model architecture.

Geomatics doi: 10.3390/geomatics5030034

Authors: Kazi Aminul Islam Omar Abul-Hassan Hongfang Zhang Victoria Hill Blake Schaeffer Richard Zimmerman Jiang Li

Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named CatBoostOpt, to estimate bathymetry based on high-resolution WorldView-2 (WV-2) multi-spectral optical satellite images. CatBoostOpt was demonstrated across the Florida Big Bend coastline, where the model learned correlations between in situ sound Navigation and Ranging (Sonar) bathymetry measurements and the corresponding multi-spectral reflectance values in WV-2 images to map bathymetry. We evaluated three different feature transformations as inputs for bathymetry estimation, including raw reflectance, log-linear, and log-ratio transforms of the raw reflectance value in WV-2 images. In addition, we investigated the contribution of each spectral band and found that utilizing all eight spectral bands in WV-2 images offers the best solution for handling complex water quality conditions. We compared CatBoostOpt with linear regression (LR), support vector machine (SVM), random forest (RF), AdaBoost, gradient boosting, and deep convolutional neural network (DCNN). CatBoostOpt with log-ratio transformed reflectance achieved the best performance with an average root mean square error (RMSE) of 0.34 and coefficient of determination (R2) of 0.87.

Geomatics doi: 10.3390/geomatics5030033

Authors: Nico Van de Weghe Lars De Sloover Jana Verdoodt Haosheng Huang

Capturing the interactions between moving objects is vital in traffic analysis, sports, and animal behavior, but remains challenging because of subtle spatiotemporal dynamics. This paper introduces HAIMO (Hybrid Analysis of the Interaction of Moving Objects), a conceptual framework that combines knowledge-driven AI for interpretable, symbolic interaction representations with data-driven models trained through self-supervised learning (SSL) on large sets of unlabeled trajectory data. In HAIMO, we propose using transformer architectures to model complex spatiotemporal dependencies while maintaining interpretability through symbolic reasoning. To illustrate the feasibility of this hybrid approach, we present a basic proof-of-concept using elite tennis rallies, where the knowledge-driven component identifies interaction patterns between players and the ball, and we outline how SSL-enhanced transformer models could support and strengthen movement prediction. By bridging symbolic reasoning and self-supervised data-driven learning, HAIMO provides a conceptual foundation for future GeoAI and spatiotemporal analytics, especially in applications where both pattern discovery and explainability are crucial.

Geomatics doi: 10.3390/geomatics5030032

Authors: Jeyavanan Karthigesu Toshiaki Owari Satoshi Tsuyuki Takuya Hiroshima

Individual tree crown detection (ITCD) and tree species classification are critical for forest inventory, species-specific monitoring, and ecological studies. However, accurately detecting tree crowns and identifying species in structurally complex forests with overlapping canopies remains challenging. This study was conducted in a complex mixed conifer–broadleaf forest in northern Japan, aiming to improve ITCD and species classification by employing two machine learning models and different combinations of metrics derived from very high-resolution (2.5 cm) UAV red–green–blue (RGB) and multispectral (MS) imagery. We first enhanced ITCD by integrating different combinations of metrics into multiresolution segmentation (MRS) and DeepForest (DF) models. ITCD accuracy was evaluated across dominant forest types and tree density classes. Next, nine tree species were classified using the ITCD outputs from both MRS and DF approaches, applying Random Forest and DF models, respectively. Incorporating structural, textural, and spectral metrics improved MRS-based ITCD, achieving F-scores of 0.44–0.58. The DF model, which used only structural and spectral metrics, achieved higher F-scores of 0.62–0.79. For species classification, the Random Forest model achieved a Kappa value of 0.81, while the DF model attained a higher Kappa value of 0.91. These findings demonstrate the effectiveness of integrating UAV-derived metrics and advanced modeling approaches for accurate ITCD and species classification in heterogeneous forest environments. The proposed methodology offers a scalable and cost-efficient solution for detailed forest monitoring and species-level assessment.

Geomatics doi: 10.3390/geomatics5030031

Authors: Enrico Corrado Borgogno-Mondino

Recently, geomatics-related data, products, services and applications have proven to significantly support many actions in environmental (land, water, extra-terrestrial) analysis, management and protection, often answering to political instances [...]