International journal of applied earth observation and geoinformation : ITC journal最新文献

{"title":"PolSAR image classification using complex-valued multiscale attention vision transformer (CV-MsAtViT)","authors":"Mohammed Q. Alkhatib","doi":"10.1016/j.jag.2025.104412","DOIUrl":"10.1016/j.jag.2025.104412","url":null,"abstract":"<div><div>This paper Introduces a novel method for Polarimetric Synthetic Aperture Radar (PolSAR) image classification using a Complex-Valued Multiscale Attention Vision Transformer (CV-MsAtViT). The model incorporates a complex-valued multiscale feature fusion mechanism, a complex-valued attention block, and a Complex-Valued Vision Transformer (CV-ViT) to effectively capture spatial and polarimetric features from PolSAR data. The multiscale fusion block enhances feature extraction, while the attention mechanism prioritizes critical features, and the CV-ViT processes data in the complex domain, preserving both amplitude and phase information. Experimental results on benchmark PolSAR datasets, including Flevoland, San Francisco, and Oberpfaffenhofen, show that CV-MsAtViT achieves superior classification accuracy, with an overall accuracy (OA) of 98.35% on the Flevoland dataset, outperforming state-of-the-art models like PolSARFormer. The model also demonstrates efficient computational performance, minimizing the number of parameters while preserving high accuracy. These results confirm that CV-MsAtViT effectively enhances the classification of PolSAR images by leveraging complex-valued data processing, offering a promising direction for future advancements in remote sensing and complex-valued deep learning.</div><div>The codes associated with this paper are publicly available at <span><span>https://github.com/mqalkhatib/CV-MsAtViT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104412"},"PeriodicalIF":7.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428850","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":"Efficient management of ubiquitous location information using geospatial grid region name","authors":"Daoye Zhu ,&nbsp;Min huang ,&nbsp;Qifeng Lin ,&nbsp;Yanyu Wang ,&nbsp;Shuang Li ,&nbsp;Chengqi Cheng","doi":"10.1016/j.jag.2025.104400","DOIUrl":"10.1016/j.jag.2025.104400","url":null,"abstract":"<div><div>With the increasing popularity of sensors and the rapid advancement of network infrastructure and communication technology, managing, retrieving, and applying ubiquitous location information (ULI) poses a significant challenge. This study introduces the concept of the geospatial grid region name (GGRN) and proposes a ULI management method based on the GGRN (UMMG). To evaluate the feasibility and retrieval efficiency of the UMMG, it was applied to mainstream databases and compared with their spatial expansion modules. The experimental results demonstrate that the UMMG effectively addresses the challenge of precise location cognition between humans and machines while also reducing the complexity of spatial database indexing, with an overall performance improvement of 43.00 % compared to Oracle Spatial and 33.30 % compared to PostgreSQL + PostGIS.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104400"},"PeriodicalIF":7.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428888","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":"Extraction of gully erosion using multi-level random forest model based on object-based image analysis","authors":"Mengxia Xu ,&nbsp;Mingchang Wang ,&nbsp;Fengyan Wang ,&nbsp;Xue Ji ,&nbsp;Ziwei Liu ,&nbsp;Xingnan Liu ,&nbsp;Shijun Zhao ,&nbsp;Minshui Wang","doi":"10.1016/j.jag.2025.104434","DOIUrl":"10.1016/j.jag.2025.104434","url":null,"abstract":"<div><div>Gully erosion cause soil organic matter loss, which poses a grave threat to food security and regional ecological sustainability. Remote sensing monitoring and information extraction of gully erosion are of great significance to protect cultivated land resources and agricultural production. To improve the extraction accuracy of gully erosion, multi-level random forest (RF) extraction model based on object-based image analysis (OBIA) is proposed to extract gully erosion information. The Gaofen-2 (GF-2) image was selected as the main data source, supplemented by topographic data, to segment the features in Dehui City based on multi-scale segmentation method. Fusing spectral, textural and geometric feature information, the RF Gini index (GI) was used for feature optimization. Gully erosion extraction based on feature classes was performed using multi-level RF model based on OBIA in the southwestern part of Dehui City, with an overall accuracy (OA) of 96.71% and a Kappa coefficient (Kappa) of 0.865. Compared with the single-level extraction results, the OA and Kappa were improved by 8.4% and 0.102, which indicated that this model has better performance and has certain application value for the research of gully erosion information extraction and dynamic monitoring.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104434"},"PeriodicalIF":7.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428889","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":"A novel approach to retrieving the surface soil freeze/thaw state in the Qinghai-Tibetan Plateau using the seasonality of CYGNSS time series","authors":"Qi Liu ,&nbsp;Shuangcheng Zhang ,&nbsp;Zhongmin Ma ,&nbsp;Xin Zhou ,&nbsp;Tao Wang","doi":"10.1016/j.jag.2025.104428","DOIUrl":"10.1016/j.jag.2025.104428","url":null,"abstract":"<div><div>Soil freeze–thaw (F/T) processes are a typical physical phenomenon on the Qinghai-Tibetan Plateau (QTP), significantly impacting regional climate change and the hydrological cycle. This study presents a Seasonal-Trend Decomposition using Loess and Long Short-Term Memory (STL-LSTM) method to detect spatiotemporal variations in soil F/T on the QTP using time series data from the Cyclone Global Navigation Satellite System (CYGNSS). The model was validated against ERA5 soil temperature data (0–7 cm) and independent in-situ observations, demonstrating good consistency. The SHapley Additive exPlanations (SHAP) model was integrated into the STL-LSTM framework to quantitatively evaluate the contributions of input features to F/T retrieval, revealing that time features contributes the most to retrieval results, followed by surface reflectivity. Moreover, spatiotemporal analysis of QTP F/T dynamics shows prominent seasonal patterns, with topography-induced shielding delaying thawing in central QTP regions and freezing trends extending from low (28°N) to high latitudes (36°N). The proposed method offers a new pathway for monitoring freeze–thaw transitions in high-latitude regions and holds potential for expansion into future high-frequency and multi-polarization GNSS-R missions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104428"},"PeriodicalIF":7.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428848","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":"Higher-density river discharge observation through integration of multiple satellite data: Midstream Yellow River, China","authors":"Qihang Liu ,&nbsp;Yun Chen ,&nbsp;João Paulo L.F. Brêda ,&nbsp;Handi Cui ,&nbsp;Hongtao Duan ,&nbsp;Chang Huang","doi":"10.1016/j.jag.2025.104433","DOIUrl":"10.1016/j.jag.2025.104433","url":null,"abstract":"<div><div>Silty Midstream Yellow River (MYR), characterized by its turbid waters, is currently underserved by a sparse network of gauging stations, which is insufficient for comprehensive flow monitoring. Establishing an extensive gauging network in this region is almost impractical. This study addresses the challenge by estimating discharge at selected ungauged reaches of the MYR, leveraging multiple remote sensing datasets with high spatiotemporal resolutions, complemented by Manning’s Equation. Satellite observation reaches (SORs) were strategically positioned at each small river section between adjacent tributaries, chosen for their variable river width, stable channel terrain, and uniform flow, which are conducive to the application of Manning’s Equation. Hydraulic parameters for 16 SORs were calculated, integrating optical and Synthetic Aperture Radar data with a digital elevation model to derive river width, water surface level, and slope. River bathymetry and bed elevation, not directly observable by satellites, were simulated using an adapted altimetry-assimilated one-dimensional (1D) hydraulic model. The discharge time-series at the SOR locations was subsequently retrieved and validated against observed discharges at existing gauges, demonstrating high accuracy with Nash-Sutcliffe Efficiency values ranging from 0.704 to 0.779 and R² values from 0.773 to 0.925. This study effectively expanded discharge observations at ungauged river reaches, increasing the number of observation sites from three to sixteen and achieving an average monitoring interval of 2.7 days per site. The enhanced river discharge observations facilitated by remote sensing provides more granular water and sediment flux data, which is instrumental for future hydrological research and soil conservation planning within large river basins.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104433"},"PeriodicalIF":7.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428887","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":"Mapping of insect pest infestation for precision agriculture: A UAV-based multispectral imaging and deep learning techniques","authors":"Narmilan Amarasingam ,&nbsp;Kevin Powell ,&nbsp;Juan Sandino ,&nbsp;Dmitry Bratanov ,&nbsp;Arachchige Surantha Ashan Salgadoe ,&nbsp;Felipe Gonzalez","doi":"10.1016/j.jag.2025.104413","DOIUrl":"10.1016/j.jag.2025.104413","url":null,"abstract":"<div><div>In recent years, the precise identification of an insect pest infestation has become increasingly critical for effective management in agricultural fields. This research addresses the imperative need for an advanced and integrated approach to mapping insect pest infestation in agricultural crops, utilising unmanned aerial vehicles (UAVs), multispectral (MS) imagery, and deep learning (DL). The existing literature reveals a limited number of studies that harness the potential of UAV-based MS imagery in conjunction with DL models for mapping and managing insect pest infestations. The primary aim is to enhance the precision and efficiency of insect pest infestation mapping through the synergistic analysis of spectral bands, vegetation indices (VIs), and textural features using DL techniques. The aerial imagery and ground truth information were collected in crop field for mapping of insect pest infestation. The investigation comprised three specific analyses; first is about establishing correlations between insect pest pupal count versus spectral bands and VIs. Second, the performance comparison of three DL models including U-Net, DeepLabV3+, Fully Convolutional Network (FCN) to segment three classes including insect pest infestation patches, other vegetation (weeds), and crops. Finally, the third analysis evaluated the efficacy of textural features against spectral features in mapping an insect pest infestation using DL techniques. The results indicate that, concerning the correlation between pupal count in the field and spectral bands or VIs, the Simple Ratio Index (SRI), and Red Edge Chlorophyll Index (RECI) demonstrated a positive correlation of 0.7, whereas the Green Chlorophyll Index (GCI) displayed a positive correlation of 0.6. Another key finding shows that spectral features outperformed textural features across all DL models for insect pest infestation segmentation. The research highlights the effectiveness of spectral features, particularly with the FCN model, which demonstrated best performance metrics for insect pest segmentation in the study field. The FCN model achieved scores with a precision (P) of 93%, recall (R) of 97%, F1-score (F1) of 95%, and Intersection over Union (IoU) of 90%, underscoring its excellence in accurately identifying and delineating pest infestations in the field. The proposed methodology and its findings offer implications such as enhanced pest surveillance, timely intervention, precision pest management, and optimised resource allocation that can be extended to optimise insect pest infestation mapping in various crop lands, enabling precise control strategies aimed at enhancing crop yield.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104413"},"PeriodicalIF":7.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419187","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":"Plantation forests driven spatiotemporal vegetation trends and its interplay with climate variables in the Northwestern Highlands of Ethiopia","authors":"Bireda Alemayehu ,&nbsp;Juan Suarez-Minguez ,&nbsp;Jacqueline Rosette","doi":"10.1016/j.jag.2025.104411","DOIUrl":"10.1016/j.jag.2025.104411","url":null,"abstract":"<div><div>Plantation forests have been increasingly established in Fagita Lekoma District, located in the Northwestern Highlands of Ethiopia, over the past two decades. However, their interaction with climate variables remains largely unexplored. This study aims to investigate the spatiotemporal dynamics of plantation forests driven vegetation changes and their relationship with climate variables in the district from 2000 to 2020. Moderate Resolution Imaging Spectroradiometer (MODIS) data and Enhanced National Climate Services (ENACTS) data of the study area were processed for 21 years to examine trends using the Mann-Kendall test and Sen’s slope estimator, employing R 4.3 programming software. Concurrently, various climatic data, including evapotranspiration (ET), land surface temperature (LST), and rainfall, were processed, and analysed to explore the relationships between vegetation change and climate variability in the district during the study period. Increasing trends of Normalized Difference Vegetation Index (NDVI) greenness in the district were highlighted, with mean annual NDVI rising from 0.53 to 0.64, showing an average trend of 0.0036 year⁻¹. This increase is primarily attributed to the widespread establishment of plantation forests across the district. The largest increase was revealed in ET, with a rate of 12.27 kg/m² year⁻¹. Conversely, LST and rainfall exhibited insignificant decreasing trends, with a rate of − 0.15 °C year⁻¹ and − 10.54 mm year⁻¹, respectively. Furthermore, a statistically significant positive correlation between NDVI and ET was observed, underscoring the critical role of vegetation in maintaining water availability. The negative correlation between NDVI and LST suggests that increased vegetation cover contributes to land surface temperature cooling. Additionally, the statistically insignificant negative correlation between NDVI and rainfall emphasizes the influence of land use change and human intervention on vegetation productivity. Overall, this study highlights the significance of long-term satellite observations in assessing the intricate interplays between vegetation dynamics driven by plantation forestry and climate variables at a local scale. The findings emphasize the role of plantation forests in enhancing vegetation greenness, improving water regulation, and mitigating land surface temperature warming, providing critical insights for sustainable land management and policy interventions in similar regions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104411"},"PeriodicalIF":7.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428876","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":"Recovery of pixels with extremely turbid waters and intensive floating algae from false cloud masking in satellite ocean color remote sensing","authors":"Menghua Wang ,&nbsp;Lide Jiang","doi":"10.1016/j.jag.2025.104408","DOIUrl":"10.1016/j.jag.2025.104408","url":null,"abstract":"<div><div>We describe our work to improve the cloud masking for satellite ocean color data processing over extremely turbid waters and intensive algae blooms (or floating algae), which are often identified as cloud mistakenly. An improved cloud masking approach is proposed using additional information of the Alternate Floating Algae Index (AFAI) and a new normalized AFAI (nAFAI), as well as ratios of the Rayleigh-corrected reflectance <em>ε</em>^(RC)(<em>λ_i</em>, <em>λ_j</em>) from the blue and near-infrared bands. Specifically, the proposed algorithm adds a recovery procedure after the original cloud masking to retrieve falsely masked pixels and identifies these pixels as turbid waters, floating algae or absorbing aerosols, from which ocean color products can be further derived. The new cloud masking algorithm has been implemented in the NOAA Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system for routine global data processing for various satellite ocean color sensors, e.g., the Visible Infrared Imaging Radiometer Suite (VIIRS), the Ocean and Land Colour Instrument (OLCI), the Geostationary Ocean Color Imager (GOCI), etc. Results show that the new cloud masking has remarkably improved ocean color data coverage, particularly over highly turbid coastal and inland waters, as well as intensive floating algae, eliminating almost all false cloud pixels. For example, using the new cloud masking algorithm, the falsely masked pixels are recovered, reducing cloud masking pixels by ∼30–40% and ∼40–50% over highly turbid China East Coast and China’s Lake Taihu, respectively.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104408"},"PeriodicalIF":7.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419358","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":"Enhancing outdoor long-distance matching in mobile AR: A continuous and real-time geo-registration approach","authors":"Kejia Huang ,&nbsp;Di Liu ,&nbsp;Sisi Zlatanova ,&nbsp;Yue Lu ,&nbsp;Yiwen Wang ,&nbsp;Taisheng Chen ,&nbsp;Yue Sun ,&nbsp;Chenliang Wang ,&nbsp;Daniel Bonilla ,&nbsp;Wenjiao Shi","doi":"10.1016/j.jag.2025.104422","DOIUrl":"10.1016/j.jag.2025.104422","url":null,"abstract":"<div><div>Geo-registration is a fundamental process seamlessly integrating digital information within the physical world in Mobile Augmented Reality (MAR). Achieving high precision, real-time capability, and strong adaptability in geo-registration is crucial for the effective functioning of MAR applications, especially in outdoor environments. However, existing methods frequently struggle with inaccuracies in long-distance positioning and latency of pose estimation, compounded by their sensitivity to scale changes of outdoor environment. This study addresses these challenges by proposing a novel continuous and real-time MAR geo-registration method for outdoor applications. Our approach integrates real-time kinematic Global Navigation Satellite System (RTK-GNSS) fusion with geodesic equations and rotation invariance estimation. This method substantially surpasses traditional methods, achieving 0.05 m virtual-real position accuracy (approximately six times better) and under 0.2° pose accuracy (nearly a fivefold improvement). Additionally, it exhibits superior robustness in complex MAR scenarios. Beyond improved accuracy, this method reduces the reliance on high-quality sensor hardware and precise calibration, making it suitable for various AR systems, including smartphones and tablets.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104422"},"PeriodicalIF":7.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428849","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":"Grace-based assessment of hydrometeorological droughts and their Possible teleconnection Mechanisms using wavelet based quantitative approach","authors":"Olfa Terwayet Bayouli ,&nbsp;Wanchang Zhang ,&nbsp;Houssem Terwayet Bayouli ,&nbsp;Zhijie Zhang ,&nbsp;Qianying Ma","doi":"10.1016/j.jag.2025.104410","DOIUrl":"10.1016/j.jag.2025.104410","url":null,"abstract":"<div><div>Climate change and recurrent extreme climatic events have intensified the vulnerability of water-stressed regions like Tunisia to droughts, severely impact agriculture, the economy, and society. This study analyzes hydro-meteorological drought patterns using the Gravity Recovery and Climate Experiment (GRACE) satellite-derived Groundwater Drought Index (GGDI), alongside traditional indices, including Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), and Standardized Runoff Index (SRI). A stochastic analysis of monthly SPEI-GGDI values was conducted using a first-order Markov chain model, to investigate regional drought hazards formation, persistence, and evolution. Pearson’s correlation coefficient and wavelet coherence were applied to evaluate interactions among indices and their teleconnections with large-scale climate patterns. Results reveal persistent droughts, with extreme events exhibiting high stability and low recovery probabilities. The most severe groundwater drought occurred in 2014–2015, averaging a GGDI value of −1.36, while 2002–2003 was the driest based on SPEI, SPI, and SRI, averaging −1.9. Correlation analysis highlights complex interactions between meteorological and hydrological droughts, with GDDI-identified droughts exhibit greater severity in frequency, intensity, and duration, indicating significant anthropogenic influence. El Niño-Southern Oscillation (ENSO) significantly influenced drought evolution, with intense negative phases exacerbating severity.<!--> <!-->This study highlights the potential of GRACE satellite data for integrated drought monitoring and provides novel insights for developing sustainable drought management strategies in Tunisia.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104410"},"PeriodicalIF":7.6,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419186","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}