Geomatics

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

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

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

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

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

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