Geomatics

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article