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

{"title":"Field-scale water use assessment in the Lower Mississippi Alluvial Plain using OpenET","authors":"Weina Duan ,&nbsp;Yun Yang ,&nbsp;Martha C. Anderson ,&nbsp;Christopher R. Hain ,&nbsp;Forrest S. Melton ,&nbsp;John M. Volk ,&nbsp;Kyle R. Knipper","doi":"10.1016/j.jag.2026.105172","DOIUrl":"10.1016/j.jag.2026.105172","url":null,"abstract":"<div><div>Water scarcity and unprecedented rapid depletion of groundwater reserves are challenges for global sustainable agricultural development. Understanding water usage is especially critical in key agricultural production regions. In this study, we focus on the Lower Mississippi Alluvial Plain (LMAP) region, one of the most important intensive agricultural regions in the United States and facing severe groundwater depletion. However, the total consumptive water use and its relationships with drought events across different land cover types remain a key unknown in this region. Here we use recently developed field-scale monthly evapotranspiration (ET) data from OpenET to investigate water use dynamics over multiple years under various drought conditions. Water use patterns for soybeans, corn, cotton, rice, and surrounding forests are analyzed, with drought impacts assessed using the U.S. Drought Monitor (USDM). The results show that the water consumption for all four crops peaks in July, but early-season patterns differ by crop type and water use practices. Rice shows the highest average total ET for the growing season (688 mm), while cotton has the lowest (612 mm). In comparison, forest ET is higher than crop ET in the LMAP and also shows a seasonal peak in July. Crops show higher variability in ET than forests during the growing season, with larger differences in standard deviation in drought years. Across the three mid-drought years during the study period, groundwater consumption by the four major crops exceeded that of forests by an average of 10⁹ m³ per growing season. Anomalies in ET normalized by reference ET (fRET), a metric of evaporative stress, exhibited rapid response to drought as USDM drought severity intensified, demonstrating the potential of remote-sensing ET metrics for early drought detection. This study utilizes the OpenET dataset to analyze vegetation water use patterns at field scale (30 m), highlighting its value for detailed, spatially explicit monitoring of crop water dynamics and drought impacts and providing critical information for regional water accounting for the development of sustainable agriculture and effective water resources management in agriculture-intensive and drought impacted regions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"147 ","pages":"Article 105172"},"PeriodicalIF":8.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147278247","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":"Improving fine-grained population distribution prediction by considering region-distinctive geographical factors-A case of Pearl River Delta, China","authors":"Ku Gao ,&nbsp;Xiaomei Yang ,&nbsp;Yueming Liu ,&nbsp;Qingyang Zhang ,&nbsp;Zhihua Wang","doi":"10.1016/j.jag.2026.105168","DOIUrl":"10.1016/j.jag.2026.105168","url":null,"abstract":"<div><div>Predicting fine-grained population distribution is crucial for effective urban planning. However, existing models widely ignore Region-Distinctive Geographical Factors (RDGF) in regional population modeling. This omission may compromise prediction accuracy, particularly in coastal zones where over 50% of the global population. To address this gap, we proposed an RDGF-incorporated approach for fine-grained population prediction, using the coastal Pearl River Delta as a case study. Leveraging multi-source geospatial data, based on generalized geographical factors (GGF) (e.g., topography, POI density, nighttime light intensity, etc.), we supplemented multi-dimensional RDGF including ecology, agriculture and transportation, etc. derived from unique regional environments (e.g., distance to shoreline, aquaculture, ports, etc.). We employed an interpretable machine learning framework (Random Forest + SHAP) to model and explain factor contribution. Results demonstrate: (1) incorporating RDGF substantially improves prediction accuracy in both model performance (with the average R² increasing by 6% under spatial cross-validation) and output (The relative error in densely populated areas can be reduced by up to 40%), thereby providing opportunity for more effective infrastructure planning and disaster risk management. (2) GGF still make the primary contribution to the model; however, RDGF are able to reveal local spatial heterogeneity and geographic decay patterns in population distribution, demonstrating greater potential for reducing prediction errors. This study provides region-specific insights for generating large-scale, fine-grained population map.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"147 ","pages":"Article 105168"},"PeriodicalIF":8.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147278289","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":"DeVCL: An end-to-end degradation-aware framework for vehicle counting and localization in satellite imagery","authors":"Ziqian Tan,&nbsp;Chen Wu","doi":"10.1016/j.jag.2026.105197","DOIUrl":"10.1016/j.jag.2026.105197","url":null,"abstract":"<div><div>Vehicle counting and localization using high-resolution satellite imagery have recently demonstrated substantial value in urban management and public services. However, satellite images routinely suffer from degradation issues, including blurring, insufficient resolution, noise, uneven illumination, and occlusion, due to sensor limitations, weather conditions, compression artifacts, and other environmental factors. These quality issues severely degrade the stability and accuracy of traditional vehicle counting and localization methods. To address this challenge, we propose Degradation-aware Vehicle Counting and Localization (DeVCL), a novel end-to-end point regression framework explicitly designed for degraded satellite imagery, which adaptively recognizes image degradation conditions and directly predicts vehicle positions. Specifically, DeVCL uses a self-supervised degradation representation jointly with image quality assessment to guide a degradation-aware feature modulation module, enhancing feature representations for low-quality inputs. We also introduce a feature-level adversarial mechanism without paired supervision to strengthen feature robustness. In addition, a density-sensitive feature refinement module is proposed to address matching ambiguities caused by densely packed and arranged vehicles, thus improving localization performance. We evaluated DeVCL using two synthetic degraded datasets built on FAIR1M_V and ITCVD, along with a newly collected dataset named SatPark that features high vehicle density and includes multiple naturally occurring degradations. Experimental results indicate that DeVCL consistently outperforms existing methods, particularly in SatPark, demonstrating strong generalization and practical adaptability.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"147 ","pages":"Article 105197"},"PeriodicalIF":8.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360638","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":"GAGNN: a geography-aware graph neural network for citywide commuting flows prediction","authors":"Youjun Tu ,&nbsp;Peixiao Wang ,&nbsp;Julie N.Y. Zhu ,&nbsp;Zhiyuan Zhao ,&nbsp;Junli Li ,&nbsp;Sheng Wu","doi":"10.1016/j.jag.2026.105175","DOIUrl":"10.1016/j.jag.2026.105175","url":null,"abstract":"<div><div>Urban commuting flow prediction is crucial for optimizing public transportation and improving efficiency, yet traditional models often focus on geographic adjacency, overlooking the complex cross-regional interactions within transportation networks. To address this, we propose a <strong>G</strong>eography-<strong>A</strong>ware <strong>G</strong>raph <strong>N</strong>eural <strong>N</strong>etwork (GAGNN) model for commuting flow prediction. The model first jointly encodes the geographic adjacency matrix and semantic adjacency from public transportation networks, developing a comprehensive attention mechanism to fuse regional proximity with cross-regional semantic connectivity. Subsequently, a Graph Attention Network (GAT) is employed to embed the multiple adjacency relations and multi-source geographic knowledge. Finally, graph embeddings are combined with spatial factors into multidimensional feature vectors, fed into an MLP for commuting flow prediction. The model was validated with Fuzhou workday mobile phone data from January to February 2023, assessing the impact of semantic adjacency from different transportation networks on performance. The results show that: (1) We proposed the GAGNN outperforms both traditional models and advanced graph neural network models (e.g., GSGNN), reducing MAE by 14.9% and improving CPC by 2.1%; (2) The type of semantic adjacency significantly impacts model prediction accuracy. Road-based semantic connections perform best, especially for long-distance commuting flows, followed by metro and bus semantic connections, while the absence of semantic connections yields the worst performance. (3) Spatial scale significantly affects model prediction performance. Under road-based semantic adjacency, accuracy slightly declines with increasing scale, whereas metro, bus, and non-semantic connections, prediction accuracy improves. These findings offer effective support for accurate regional commuting flow modeling and public transportation networks optimization.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"147 ","pages":"Article 105175"},"PeriodicalIF":8.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147278199","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":"Arctic navigation risk assessment using ICESat-2 and GIS-Based AHP","authors":"Sang-Hoon Lee ,&nbsp;Hong-Sik Yun ,&nbsp;Seung-Jun Lee","doi":"10.1016/j.jag.2026.105150","DOIUrl":"10.1016/j.jag.2026.105150","url":null,"abstract":"<div><div>The Arctic Ocean is among the world’s most hazardous maritime regions, where sea ice, uncharted bathymetry, and limited emergency infrastructure threaten vessel safety. Recent advances in remote sensing now provide critical spatial data that can mitigate these risks. This study proposes a GIS-based framework for monthly Arctic navigation risk assessment by integrating sea ice, bathymetry, satellite communication, and accessibility to rescue and evacuation infrastructure. Using Spatial Multi-Criteria Evaluation (SMCE) with Analytic Hierarchy Process (AHP), risks were evaluated from September 2022 to April 2023 with ICESat-2 observations. Five vessel classes were analyzed, from non-icebreaking ships to those with maximum icebreaking capacities of 1.0 m, 1.5 m, 2.0 m, and 2.8 m. The resulting maps delineate monthly risk zones and reveal spatial and temporal variability across the Arctic. These outputs provide decision support for safer routing, operational preparedness, and policy development. The framework also demonstrates practical relevance for e-Navigation systems and advances methodological approaches to maritime risk assessment.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"147 ","pages":"Article 105150"},"PeriodicalIF":8.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147278291","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":"Salinity-induced global pattern of atmospheric water constraints on mangrove photosynthetic activity revealed by time series Sentinel-2 data","authors":"Yanjie Liu ,&nbsp;Yueting Deng ,&nbsp;Hui Luo ,&nbsp;Nengwang Chen ,&nbsp;Yougan Chen ,&nbsp;Zhenong Jin ,&nbsp;Xu Wang ,&nbsp;Hongsheng Zhang ,&nbsp;Xudong Zhu","doi":"10.1016/j.jag.2026.105170","DOIUrl":"10.1016/j.jag.2026.105170","url":null,"abstract":"<div><div>Atmospheric drought stress limits mangrove photosynthetic activity, and this constraint can be further amplified by high salinity, yet their combined global effects remain poorly understood. Here, we integrated multi-source Earth observation and geoinformation datasets, including Sentinel-2 red-edge position (a proxy for canopy photosynthetic activity), vapor pressure deficit from TerraClimate, seawater salinity from Copernicus reanalysis, to investigate how salinity regulates the sensitivity of mangrove photosynthesis to atmospheric drought stress during 2019–2023. Datasets were harmonized and analyzed through reproducible geoinformation workflows at 10 m–0.5° resolutions, enabling large-scale coupling analyses between remote sensing proxies and climate drivers. We found that drought stress constrained mangrove photosynthetic activity worldwide, with stronger limitations in tropical savannahs than in tropical rainforests. Marine mangroves exposed to persistent high salinity were more sensitive than estuarine mangroves influenced by freshwater inflow. These results reveal a global pattern in which salinity amplifies atmospheric water constraints on mangrove photosynthesis. Mangroves in dry climates and high-salinity habitats are therefore most vulnerable to future warming and drying. Our findings confirm that integrating multi-source satellite observations with geoinformation analysis provides an effective, large-scale approach for assessing vegetation vulnerability and identifying conservation priorities in climate-sensitive mangrove ecosystems.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"147 ","pages":"Article 105170"},"PeriodicalIF":8.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209250","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":"Explainable artificial intelligence reveals heterogeneous erosion responses to extreme rainfall: A new framework for conservation prioritization","authors":"Dongling Ma ,&nbsp;Shuangyun Peng ,&nbsp;Zhiqiang Lin ,&nbsp;Jiaying Zhu ,&nbsp;Xianchun Pan ,&nbsp;Yuanmei Jiao ,&nbsp;Ziyi Zhu ,&nbsp;Shuangfu Shi ,&nbsp;Biting Cui ,&nbsp;Rong Jin","doi":"10.1016/j.jag.2026.105181","DOIUrl":"10.1016/j.jag.2026.105181","url":null,"abstract":"<div><div>Effectively combating soil erosion under intensifying extreme precipitation requires moving beyond broad-scale assessments to precision-targeted interventions. Current approaches often fail to capture the differentiated erosion responses across diverse landscapes, limiting the efficacy of conservation investments. Here, we introduce a novel framework that combines modeling (RUSLE) with Explainable artificial intelligence (LightGBM model with SHAP method) and multi-criteria decision analysis to prioritize soil and water conservation (SWC) efforts. Applied to 20 SWC zones in the ecologically critical Yunnan Province, China, our analysis reveals that maximum 5-day precipitation (RX5) is the paramount driver of erosion, superseding other precipitation metrics. We uncover distinct regional response mechanisms: erosion in northwestern cold alpine zones is governed by cumulative rainfall, while erosion in temperate and tropical zones is triggered by short-duration, high-intensity events. This mechanistic understanding enabled the robust identification of the Northwest Alpine Gorge and Western Broad Valley zones as highest-priority areas demanding urgent action. By systematically diagnosing the primary climatic drivers and identifying the most vulnerable regions, our framework provides a powerful, replicable blueprint for optimizing conservation resources and enhancing climate resilience in mountainous ecosystems globally.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"147 ","pages":"Article 105181"},"PeriodicalIF":8.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147278340","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":"Detection and accuracy of a geomorphic Proxy-Based shoreline indicator in PlanetScope imagery","authors":"Joshua T. Kelly,&nbsp;Suvam Patel","doi":"10.1016/j.jag.2026.105173","DOIUrl":"10.1016/j.jag.2026.105173","url":null,"abstract":"<div><div>Satellite-derived shoreline mapping is a common technique for quantifying geomorphic shoreline change. However, shoreline positions derived using moderate-resolution data have questionable accuracy, are influenced by metocean conditions, and are typically based on the boundary of a binarized spectral index rather than a visible geomorphic indicator, such as the high-water line (HWL). PlanetScope (PS) can visualize the location of the HWL by detecting the spectral reflectance differences between wet and dry sediment along a sandy beach surface due to its improved spatial resolution of 3 m/pixel. The accuracy of nine HWL proxy shoreline positions is assessed by comparison to a contemporaneous mean high water (MHW) shoreline delineated across Moro Beach, CA, using a digital elevation model created from RTK GPS-corrected Unmanned Aircraft System imagery. The offset between the PS-derived HWL and UAS-derived MHW positions (Δd) was measured every 10 m in the alongshore direction using the Digital Shoreline Analysis System for each HWL dataset. A significant exponential relationship was observed between Δd and the tide height at the time of PS image acquisition, whereby the HWL shoreline was located further landward (seaward) during higher (lower) tides. The HWL shoreline, when acquired at or near low tide, was spatially coincident with the MHW shoreline, the most reliable yet cost-prohibitive shoreline proxy. PlanetScope’s advancement in spatiotemporal resolution introduces a new approach to satellite-derived shoreline mapping, one that is based on a geomorphic proxy position and a minimized influence of tide heights.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"147 ","pages":"Article 105173"},"PeriodicalIF":8.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147278288","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":"Evaluating superpixel algorithms for standing dead tree delineation using aerial orthoimagery","authors":"Igor Pawelec,&nbsp;Paweł Hawryło,&nbsp;Paweł Netzel,&nbsp;Jarosław Socha","doi":"10.1016/j.jag.2026.105180","DOIUrl":"10.1016/j.jag.2026.105180","url":null,"abstract":"<div><div>High-resolution remote sensing data are essential for monitoring forest health and detecting changes such as tree mortality. This study evaluates low-level segmentation methods for delineating standing dead trees (SDTs) using widely available true-color (RGB) aerial orthoimagery, independent of near-infrared (NIR) or LiDAR data. The analysis was conducted across eight forest sites in northern Poland, dominated by coniferous species such as Scots pine (<em>Pinus sylvestris</em> L.) and Norway spruce (<em>Picea abies</em> L.).</div><div>Four representative superpixel-based algorithms were tested — Simple Linear Iterative Clustering (SLIC), its zero-parameter variant (SLIC0), scale-adaptive superpixels (adaptels), and a spatially regularized watershed transform (waterpixels). All methods represent preprocessing approaches designed to reduce image complexity and preserve meaningful spectral–spatial structures prior to object-based image analysis (OBIA). In addition, the impact of converting imagery to the perceptually uniform CIELAB color space was assessed to enhance spectral separability and reduce illumination effects. Segmentation accuracy was evaluated against a manually verified reference dataset of 1,200 SDT crowns using multiple quality metrics.</div><div>The results indicate that the adaptels algorithm, particularly when combined with the CIELAB color transformation, achieved the most balanced performance across all evaluation metrics, defined by a simultaneous reduction of segmentation fragmentation and boundary generalization errors while maintaining high overall detection accuracy. This combination proved to be an efficient and cost-effective solution for SDT segmentation using standard RGB orthophotos. The findings highlight the potential of perceptually uniform color transformations as practical tools for scalable, reproducible, and low-cost forest monitoring. The study also provides a reference database of standing dead trees to support further research and future integration with deep learning-based detection frameworks.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"147 ","pages":"Article 105180"},"PeriodicalIF":8.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147278290","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":"Spectral-Feature-Driven photovoltaic Detection: A universal Physics-Based index for rapid Localization","authors":"Shuang He ,&nbsp;Qingjiu Tian ,&nbsp;Jia Tian ,&nbsp;Lina Hao","doi":"10.1016/j.jag.2026.105164","DOIUrl":"10.1016/j.jag.2026.105164","url":null,"abstract":"<div><div>Photovoltaic (PV) energy is critical to the transition towards a net-zero economy and plays a vital role in meeting the Sustainable Development Goals (SDGs), particularly regarding affordable clean energy (SDG 7) and climate action (SDG 13). Timely and accurate acquisition of the spatial distribution of PV installations is critical for regional energy planning, capacity estimation, and policy adjustment. However, accurately detecting PV installations remains challenging due to their environmental complexity and structural diversity. Through multi-platform spectral analysis (including Sentinel-2, Landsat-8, and GF-2 imagery), this study identifies distinctive spectral reflectance properties of PV materials, characterized by a prominent peak in the 400–500 nm range and significantly lower reflectance in the visible to near-infrared spectrum compared to natural landscapes, while exhibiting higher reflectance than water bodies. Leveraging physics-based spectral signatures that remain consistent across diverse geographical settings, we introduce the Spectral Ratio-Normalized Difference Solar Photovoltaic Panel Index (SPPI), a universal approach for efficient PV detection using optical satellite imagery. Quantitative validation across multiple regions (urban, rural, and mountainous environments) demonstrates that SPPI achieves exceptional performance with 94.34% overall accuracy and a robust Kappa coefficient of 0.778, outperforming existing index-based methodologies while producing results comparable to more computationally intensive deep learning approaches. The SPPI methodology’s distinctive advantage lies in its ability to generate precise PV polygon boundaries while maintaining computational efficiency, enabling rapid large-scale mapping without specialized hardware requirements. While installation variations and extreme viewing angles may affect performance, the physics-based nature of the index ensures consistent results under normal imaging conditions. This universal, computationally efficient approach facilitates effective PV installation monitoring and energy capacity estimation, enhancing renewable energy analytics for carbon neutrality initiatives.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"147 ","pages":"Article 105164"},"PeriodicalIF":8.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360590","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}