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

{"title":"Desertification expansion significantly suppresses photosynthetic peak capacity of arid ecosystems at the global scale","authors":"Kaiyang Qiu ,&nbsp;Qingbin Zhang ,&nbsp;Yingzhong Xie ,&nbsp;Mingjie Shi ,&nbsp;Chengyun Wang ,&nbsp;Tong Dong ,&nbsp;Jun Ma ,&nbsp;Panxing He","doi":"10.1016/j.jag.2025.105042","DOIUrl":"10.1016/j.jag.2025.105042","url":null,"abstract":"<div><div>Arid ecosystems occupy about two-fifths of the global land surface, and fluctuations in their productivity play a pivotal role in global carbon sequestration and ecosystem service provision. However, the global-scale effect of desertification expansion on the annual maximum photosynthetic peak has not yet been systematically quantified. In this study, 30-m high-resolution desert cover data (GLCLUC) and multi-source remote-sensing photosynthetic indicators were integrated, using a space-for-time substitution framework to establish a global desertification scenario classification system. We quantitatively evaluated the influence of diverse desert expansion and contraction scenarios on the ecosystem photosynthetic peak (GPP_max). Results indicate that the average GPP_max in high-intensity expansion regions (HIEs) is 8.23 g C m⁻² 8d⁻¹, whereas medium- to low-intensity expansion regions (MIEs) show a value of 8.95 g C m⁻² 8d⁻¹. By contrast, medium- to low-intensity contraction regions (MIRs) and high-intensity contraction regions (HIRs) demonstrate markedly higher GPP_max values of 10.64 g C m⁻² 8d⁻¹ and 17.64 g C m⁻² 8d⁻¹, respectively. Regarding the photosynthetic peak difference (ΔGPP_max), expansion scenarios (HIEs, MIEs) significantly decrease ecosystem photosynthetic potential, with average ΔGPP_max reductions of 1.19–3.95 g C m⁻² 8d⁻¹ relative to contraction scenarios (HIRs, MIRs). The most pronounced losses occur in South America, North America, and Eurasia, with South America exhibiting reductions exceeding 6 g C m⁻² 8d⁻¹. Additionally, ecosystems with initially higher photosynthetic potential experience greater GPP_max declines under intense desert expansion. This study provides the first global-scale evidence revealing how different desertification pathways modify ecosystem photosynthetic peaks and their regional disparities, offering critical scientific support for ecological restoration, carbon sequestration strategies, and land management across arid landscapes.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105042"},"PeriodicalIF":8.6,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791193","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":"High–low frequency feature fusion network for pavement crack segmentation in complex environments","authors":"Huazhong Jin,&nbsp;Guofeng Wu,&nbsp;Yi Cen","doi":"10.1016/j.jag.2025.105048","DOIUrl":"10.1016/j.jag.2025.105048","url":null,"abstract":"<div><div>For routine road maintenance, accurate and efficient crack detection holds significant research value and practical importance. With advances in computer vision, image processing-based crack detection has achieved promising results. However, in complex environments, factors such as illumination and weather introduce noise interference, especially in high-resolution crack images where fine fracture features show heightened complexity and diversity. This often leads to degraded detection accuracy in such challenging scenarios. To address this, we propose FE-SegNeXt, a convolutional neural network segmentation framework tailored for high-resolution crack detection in complex environments. The algorithm leverages distinct characteristics of high- and low-frequency features in crack images: average pooling separates these features, while a dedicated Frequency Collaborative Enhancement Module (FCEM) independently enhances high- and low-frequency components before fusing multi-band information. This design significantly improves the model’s ability to extract subtle cracks from high-resolution images under noisy conditions. Additionally, we introduce a Locally Enhanced Feed-Forward Network (LE-FFN) to amplify perception of weak crack signals in local regions, further refining fine-grained feature extraction. Experimental results on the public datasets Sun520, Rain365, and BJN260 demonstrate that the proposed method achieves F1-scores of 61.55%, 62.57%, and 60.31%, respectively, outperforming existing crack detection algorithms.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105048"},"PeriodicalIF":8.6,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791191","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":"Climatic effects on U.S. agricultural productivity: Evidence and prediction from total factor productivity and crop yields","authors":"Ruixin Yang ,&nbsp;Sun Ling Wang ,&nbsp;Qian Liu ,&nbsp;Mengfei Xin","doi":"10.1016/j.jag.2025.105028","DOIUrl":"10.1016/j.jag.2025.105028","url":null,"abstract":"<div><div>Climate conditions such as the dynamics of temperature and precipitation significantly influence agricultural production. Therefore, the climate-induced agricultural economic consequences cannot be neglected. This study employs machine learning models with time series climate data, a panel of state-by-year total factor productivity estimates (1960–2004) for 48 contiguous states of the United States (U.S.), and the yield data for corn and soybean (1960–2020) in the main producing states to quantify the potential climatic effects on U.S. agricultural productivity under different climate scenarios. The results show that the climate change impacts are spatially heterogeneous under the Representative Concentration Pathway (RCP) 4.5 and 8.5 climate change scenarios from 2020 to 2050. However, the effects on future agricultural productivity are consistently negative regardless of the region. The negative climatic effects on productivity growth will offset the advancements of U.S. agricultural productivity after 5–25 years under various climate change scenarios assuming the same advancement rates as the past. The predictions of the impacts could advance our understanding of future productivity growth in U.S. agriculture under climate change. The analysis results could also shed light on policy planning to mitigate various effects and threats to food security due to the changing climate.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105028"},"PeriodicalIF":8.6,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791189","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":"Satellite-observed acceleration of glacier velocity on the Antarctic Peninsula in response to climate warming","authors":"Yulong Kang ,&nbsp;Shichang Kang ,&nbsp;Bryan Riel ,&nbsp;Tanuj Shukla ,&nbsp;Tao Che ,&nbsp;Zongli Jiang ,&nbsp;Zhaoqiang Zhou ,&nbsp;Qiangqiang Xu ,&nbsp;Zhenfeng Wang ,&nbsp;Wanqin Guo","doi":"10.1016/j.jag.2025.105035","DOIUrl":"10.1016/j.jag.2025.105035","url":null,"abstract":"<div><div>The Antarctic Peninsula (AP) is experiencing rapid mass losses due to global warming in recent decades. Understanding high-resolution seasonal ice speed variability is essential for uncovering the spatiotemporal dynamics of glaciers and ice shelves and their response to ongoing climate change. Here, we present Sentinel-1 satellite-based observations of seasonal flow velocity changes in 581 glaciers on the AP during the period 2018–2023. We find that the glacier surface velocities exhibit an acceleration since the 21st century, with this trend intensifying substantially in recent years. Summer velocity acceleration was particularly pronounced in 2018–2023, with an average increase of 6.22 ± 5.90 % while annual speed increased by &gt; 1.0 %. The summer acceleration of glacier flow was most notable on the Larsen Ice Shelf. Western AP glaciers showed higher velocities and experienced more rapid acceleration than those in the eastern. These changes are primarily driven by warming ocean waters and intensified surface melt. As climate warming persists, the observed acceleration of glacier flow and associated ice shelf mass loss on the AP are projected to persist, depending on the rate and magnitude of future forcing.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105035"},"PeriodicalIF":8.6,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791188","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":"A spatially-informed interpretable deep learning framework for high-resolution nutrient monitoring in complex coastal waters","authors":"Yiqiang Hu ,&nbsp;Weikang Zhan ,&nbsp;Qingyou He ,&nbsp;Yunchen Liu ,&nbsp;Haigang Zhan","doi":"10.1016/j.jag.2025.105025","DOIUrl":"10.1016/j.jag.2025.105025","url":null,"abstract":"<div><div>Satellite monitoring of non-optically active nutrients remains an ongoing challenge due to their weak spectral signals. While artificial intelligence (AI) methods can improve estimates by incorporating spatiotemporal information, their dependence on simple geographic coordinates tends to oversimplify the complex dynamics of nutrient distribution in coastal waters, especially in data-sparse regions. To address this gap, we propose the Spatiotemporal Kernel Density Estimation Deep Neural Network (ST-KDE-DNN). This model leverages precomputed spatial influence fields, derived from river outlet locations using KDE, as direct inputs to establish a source-driven spatial prior that reflects complex mixing patterns. We applied the ST-KDE-DNN framework to retrieve Dissolved Inorganic Nitrogen (DIN) and Phosphorus (DIP) from Landsat-8/9 imagery in the Pearl River Estuary (PRE), a typical multi-outlet coastal system. Compared with models incorporating standard spatiotemporal predictors, incorporating the KDE achieved significantly higher precision, with a reduced Root Mean Square Error (RMSE) of 45% for DIN and 29% for DIP, respectively. The most prominent improvements occurred in complex mixing zones where river plumes overlap. Furthermore, Explainable AI (XAI) analysis confirmed that the KDE features were dominant predictors, which enhances model transparency and grounds its predictions in physical processes. Based on this framework, we generated high-resolution time series of DIN and DIP for the PRE, and further revealed their seasonal and inter-annual variabilities. The ST-KDE-DNN offers a scalable, physically-informed, and interpretable solution for advancing nutrient monitoring in complex estuarine and coastal systems worldwide.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105025"},"PeriodicalIF":8.6,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791195","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":"Examining the spatial configuration of street junctions across cities: a fractal approach on intra-urban clusters","authors":"Wei Zhu ,&nbsp;Ding Ma ,&nbsp;Biao He ,&nbsp;Xiaomin Lu ,&nbsp;Yebin Chen ,&nbsp;Jing Ying ,&nbsp;Chengpeng Li ,&nbsp;Lin Li ,&nbsp;Renzhong Guo","doi":"10.1016/j.jag.2025.105021","DOIUrl":"10.1016/j.jag.2025.105021","url":null,"abstract":"<div><div>Urban streets form the backbone of cities, and variations in street-junction spatial configuration provide valuable insights into urban form and function, both of which exhibit fractal and scaling characteristics in their structural and statistical properties. However, these patterns emerge from the interaction of multiple spatial processes operating at different scales, making it difficult to capture and compare their form-function relationships using single-scale analyses. To address this challenge, this study proposes a new α–β framework for multi-scale urban form comparison, grounded in fractal theory. Using a progressive clustering method, we construct scaling profiles for multiple cities, deriving the α (form) and β (function) parameters to systematically compare urban structures with differing morphologies and to identify urban clusters at their characteristic scales. This approach transforms the subjective perception of morphology into an actionable analytical tool, enabling integrated form-function assessment and overcoming the challenges of comparative studies from different urban systems. Overall, the proposed α–β framework effectively deconstructs urban complexity, as evidenced by its application to a comparative analysis of nine global metropolises.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105021"},"PeriodicalIF":8.6,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791190","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 automated method for estimating the depth of melt ponds using ICESat-2 LiDAR point cloud data: application to surface melt of Arctic sea ice","authors":"Xiaoyi Shen ,&nbsp;Haili Li ,&nbsp;Chang-Qing Ke","doi":"10.1016/j.jag.2025.105033","DOIUrl":"10.1016/j.jag.2025.105033","url":null,"abstract":"<div><div>The depth of melt ponds upon Arctic sea ice is a critical indicator for understanding the state of sea ice and the progression of climate change. The advent of the ICESat-2 lidar altimeter offers a promising avenue to monitor melt ponds, and existing studies are capable of detecting melt pond location and depth. Here we propose a novel approach that incorporates ICESat-2 photon positioning and elevation uncertainties. By converting the photon elevation distribution along the satellite track into a two-dimensional image where horizontal and vertical axes represent track distance and elevation, we employ image classification techniques to distinguish the surface and bottom of melt ponds. This provides a fully automated method for identifying melt ponds, and then the estimation of their depths. Converting photon information into images effectively simplifies the computation. We tested this method on 100 randomly distributed melt pond samples of varying sizes and depths. Out of these, 97 melt ponds were successfully identified. A comparison with manually annotated data revealed an average absolute bias of 5 cm and a correlation coefficient of 0.86, outperforming other methods. This approach can detect large and deep melt ponds with widths greater than 17 m and depths ranging from 0.3 to 2 m, which can be further used to monitor the melt ponds in the whole Arctic. It facilitates the acquisition of more detailed melt pond depth information, which is crucial for quantifying the surface melt of Arctic sea ice.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105033"},"PeriodicalIF":8.6,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791197","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":"RRDPM: Relay residual diffusion probabilistic model for global typical land and seabed DEM super-resolution","authors":"Jianbing Chen ,&nbsp;Ziyin Wu ,&nbsp;Fanlin Yang ,&nbsp;Mingwei Wang ,&nbsp;Xianhai Bu ,&nbsp;Xiaodong Cui ,&nbsp;Jihong Shang ,&nbsp;Dineng Zhao ,&nbsp;Jieqiong Zhou ,&nbsp;Yang liu","doi":"10.1016/j.jag.2025.105031","DOIUrl":"10.1016/j.jag.2025.105031","url":null,"abstract":"<div><div>Global digital elevation models (DEMs) play a crucial role in modern scientific research and human activities. However, acquiring comprehensive elevation data at global scale remains challenging, particularly for oceanic regions, with the current open-access global open DEM resolution limited to 15 arc-seconds (approximately 450 m). In order to further improve the global DEM resolution, we propose an innovative Relay Residual Diffusion Probabilistic Model (RRDPM) for DEM super-resolution tasks based on diffusion model principles. Our model achieves distribution transformation between high- and low-resolution DEMs through tens of steps. Moreover, RRDPM introduces two types of inherently indivisible sampling equations, and we prove that each of them has different advantageous positions, and integrate these advantageous positions using a “relay race” approach. In addition, we discard the conventional downsampling-super-resolution paradigm, by utilizing paired datasets of global 15 arc-second and local 3 arc-second DEMs, so that our method can be trained and tested in real-world situations. The experimental results show that the RRDPM reduces the RMSE by 20.73 % and 22.77 % for land and seabed regions, respectively, and improves the PSNR by 2.23 dB and 2.34 dB, respectively, for the same areas, representing significant advancements in the current global DEM detail recovery. The code of RRDPM is available at <span><span>https://github.com/SuperChen100/RRDPM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105031"},"PeriodicalIF":8.6,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791196","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":"Moving vehicles tracking from satellite video data based on spatiotemporal high-order relation learning and reasoning","authors":"Ziyuan Feng ,&nbsp;Xianfeng Zhang ,&nbsp;Bo Zhou ,&nbsp;Miao Ren ,&nbsp;Xiaobo Zhi","doi":"10.1016/j.jag.2025.105015","DOIUrl":"10.1016/j.jag.2025.105015","url":null,"abstract":"<div><div>Tracking moving vehicles in satellite videos presents several challenges, including complex background interference and the difficulty of detecting small targets. Most existing multiple object tracking (MOT) methods utilize convolutional models to capture local semantics or self-attention mechanisms to address global semantics for moving target detection. However, these methods tend to struggle with small and visually similar targets, making them particularly vulnerable to complex background interference, which often results in a large number of false positives and missed detections. Furthermore, many current approaches rely on the Hungarian matching algorithm or other intricate, unlearnable association optimization methods to achieve effective tracking once relevant information is gathered. This reliance often yields suboptimal outputs from the network models. To tackle these issues, this article presents an end-to-end graph network based on spatiotemporal high-order relation learning and reasoning for vehicle tracking in satellite video. The representation module of spatial high-order relations is designed to capture the spatial high-order relations between moving vehicles and their local environments, as well as global key references. Meanwhile, the temporal semantic reasoning module focuses on analyzing the evolution of these spatial high-order relations over time, thereby constructing the spatiotemporal high-order connections among the targets of interest and ensuring the continuous and stable detection of moving vehicles. Ultimately, a graph network based on spatiotemporal high-order relation reasoning is developed to perform learnable associations of target information across video frames, achieving a globally optimal solution to the tracking problem. Comparative experiments on the SatVideoDT, CGSTL, and ShuangQing-1 satellite video datasets demonstrate that the proposed method effectively enables end-to-end tracking of moving vehicles, attaining state-of-the-art performance across most evaluation metrics. On the SatVideoDT dataset, the model achieves a Multiple Object Tracking Accuracy (MOTA) of 65.1% and an Identity F1 Score (IDF1) of 70.9%. The proposed network model holds significant promise for the automated interpretation of satellite video data. The code is available at https://github.com/zsspo/GHOST-R.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105015"},"PeriodicalIF":8.6,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759385","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":"A coupled InSAR—Integrated probability model framework for goaf locating under different extraction conditions","authors":"Teng Wang ,&nbsp;Yunjia Wang ,&nbsp;Feng Zhao ,&nbsp;Zhanguo Ma ,&nbsp;Liyong Li ,&nbsp;Sen Du ,&nbsp;Kewei Zhang ,&nbsp;Nianbin Zhang ,&nbsp;Dawei Zhou ,&nbsp;Xinpeng Diao ,&nbsp;José Fernández","doi":"10.1016/j.jag.2025.105024","DOIUrl":"10.1016/j.jag.2025.105024","url":null,"abstract":"<div><div>Accurately locating goafs is essential for assessing and mitigating surface instability risks, protecting infrastructure, and guiding urban planning and ecological restoration efforts. The Probability Integral Model (PIM), Improved PIM (IPIM), and Probability Density Function (PDF) models have demonstrated effectiveness in locating underground goafs. However, due to the specific assumptions underlying each model, their applicability is limited under different extraction conditions. To this end, a novel inversion framework, termed the Locating Goaf Method based on an Integrated Probability Model (LGM-IPM), is proposed to locate underground goafs under different extraction conditions using InSAR-derived deformation. First, an integrated probability model (IPM) is developed by systematically analyzing the applicability boundaries of the PIM, IPIM, and PDF models. The proposed model accurately characterizes the relationship between surface deformation and the geometric parameters of underground goafs under varying mining intensities. Subsequently, an Adaptive Mining-Affected Pixel identification algorithm (AMAPI) is developed based on the spatiotemporal characteristics of mining-induced deformation, enabling efficient input data reduction and significantly improving computational efficiency. The geometric parameters of the underground goafs are then estimated using the Genetic Algorithm-Particle Swarm Optimization (GA-PSO). These results from the Qianyingzi coal mine, China, demonstrate that the proposed LGM-IPM effectively estimates goaf parameters under different extraction conditions. The integrated AMAPI algorithm achieves a 91% reduction in computation time by effectively filtering out unaffected pixels. Moreover, the LGM-IPM model yields the highest precision, with an average relative error of 6.38%, representing improvements of 59.89% over LGM-PIM (15.90%) and 25.69% over LGM-IPIM (8.58%). These findings demonstrate that the proposed LGM-IPM framework provides a computationally efficient and more generalizable solution for InSAR-based goaf locating under different mining conditions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105024"},"PeriodicalIF":8.6,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738414","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}