Applied Geomatics最新文献

{"title":"Characterization of urban surface materials through UAV-based thermal imaging and in situ data collection. Two case study in Madrid (Spain)","authors":"Beatriz Arranz,&nbsp;Raquel Burgos-Bayo,&nbsp;Emilia Román López,&nbsp;Julián Aguirre de Mata,&nbsp;Fernando Martín-Consuegra","doi":"10.1007/s12518-026-00787-y","DOIUrl":"10.1007/s12518-026-00787-y","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated machine learning and mobile GIS framework for bridge risk management: a prototype study","authors":"Ameur Gharbi","doi":"10.1007/s12518-026-00777-0","DOIUrl":"10.1007/s12518-026-00777-0","url":null,"abstract":"<div><p>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%. Feature importance analysis indicated that structural condition, bridge age, and load capacity were the most influential variables in the classification process. Integration within the QGIS environment enabled spatial visualization and mapping of bridge risk categories, supporting inspection planning and maintenance prioritization. The proposed framework demonstrates the feasibility of combining Mobile GIS, geospatial analysis, and machine learning within a unified bridge risk classification workflow. The study is presented as a proof-of-concept decision-support system for structured bridge risk classification rather than a data-driven model for predicting real-world bridge failure. </p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148838032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Quantifying urban expansion in Gurugram: a multi-sensor GIS,entropy, and machine-learning approach","authors":"Agradeep Mohanta,&nbsp;Ameet Yadav,&nbsp;Naveen Yadav,&nbsp;Kalyan Malik,&nbsp;Sanjay Kumar,&nbsp;Aman Kumar,&nbsp;Pardeep Kumar,&nbsp;Sultan Singh","doi":"10.1007/s12518-026-00783-2","DOIUrl":"10.1007/s12518-026-00783-2","url":null,"abstract":"","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148783358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying urban expansion in Gurugram: a multi-sensor GIS, entropy, and machine-learning approach","authors":"Agradeep Mohanta,&nbsp;Ameet Yadav,&nbsp;Naveen Yadav,&nbsp;Kalyan Malik,&nbsp;Sanjay Kumar,&nbsp;Aman Kumar,&nbsp;Pardeep Kumar,&nbsp;Sultan Singh","doi":"10.1007/s12518-026-00782-3","DOIUrl":"10.1007/s12518-026-00782-3","url":null,"abstract":"<div><p>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 &gt; 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.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148614176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of olive tree crown volumes using google earth imagery: a comparison with terrestrial laser scanning (TLS)","authors":"Antonio J. Zapata-Sierra,&nbsp;M. A. Montero-Rodríguez,&nbsp;Carmen Marín-Buzón,&nbsp;Antonio M. Pérez-Romero,&nbsp;Francisco Manzano-Agugliaro","doi":"10.1007/s12518-026-00781-4","DOIUrl":"10.1007/s12518-026-00781-4","url":null,"abstract":"<div><p>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 (V_SEC) selected as the control due to its statistical robustness. Results indicated that the traditional ellipsoidal model significantly underestimated volume (-28%), while the cylindrical model slightly overestimated it (+ 8%). Conversely, the proposed Google Earth method, multiplying the satellite-derived projected area by canopy height, demonstrated high accuracy, with a negligible bias (+ 0.57 m³) compared to the control method, TLS Cross-sections; this means 2.3%. Furthermore, a production coefficient of 1.5 kg of olives per m³ of canopy volume was established for this orchard. We conclude that while TLS provides superior detail, the Google Earth based method offers a rapid, cost-effective, and sufficiently accurate alternative for yield forecasting in large scale olive groves.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of deep learning into a spatial data infrastructure for automatic delineation of agricultural parcels in rural areas","authors":"Sara Sahraoui,&nbsp;Reda Yaagoubi,&nbsp;Mourad Bouziani,&nbsp;Zakariae Haraz","doi":"10.1007/s12518-026-00780-5","DOIUrl":"10.1007/s12518-026-00780-5","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of TerraSAR-X satellite neutral radio occultation profiles across the equatorial region","authors":"Asmaa Taha Hassan,&nbsp;Ibrahim Fouad Ahmed,&nbsp;Ashraf El-Kutb Mousa,&nbsp;Gamal El-Fiky","doi":"10.1007/s12518-026-00775-2","DOIUrl":"10.1007/s12518-026-00775-2","url":null,"abstract":"<div><p>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 &gt; 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.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Carbon stock modelling in Minas Gerais, Brazil: effects of dimensionality reduction in machine learning algorithms","authors":"Emerson Cristi de Barros,&nbsp;Gefferson Pereira da Paixão,&nbsp;José Augusto Amorim Silva do Sacramento,&nbsp;Paulo Sergio Taube,&nbsp;João Thiago Rodrigues de Sousa","doi":"10.1007/s12518-026-00772-5","DOIUrl":"10.1007/s12518-026-00772-5","url":null,"abstract":"<div><p>This study aimed to forecast soil carbon in Minas Gerais, Brazil. We are using the Random Forest Regressor machine learning technique. The study utilised 41 environmental factors. We were focused on how reducing the model’s complexity could help map soil carbon more effectively. To select the variables, we utilised Altmann’s permutation method combined with the top-k selection test. Results show that solar radiation, water deficiency, and the Palmer Drought Severity Index were the main variables. Notably, although the simplified model exhibited precision metrics, it provided greater interpretability and robustness. We also found several non-linear connections between carbon storage and variables such as solar radiation and water stress. It became evident that the climate significantly influences carbon distribution in this area. In general, these enhanced models could significantly support agricultural zoning, conservation initiatives, and soil management.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00772-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GIS-based rainwater accumulation susceptibility mapping in Abomey and Bohicon, Benin","authors":"Yéhounko Bruno Buffon Gbodjinou,&nbsp;Moïse Chabi,&nbsp;Ousséni Arouna","doi":"10.1007/s12518-026-00778-z","DOIUrl":"10.1007/s12518-026-00778-z","url":null,"abstract":"<div><p>The cities of Abomey and Bohicon are experiencing rapid urbanization and increasing soil sealing, which exacerbate the risks of rainwater accumulation in the urban area. This study maps the zones susceptible to rainwater accumulation across the area through an approach coupling the Analytic Hierarchy Process (AHP) and Geographic Information Systems (GIS). Fourteen criteria were weighted and integrated: accumulation, runoff and precipitation; topographic indices (TWI, SPI, TPI); land use and land cover (LULC), drainage density, stormwater drainage network density (DRAP), NDVI, geology, pedology, elevation and slope. The data combine ALOS PALSAR imagery, Sentinel-2 imagery and CHIRPS rainfall data over the 2000–2024 period, together with field surveys. The AHP matrix shows a satisfactory consistency ratio (CR = 0.021). Direct hydrological transfer factors dominate the hierarchy (46.71% of the total weight), led by accumulation (21.79%), runoff (14.77%) and precipitation (10.15%). The resulting map identifies 14.28 km² (10.43% of the study area) in the high susceptibility class, concentrated in urbanized sectors with low drainage coverage. Field validation using 445 ground-truth points yields an overall accuracy of 86.07% and a Kappa of 0.788. This mapping provides an operational tool for urban planning and the targeting of drainage investments.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reference SMT-assisted ICP approach for integrating low-cost LiDAR and terrestrial laser scanner point clouds","authors":"Wioleta Błaszczak-Bąk,&nbsp;Tomasz Kozakiewicz","doi":"10.1007/s12518-026-00779-y","DOIUrl":"10.1007/s12518-026-00779-y","url":null,"abstract":"<div><p>The increasing popularity and availability of smartphones integrated with high-precision sensors have paved the way for innovative scientific applications and cost-effective observations. In the field of geodetics, the Light Detection and Ranging (LiDAR) sensor plays a crucial role, facilitating the rapid acquisition of three-dimensional point clouds that represent objects. This capability is especially advantageous when complementing measurements obtained using terrestrial laser scanning (TLS), which represents a ground-based LiDAR technology. The use of a LiDAR-equipped smartphone enables the efficient capture of small fragments, thereby eliminating the necessity for an expensive, typically of considerable size, and not always freely accessible terrestrial scanner. However, it is imperative to register the point clouds acquired with a smartphone’s LiDAR sensor with those obtained using a terrestrial scanner during the pre-processing stage. The objective of this study is to propose a novel method for point cloud registration. Dedicated spatial measurement targets (SMTs) were incorporated into the Iterative Closest Point (ICP) algorithm for the purpose of enhancing the registration process. The efficacy of the proposed method was evaluated through a series of tests involving measurements obtained with a LiDAR-equipped smartphone, with and without the incorporation of SMTs. The results were further compared to measurements conducted using TLS. The analysis demonstrated that the newly proposed registration method yielded superior results in comparison to traditional point cloud registration techniques. The integration of SMTs with the ICP algorithm enhances the accuracy and efficiency of point cloud registration, ensuring precise alignment of data from different devices. This method automates the process, reducing manual corrections and expediting data processing. It is particularly beneficial in applications like architecture and construction, where seamless integration of smartphone LiDAR and TLS data improves workflow and reduces costs.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00779-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}