Geomatics, Volume 6, Issue 3 (June 2026) – 30 articles

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article