The growing need for affordable and accessible geospatial tools in land administration has intensified interest in mobile GNSS applications, particularly for Fit-For-Purpose (FFP) Cadastral Mapping. This study evaluates the positional accuracy of eight selected mobile GNSS applications by comparing their coordinate outputs against reference data obtained from Differential GNSS (DGNSS). Initially, ten applications were tested under similar environmental conditions; however, two were excluded from final analysis due to persistent extreme deviations and irregular positional stability across the survey stations. Their Root Mean Square Error (RMSE) values were significantly higher than those of the other applications and exceeded the acceptable accuracy threshold for FFP Cadastral Mapping. The remaining eight apps were assessed using statistical methods, including the Kruskal-Wallis test, to determine significant differences in positional accuracy. The Mobile Topography application demonstrated the highest accuracy, with a mean deviation of 2.83 m, a 95
This study proposes an integrated bridge risk management framework combining Machine Learning (ML), Geographic Information Systems (GIS), and Mobile GIS technologies to support the organization and spatial visualization of bridge inspection and risk classification processes in Algeria. A Mobile GIS application was deployed to collect georeferenced field data on structural defects, including deck deterioration, superstructure and substructure anomalies, and bearing malfunctions, enabling structured and timely inspection data acquisition. Due to the absence of comprehensive historical bridge failure or deterioration datasets, existing bridges were first evaluated using a Likelihood of Failure × Consequence of Failure (LOF × COF) framework, and the resulting risk categories were used as reference labels for model development. A Random Forest classifier was applied to a dataset of 15 bridges described by structural, environmental, and operational attributes. Model evaluation was conducted using Leave-One-Out Cross-Validation (LOOCV), resulting in an overall accuracy of 86.67
Rapid urban expansion in emerging metropolitan regions has created significant challenges for sustainable land management, environmental conservation, and infrastructure planning. Gurugram District, one of India’s fastest-growing urban centers within the National Capital Region (NCR), has experienced substantial land-use transformation over the last decade; however, a comprehensive geospatial assessment of its spatial growth dynamics and future expansion patterns remains limited. Therefore, the present study aims to quantify the spatiotemporal patterns of urban expansion, evaluate the intensity and direction of urban sprawl, and predict future urban growth using integrated geospatial and machine-learning approaches. Multi-temporal Sentinel-2 imagery (2015–2025), Enhanced Vegetation Index (EVI)-based built-up extraction, supervised classification, Shannon’s entropy analysis, factor analysis, multiple regression, ANN–Markov Chain modelling, and XGBoost algorithms were employed to assess urban growth dynamics in Gurugram District. The results indicate that built-up area increased substantially from 102.50 km² in 2015 to 168.20 km² in 2025, primarily driven by infrastructure-led development along the Dwarka Expressway, Southern Peripheral Road, and New Gurugram corridors. Shannon’s entropy analysis revealed increasing spatial dispersion, indicating a transition from relatively compact urban development toward fragmented peri-urban sprawl. Factor analysis and regression modelling identified economic forces, accessibility, administrative processes, and opportunity-driven variables as the major determinants of urban expansion, while environmental variables exhibited comparatively lower influence. Future urban growth simulations demonstrated high predictive performance (AUC > 0.90) and projected intensified expansion toward transport-oriented peri-urban sectors by 2030. The findings highlight the growing pressure on agricultural land, ecological systems, and urban infrastructure, emphasizing the need for integrated spatial planning, sustainable land-use management, and evidence-based urban policy interventions for Gurugram’s future development.
Yield forecasting is a critical challenge in modern olive cultivation, where canopy volume is a primary determinant of production in irrigated orchards. This study evaluates different methodologies for estimating olive tree crown volume to predict yield. Terrestrial Laser Scanning (TLS), considered the most accurate method, was used as a reference to benchmark traditional geometric models (ellipsoid and cylinder) and a remote sensing approach based on Google Earth imagery. The study was conducted on an irrigated ‘Arbequina’ orchard in Almeria, Spain. For TLS, three calculation algorithms were analyzed (Cross-sections, Cyclone, and CloudCompare), with the cross-sectional method (VSEC) selected as the control due to its statistical robustness. Results indicated that the traditional ellipsoidal model significantly underestimated volume (-28
Precise delineation of agricultural parcels from aerial images is a central issue for land use monitoring, agricultural planning and the optimization of cultivation practices. In rural areas, the lack of up-to-date, interoperable parcel boundary data hinders the implementation of effective agricultural policies and the modernization of farming practices. This study proposes a processing workflow integrating deep learning within a Spatial Data Infrastructure (SDI), to automate parcel delineation from RGB orthoimages at 1-meter resolution. This SDI constitutes a structuring framework that guarantees the interoperability, sharing and continuous updating of results, while facilitating their exploitation. Six image segmentation models U-Net, DeepLabV3+, TransUNet, MFCA-Net, HRNet-OCR and BiSeNetV2 were evaluated using RGB orthoimages with 1-meter spatial resolution. The U-Net and DeepLabV3 + models deliver the best results by achieving a Dice coefficient of 0.9384 and 0.9355 and an F1 Score of 0.9416 and 0.9385 and an IoU of more than 0.87. The second-best results were obtained by MFCA-Net and TransUNet which demonstrate good shape recognition abilities although they sometimes produce over-segmentation. The performance of HRNet-OCR and BiSeNetV2 is limited mainly because they struggle with small objects detection which results in high Loss values (0.1168 and 0.0932) and IoU below 0.83. The U-Net model demonstrates the highest stability for applications that need precise contour detection and strong generalization capabilities. The implementation of this intelligent data into an SDI system creates a practical solution which drives scientific research and technological advancement and enables specific agricultural and territorial management innovations.
Global Navigation Satellite System Radio Occultation (GNSS-RO) provides high‑precision atmospheric profiling by measuring the bending of radio signals as they traverse Earth’s atmosphere. This study evaluates TerraSAR‑X (TSX) Neutral Radio Occultation (NRO) atmospheric profiles over the equatorial belt (40°S–40°N) from 2009 to 2023 through comparison with COSMIC‑2 satellite observations and three global numerical weather prediction models (GFS, ECMWF, ERA5). The analysis focuses on pressure, temperature, refractivity, and water vapor pressure (WVP), using a 10‑day sampling strategy to balance computational efficiency and statistical robustness while sampling a broad range of synoptic and seasonal conditions. For TSX–COSMIC‑2 collocations, the results show excellent agreement for pressure (mean difference: 0.02 mb, RMSE: 0.32 mb), temperature (0.21 °C, RMSE: 0.97 °C), and WVP (0.14 mb, RMSE: 0.63 mb), while refractivity also demonstrates strong consistency (0.62, RMSE: 2.76) with correlation coefficients ≥ 0.92 for all parameters. t‑ and F‑tests applied to the matched profiles do not indicate statistically significant mean or variance differences for pressure, temperature, or refractivity (p > 0.05), whereas WVP exhibits significant differences attributed to small‑scale spatiotemporal variability. Comparisons with GFS, ECMWF, and ERA5 confirm generally good agreement across all variables over the full 15‑year period, with consistently low biases and RMSE values. Annual, seasonal, and diurnal analyses reveal stable TSX profile availability and clear equatorial sampling characteristics. The combined results support the use of TSX NRO observations as a reliable component of the atmospheric observing system in tropical regions and provide a practical validation framework for future satellite‑based atmospheric profiling missions.
Heritage 3D Reconstruction is a digital bridge to the past, providing a vivid window into history by meticulously preserving the intricate details of cultural artifacts and sites and bringing them to life for future generations to explore and appreciate. In recent years, novel Neural Rendering methods, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have shown promising results for 3D reconstruction. Despite their rapid development, a systematic comparison of these approaches in the context of 360^∘ imagery, particularly within Digital Cultural Heritage (DCH), remains largely unexplored. In this work, we present a novel comparative framework specifically designed to address this gap, performing a comprehensive assessment of traditional 360^∘ photogrammetry against emerging Neural Rendering approaches. To validate its effectiveness, we conducted a comparative analysis on two different Cultural Heritage scenarios: a large-scale outdoor environment and an object-centered setting. These case studies, which present a variety of challenges in the DCH domain, were selected to evaluate the ability of photogrammetry, NeRF, and 3DGS-based methods to reconstruct and render complex scenes in terms of both geometric accuracy and visual quality. The results highlight the differences between Neural Rendering approaches and conventional methods, particularly in handling spherical geometry, adaptability to varying lighting conditions and efficiency in data acquisition and processing. These findings confirm the continued robustness of spherical photogrammetry for metric reconstruction, while also underscoring the potential of Neural Rendering-based scene representation methods as promising tools for DCH preservation.
Remote sensing (RS) technologies have increasingly become essential in mapping mineral resources, as they offer innovative and cutting-edge solutions crucial for detecting and comprehensively assessing mineral deposits across diverse geological settings. This in-depth review thoroughly explores several prominent and newly emerging trends, encompassing significant advancements in satellite imagery, strategic combination of artificial intelligence (AI) and machine learning (ML) methodologies along with relevant case studies, the use of Light detection and ranging (LiDAR), RADAR (Radio Detection and Ranging) systems, drone and unmanned aerial vehicles (UAVs), real time data analysis and data fusion techniques. These sophisticated technologies markedly enhance the efficacy and accuracy of data collection, processing, and analysis, ultimately facilitating the formulation and execution of more effective exploration strategies that can considerably diminish the time and resources necessary for successful mineral exploration. Unlike previous reviews, we synthesize these trends to chart the evolution towards predictive mineral systems modeling, identifying key challenges and future pathways for the industry. By addressing both the innovative features and the constraints of remote sensing (RS) technologies, this analysis seeks to present a balanced viewpoint that enriches the ongoing dialogue regarding their future role in mineral resource exploration. Ultimately, this review functions not only as a reference for comprehending the current state of remote sensing (RS) technologies but also as a framework for future research and development initiatives to address existing challenges and optimize the potential advantages of these revolutionary tools.
Utility poles, particularly lamp posts, are essential for urban planning, infrastructure management, and road safety. Accurate roadside asset inventories are increasingly required to support maintenance and safety assessments. UAV photogrammetry has emerged as a potentially cost-effective and efficient approach for infrastructure mapping due to its rapid data acquisition and high spatial resolution. However, extracting pole-specific information from UAV-derived point clouds remains challenging and labour-intensive, especially in newly developed areas. While Light Detection and Ranging (LiDAR) has been widely applied for pole detection, limited studies have explored UAV photogrammetry as a standalone solution. This study proposes a proof-of-concept automated workflow for extracting lamp posts from UAV-derived point clouds using clustering-based segmentation techniques. The method integrates planar grid filtering for feature isolation and K-means clustering for object identification, with preprocessing steps including voxelisation and statistical outlier removal. The workflow was implemented using geometrically defined parameters, which are expressed in metres in accordance with the coordinate system of the dataset. Parameter values, including voxel size (0.1 m), neighbour distance (10 m), grid resolution (0.1 m), standard deviation (2.0), and lower-bound height (8 m), were determined through empirical evaluation. The method was applied to a dataset of 402 UAV images to generate a dense point cloud. Clustering analysis identified six distinct objects, consistent with the number of lamp posts observed in the study area. The extracted heights showed a mean difference of 0.02 m when compared with field measurements. The results demonstrate the potential feasibility of the proposed approach for automated extraction of pole-like structures from UAV photogrammetric point clouds under the tested controlled conditions. However, the validation is limited to a single study area and should be interpreted as a proof-of-concept. Further evaluation using larger and more diverse datasets is required to assess robustness, scalability, and applicability across different environments.
The urban materials surface interacts with solar energy, and these interactions can significantly influence the thermal environment, contributing to the Urban Heat Island effect. The work presented here responds to the need to better understand the thermal behavior of urban surfaces during the summer period under real conditions of solar exposure. The project focus on analyzing two case studies located in neighborhoods affected by intense Urban Heat Island and social vulnerability, urban overheating rises urban vulnerability. The main objective of this work is to characterize the thermo-optical properties and thermal behaviour of representative urban materials using an integrated methodology combining UAV thermal imagery and in situ measurements, obtaining values of reflectivity, emissivity, and surface temperature. The two neighbourhoods are used as complementary case studies to evaluate the applicability and robustness of the proposed methodology under real urban conditions, rather than as experimental replicates intended for direct thermal comparison. This characterization will help identify how commonly used materials behave thermally and support the proposal of improvements and intervention priorities. Thermal images were obtained using UAVs over two vulnerable urban areas. Thermal emissivity and reflectance were combined with thermal images. Solar exposure and surface orientation are key factors in thermal performance. Wastewater and sludge act as critical nodes linking MPs to One Health risks.
Trees located along railway corridors pose a significant risk to transport infrastructure, particularly in the context of increasingly frequent and intense storm events driven by climate change. Proactive vegetation management strategies are therefore essential for reducing the risk of disruptions caused by falling trees. This study applies an existing GIS-based assessment framework to analyse tree fall risk and hazard along railway infrastructure. The approach is demonstrated along a corridor in the Sachsenwald forest, Germany, using high-resolution individual tree crown (ITC) data derived from the Digital Twin Germany (DigiZ-DE) project. By integrating high-resolution airborne LiDAR data and openly available geospatial datasets, the methodology enables spatially explicit assessments at the level of individual trees. It combines two complementary analytical perspectives: a basic risk-oriented exposure analysis, which identifies trees that may directly affect railway infrastructure based on height and distance to the track, and an advanced hazard-oriented susceptibility analysis, which incorporates site- and tree-specific factors. This dual structure enables both rapid operational screening and more comprehensive, precautionary vegetation management. The results can inform targeted interventions, enabling resource-efficient and ecologically sensitive mitigation strategies. While the accuracy of the outputs depends on the timeliness and quality of the input data, the modularity of the approach supports its transferability across regions and infrastructure types. Although applied here to railway systems, the framework is readily adaptable to other transport or utility corridors and can contribute to climate-resilient infrastructure planning and natural hazard management.
Groundwater vulnerability assessment is critical for sustainable management. However, previous research in arid regions, generally rely on traditional index-based models and their methodological adaptations. Combining conventional parametric approaches and machine learning (ML) methods, the objective of this study is to conduct a comparative assessment of groundwater vulnerability and water quality in the Medenine aquifer (Southeastern Tunisia) covering about 3100 km2. Using GIS-based spatial analysis, four vulnerability models were applied: standard DRASTIC, modified DRASTIC, AHP-DRASTIC and AHP-modified DRASTIC. Geological, hydrogeological, and land-use factors were integrated to generate vulnerability maps. Moreover, the Groundwater Quality Index (GQI) was computed and integrated with ML models to analyze the hydrochemical parameters influence on groundwater quality and to explore nonlinear relationships. Based on the standard DRASTIC, the area was divided into low (2 Standard and modified parametric models are widely used for groundwater vulnerability assessment based on predefined parameters. AHP-modified DRASTIC model rerated and reweighted parameters to improve spatial representation of vulnerability. Vulnerability maps serve as valuable tools for groundwater protection and sustainable territorial planning in arid regions. Groundwater Quality Index (GQI) supports effective groundwater management in arid regions. Machine Learning integration improves GQI prediction and supports more accurate water quality assessment.
This contribution aims to analyze the specific vulnerabilities of the historic center of Venice and to develop an evaluative method capable of taking into account both the intrinsic and extrinsic characteristics of the built environment through the use of georeferenced information systems. In particular, the study focuses on the analysis of vulnerabilities related to the gradual phenomenon of mean sea level rise and on an approach for the identification of site-specific surface temperatures based on remote sensing data. Finally, the work addresses the integration of the different types of datasets into a single spatial management platform (GIS), referenced to individual historic buildings.
Groundwater (GW) is recognized as the second-largest freshwater reservoir in the world, following surface water. Over the years, it has been subjected to immense pressure to meet human demands. Additionally, one of the most significant consequences of human activities is climate change, which can alter the physical and chemical properties of GW, thereby impacting its ecological functions. This study examined GW patterns in Iran from 2000 to 2022, emphasizing the roles of both climatic and anthropogenic factors in altering GW levels. The present study evaluated various climatic and anthropogenic variables, including air temperature (AT), precipitation, snow cover, urban development, agricultural land, and surface water storage to explore their relationships with changes in GW. The Mann-Kendall (MK) trend test was employed to analyze these relationships. The MK trend analysis revealed a significant declining trend in GW (S = − 167, Z = − 4.04), accompanied by decreasing trends in surface water (Sen’s slope = − 15.29) and cropland area (Sen’s slope = − 337.25). Snow cover, precipitation, AT, and built-up areas exhibited non-significant trends over the study period. The analysis also revealed a correlation coefficient of -0.43 between AT and GW. Precipitation and snow cover were found to have a relatively moderate effect on GW levels, with a correlation coefficient of 0.43 and 0.54, respectively. A significant negative correlation of -1.00 was observed between built-up areas and GW from 2000 to 2022, indicating that an increase in settlement development has led to decrease in GW. The findings also indicated a positive correlation coefficient of 0.75 and 0.872 between cropland, surface water and GW, respectively, demonstrating that a decrease in cropland is linked to a decrease in GW and surface water. Overall, the results underscore the substantial influence of anthropogenic factors, such as urban expansion, agriculture, and surface water on GW in Iran. Anthropogenic factors, as well as climatic factors contributed to a reduction in GW by 33 mm, with a marked decline starting in 2009. Before this pivotal year, the average GW was around 629 mm, which dropped to 596 mm afterward.
eXtended Reality (XR) technologies are increasingly employed for the visualization and communication of cultural heritage (CH), offering new opportunities for interpretation, documentation, and education. Within this context, the integration of XR with rigorous geomatic methodologies remains a relevant research topic, particularly for engineering-oriented applications. This paper presents a Mixed Reality (MR) application developed through a fully metric and integrated geomatic workflow in a university education context. The case study is the façade of the remaining structure of the San Salvatore ad Calchi medieval church, formerly identified as part of the Theodoric Palace, in Ravenna, Italy. Terrestrial Laser Scanning (TLS), close-range and unmanned aerial vehicle (UAV) photogrammetry, GNSS, and GIS data were systematically combined to produce an accurate three-dimensional documentation of the structure. A historical drawing from 1800’s – representing a previous constructive phase of the façade – was rectified using the obtained georeferenced orthophoto and transformed into a metrically consistent 3D model, enabling its in-situ superimposition onto the existing building within a MR environment. The application was developed in Unity and deployed on a Meta Quest 3 headset, permitting mutual visualization of real and virtual elements. Beyond the technical implementation, the study emphasizes the educational perspective. The complete Survey-to-XR workflow was integrated into a master’s-level Geomatics Engineering course focused on cultural heritage. Pre- and post-experience surveys indicate that XR enhances students’ understanding of integrated geomatic processes, spatial relationships, and data visualization and interpretation. The results support the use of MR as an effective educational tool when grounded in robust geomatic frameworks.
Gully erosion represents a severe geomorphic threat to tropical landscapes, yet multi-temporal assessments of its expansion and comparative susceptibility modelling remain under-researched. This study implements an integrated geomatics pipeline to quantify gully dynamics and model erosion susceptibility in two contrasting formations in Anambra State, Nigerian: the gully-prone Nanka sands formation of Idemili and the stable Imo clay formations of Awka area. Utilising the high-resolution Google Earth imagery of years 2000 and 2025, gully inventories were manually digitised, revealing a significant intensification of land degradation. In Idemili, gully frequency rose by 69
In recent years, research in Structural Health Monitoring (SHM) has intensified due to the growing demand for efficient inspection, monitoring, and maintenance of civil engineering structures. To meet this demand, studies have focused on developing non-destructive techniques (NDT) capable of providing accurate and continuous assessments. Among these, point clouds have emerged as a robust approach to capture comprehensive environmental data and provide detailed information for further analysis. This paper presents a state-of-the-art review summarizing advancements over the last ten years in the use of point clouds obtained by laser scanning and photogrammetry in SHM. Initially, an overview of the data acquisition techniques and their operational principles is presented. Subsequently, relevant studies selected through a systematic literature review are analyzed, emphasizing their methodologies, advantages, and limitations. Finally, the paper discusses the current challenges in integrating point cloud technologies into SHM frameworks and outlines potential future research directions aimed at enhancing their precision and automation capabilities.
Accurate and up-to-date bathymetric data are essential for the management and spatial planning of transitional coastal environments, yet conventional survey methods remain costly and difficult to implement at large spatial scales and high temporal frequency. This study investigates the potential of Satellite-Derived Bathymetry (SDB) as a cost-effective monitoring tool, using the Venice Lagoon as a morphologically and optically complex test site. Two methodological axes were developed. The first applies a stratified log-ratio Band Ratio Method (Stumpf et al. 2003) using Sentinel-2 multitemporal imagery, calibrated against the 2002 full-lagoon bathymetric dataset (Sarretta et al. 2010), achieving a Root Mean Square Error (RMSE) of 1.09 m and an R² of 0.87 across the entire depth range. The second axis refines this approach through advanced radiometric pre-processing, including sun-glint correction and clean-water compositing, combined with a Random Forest substrate classification and a stratified SDB calibration for sandy versus muddy seafloors. The resulting SDB was further integrated with Kriging with External Drift (KED), using the 2013 CNR-ISMAR (National Research Council Institute of Marine Sciences) multibeam survey as ground truth. This hybrid SDB-KED model achieved an R² of 0.73, an RMSE of 2.55 m, and a near-zero bias (-0.09 m) over more than 320,000 validation points, demonstrating effective bathymetric reconstruction across the full lagoon depth range. Residual uncertainties are concentrated at channel margins and in areas exceeding 15 m depth, where optical signal extinction limits satellite penetration. The proposed framework, implemented entirely on open-source data and cloud computing platforms, offers a replicable and scalable protocol for supporting spatial planning in dynamic coastal and marine environments.
By 2050, about two-thirds of the world’s population will live in urban areas, intensifying resource consumption in cities. Depending on urban sprawl and building morphology, this can lead to adverse environmental and public health effects, including increased energy consumption and the Urban Heat Island effect. While urban sprawl in India has been studied in terms of land-use changes, systematic assessments of spatio-temporal variability in building morphology remain limited. The current study addresses this gap by examining the evolution of building morphology in 11 major Indian cities over two years: 2018 and 2023. Remote sensing data, coupled with a deep learning model, Simultaneous building Height And footprinT extraction from Sentinel imagery (SHAFTS), produces building height and footprint maps at 100 m resolution. Variability index (VI), defined as the rate of change in spatial autocorrelation with shift distance, was used to quantify urban heterogeneity. In 2023, the mean footprint and height VIs were 0.0481 km−1 and 0.0598 km−1, indicating that height variability was 24
In India, mustard (Brassica juncea) is a significant cash and oilseed crop. This work aims to evaluate the different mustard crop classification algorithms for mapping mustard crop in Bharatpur District, Rajasthan, India, using Google’s Earth Engine (GEE) cloud platform and integrates socio-economic, economic, and terrain-topography parameters into the crop suitability analysis. Several methods were used, including unsupervised and supervised classification, HDRBC, RF, and machine learning-based classification, over multi-temporal satellite data captured 15 days apart from October 2022 until March 2023-Rabi, and geospatial indicators generated include FCC, NDVI, land use/land cover crop masks, and time series vegetation dynamics through custom GEE codes. According to the government statistics report, the area under mustard cultivation for the 2022–2023 season was around 274,500 ha. According to the model predictions, the areas under mustard cultivation are unsupervised, 296,763 ha; supervised, 290,954 ha; HDRBC, 280,534 ha; and RF, 278,541 ha. The model with the best accuracy (93.67
