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

{"title":"STDPNet: supervised transformer-driven network for high-precision oil spill segmentation in SAR imagery","authors":"Yiheng Xie ,&nbsp;Xiaoping Rui ,&nbsp;Yarong Zou ,&nbsp;Heng Tang ,&nbsp;Ninglei Ouyang ,&nbsp;Yingchao Ren","doi":"10.1016/j.jag.2025.104812","DOIUrl":"10.1016/j.jag.2025.104812","url":null,"abstract":"<div><div>Oil spill incidents are one of the major factors damaging marine ecosystems, and there is an urgent need for effective detection and identification technologies to quickly locate oil spill contamination areas. Synthetic Aperture Radar (SAR) is capable of monitoring the ocean surface under various weather and lighting conditions, but the SAR images often contain dense speckle noise, and popular SAR oil spill image datasets typically lack sufficient polarization information. To overcome these issues, this study introduces a novel polarimetric decomposition method to generate synthetic color image datasets that integrate multiple polarization features, thereby enhancing image texture and contrast. An image denoising module is designed, which reduces noise interference in the color images through an adaptive sampling approach. Furthermore, a novel Transformer-CNN architecture model is proposed, integrating two modules: the Super Visual Attention Transformer and the Directional Multi-Branch Scale Self-Calibration Module. The segmentation performance of the model is comprehensively evaluated on three datasets, and compared with state-of-the-art segmentation methods, demonstrating superior classification accuracy and stability. This research provides an effective technical support for accurate oil spill detection and marine ecosystem protection.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104812"},"PeriodicalIF":8.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918025","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":"Estimating urban noise levels from Multi-Scale and Multi-Spectral remote sensing imagery","authors":"Zhihong Chen ,&nbsp;Teng Fei ,&nbsp;Jing Xiao ,&nbsp;Jing Huang ,&nbsp;Dunxin Jia ,&nbsp;Meng Bian","doi":"10.1016/j.jag.2025.104818","DOIUrl":"10.1016/j.jag.2025.104818","url":null,"abstract":"<div><div>Establishing a high-quality urban sound environment is essential for the sustainable development of modern cities. Estimating the noise pollution levels in urban areas is integral to improving the overall well-being of city dwellers. However, current approaches to noise levels estimation present significant challenges. Existing approaches are highly data-dependent. They either rely on data from noise sampling networks or require urban geographical data related to noise. Moreover, the latter approach often involves relatively complex modeling processes. This reliance on data availability and granularity significantly constrains the applicability of these methods. In this study, we propose a novel framework for urban noise levels estimation, leveraging deep learning techniques and multi-scale, multi-spectral remote sensing imagery. Specifically, we utilize a noise recording device to sample sound pressure level (SPL) data through mobile measurements at various locations during the daytime, a Transformer-based model is then constructed to learn noise-related information embedded in the scale, spectral, and spatial contextual features of Sentinel-2 imagery. The extracted high-dimensional feature vectors are used to quantitatively estimate SPL, with the proposed Noise-Trans-Sentinel model achieving MAE, RMSE, and R² values of 3.48, 4.68, and 0.63, respectively. Finally, a SHAP method is employed to interpret the model, exploring the role of multi-scale and multi-spectral remote sensing information in urban noise levels estimation. Our proposed framework enables and validates low-cost, spatially continuous noise estimation in urban areas. It fills a critical gap by demonstrating, for the first time, that high-resolution urban noise mapping can be achieved solely from remote sensing imagery, without relying on dense sensor networks or GIS data. This research contributes to cross-modal studies in urban environmental science and informs the optimization of urban soundscapes.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104818"},"PeriodicalIF":8.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911728","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":"National mariculture mapping in China at 10-m spatial resolution based on EMA-UNet and Sentinel-1/2 imagery","authors":"Lina Ke ,&nbsp;Yao Lu ,&nbsp;Pan Zhang ,&nbsp;Quanming Wang ,&nbsp;Zhenqi Cui ,&nbsp;Qingli Jiang","doi":"10.1016/j.jag.2025.104802","DOIUrl":"10.1016/j.jag.2025.104802","url":null,"abstract":"<div><div>Accurately measuring mariculture’s area and spatial distribution is vital for the sustainable development of marine resources. However, existing research primarily focuses on the regional scale, while national mariculture studies remain limited, with significant differences in extracted areas. This study utilized time-series Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multi-Spectral Instrument (MSI) imagery and developed the Efficient Multi-scale Attention U-Net (EMA-UNet) to generate a fine-grained map of China’s mariculture. The spatial distribution characteristics of mariculture areas were revealed through spatial analysis methods. The results showed that: (1) The EMA-UNet proposed in this study achieved higher accuracy than comparative models, with an overall accuracy of 95.9 %, an F1 score of 94.3 %, and an Intersection over Union (IoU) of 89.4 %, with improved extraction effects. (2) China’s mariculture was estimated to cover 2,941.7 km², with northern provinces accounting for 56.46 % of the total. Fujian, Shandong, and Liaoning were the three provinces with the largest mariculture areas, contributing 27.36 %, 25.85 %, and 20.87 % respectively. (3) China’s mariculture density showed a “dense in the east and sparse in the west”, “dense in the north and sparse in the south” pattern, with high-intensity aggregations in Fujian and central-southern Guangdong, and low-intensity aggregations in Bohai Bay and nearshore Guangxi, reflecting the influence of natural-socioeconomic factors. This study provides a fine-grained map of China’s mariculture distribution, serving as a data foundation for relevant studies. It also investigated the effects of extraction methods, data sources, and band features, offering insights for optimizing mariculture extraction methods.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104802"},"PeriodicalIF":8.6,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907055","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 framework for road space extraction from point clouds and integration into 3D city models","authors":"Elisavet Tsiranidou,&nbsp;Patricia González-Cabaleiro,&nbsp;Antonio Fernández,&nbsp;Lucía Díaz-Vilariño","doi":"10.1016/j.jag.2025.104803","DOIUrl":"10.1016/j.jag.2025.104803","url":null,"abstract":"<div><div>This study presents a robust methodology for segmenting an urban road network at multiple levels of granularity, leveraging Mobile Laser Scanning (MLS) point cloud data and the CityGML 3.0 framework. The proposed approach integrates semantic and geometric information to delineate road spaces into sections, intersections, sidewalks, parking areas, and individual driving lanes. The methodology achieves spatial accuracy and compliance with road design standards through the use of clustering algorithms, alpha shape methods, and geometric refinements. Case studies in two 2 km intra-urban networks—Santiago de Compostela and Madrid, both comprising local and collector streets with uninterrupted carriageways and white-colour markings—demonstrate the approach’s effectiveness, yielding highly accurate results with average Intersection over Union (IoU) scores of 0.85 for Santiago de Compostela and 0.83 for Madrid across road features. Parking areas in Santiago de Compostela achieved 0.9 IoU, while zebra crossings in Madrid exhibited limitations with 0.68 IoU due to their smaller size and complex geometry. The results provide a standardized and detailed road network representation, suitable for urban planning, traffic management, and autonomous vehicle navigation. Future work aims to expand the methodology for diverse datasets, incorporate multi-temporal analyses, and integrate additional traffic objects for enhanced CityGML modelling. This research highlights the potential of point cloud-based methods to advance digital twin development and 3D semantic urban modelling.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104803"},"PeriodicalIF":8.6,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902793","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":"Deep learning-driven forest canopy height mapping in boreal regions through multi-source remote sensing fusion: Integrating Sentinel-1/2, PALSAR, and ICESat-2/LVIS data","authors":"Chenxi Liu ,&nbsp;Wei Gong ,&nbsp;Shuo Shi ,&nbsp;Tong Wang ,&nbsp;Tao Xu ,&nbsp;Zixi Shi ,&nbsp;Jiayun Niu","doi":"10.1016/j.jag.2025.104766","DOIUrl":"10.1016/j.jag.2025.104766","url":null,"abstract":"<div><div>Forest canopy height is a key indicator for estimating forest carbon sinks and managing vegetation growth. Existing methods for fusing optical and Light Detection and Ranging (LiDAR) data still have limitations in canopy height estimation for boreal forests. In this study, we develop a forest canopy height estimation model (VGG-AdaBins) that leverages convolutional neural networks (CNNs) to extract deep features from multi-source remote sensing data. By introducing an adaptive tree height distribution estimation module, the model enables the coupling of multi-source remote sensing data for forest canopy height estimation. A joint validation dataset is constructed, including Sentinel-1/2, PALSAR images, airborne LVIS LiDAR, and spaceborne ICESat-2 photon-counting LiDAR data. This dataset is used to train the canopy height model. Finally, the performance of the canopy height prediction model is evaluated using 100 independent airborne datasets. The model’s prediction of tree height shows an MAE of 1.42 m and a RMSE of 2.25 m. The predicted 30 m canopy height map exhibits good consistency with the existing airborne data and demonstrated higher accuracy compared with current forest canopy height maps, with an accuracy improvement of at least 20%. The high prediction accuracy demonstrates that VGG-AdaBins, by integrating multi-source remote sensing data, can map continuous canopy height at the regional scale. This approach contributes to the advancement of large-scale canopy height mapping and forest carbon stock assessment in boreal forests.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104766"},"PeriodicalIF":8.6,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896295","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":"Dynamic surface water fraction (DSWF): Global surface water fraction mapping at 10-meter spatial resolution with Sentinel-2 imagery in Google Earth Engine","authors":"Yalan Wang ,&nbsp;Giles Foody ,&nbsp;Pu Zhou ,&nbsp;Yuyang Li ,&nbsp;Xiang Li ,&nbsp;Yihang Zhang ,&nbsp;Yun Du ,&nbsp;Xiaodong Li","doi":"10.1016/j.jag.2025.104813","DOIUrl":"10.1016/j.jag.2025.104813","url":null,"abstract":"<div><div>Mapping surface water at high spatiotemporal resolution is critical for managing water resources and mitigating disasters. Current global surface water datasets are typically generated at a relatively coarse monthly temporal resolution and 30-meter spatial resolution. Moreover, the mixed pixel problem further limits their ability to precisely map small water bodies. This study produced the global Dynamic Surface Water Fraction (DSWF) mapping by combining Sentinel-2 imagery and Dynamic World dataset using Google Earth Engine (GEE). Different from the analysis-ready wall-to-wall global datasets, DSWF is generated on-demand online in response to user-defined areas of interest and time. DSWF explored sub-pixel surface water fraction information at 10-meter spatial resolution, enabling the precise representation of fine-scale spatial features of surface water and minimizing the blurring artifacts commonly associated with conventional hard classification. The accuracy of DSWF was evaluated across 113 validation tiles, demonstrating an overall root mean squared error (RMSE) of 0.090 and mean absolute error (MAE) of 0.021 when validated against both pure and mixed pixels. In comparison to the 30-meter Landsat-based global datasets, DSWF provides more accurate spatial distributions of surface water, with particular effectiveness for small ponds and narrow rivers.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104813"},"PeriodicalIF":8.6,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896294","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":"MSS-Net: a lightweight network incorporating shifted large kernel and multi-path attention for ship detection in remote sensing images","authors":"Guoqing Zhou,&nbsp;Xiangting Wang,&nbsp;Sheng Liu,&nbsp;Yuefeng Wang,&nbsp;Ertao Gao,&nbsp;Jiangying Wu,&nbsp;Yanling Lu,&nbsp;Linbo Yu,&nbsp;Weiyi Wang,&nbsp;Kun Li","doi":"10.1016/j.jag.2025.104805","DOIUrl":"10.1016/j.jag.2025.104805","url":null,"abstract":"<div><div>For the challenges with nearshore small ship detection in remote sensing images (RSIs) under complex background, a lightweight network called “multi-path attention and shifted large kernel network for ship detection” (briefly called “MSS-Net”) in RSIs is proposed. Firstly, shifted large kernel with feature enhancement module (SLKE) is developed to enlarge receptive field by decomposing large kernels and shift operation while performing dynamic channel attention. Secondly, multi-path attention (MPA) is designed to effectively retain the co-calibration of spatial-channel information of ships. Thirdly, shared convolutional detection head (SCDH) is built to unify multi-scale features, reducing parameter redundancy. The proposed MSS-Net is validated through three public datasets, TGRS-HRRSD, MASATI and LEVIR. Using YOLOv8 as a baseline model for comparison analysis. The results demonstrate that the mAP50 reaches 97.5%, 78.8%, and 93.2% with the three datasets, respectively. The mAP50 with the proposed MSS-Net is higher 2.9% than YOLOX, 4.4% than RetinaNet in popular one-stage ship detection models, and 4.8% than Faster R-CNN; 2.7% than Cascade R-CNN in two-stage ship detection models. Moreover, the parameters in the MSS-Net reduces 26.7% relative to the baseline model, achieving a lightweight design. Besides, ablation experiments are conducted with the TGRS-HRRSD dataset. The results demonstrates that the SLKE increases mAP50 by 1.1%, the MPA increases mAP50 by 1.7%, while the SCDH reduces parameters by 35%. These results demonstrate that the MSS-Net achieves notable advances for lightweight ship detection.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104805"},"PeriodicalIF":8.6,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893765","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":"Can 3D model improve the accuracy of leaf chlorophyll content estimation using UAV and sentinel-2 data?","authors":"Jiachen Li ,&nbsp;Hu Zhang ,&nbsp;Raúl López-Lozano ,&nbsp;Marie Weiss ,&nbsp;Chenpeng Gu ,&nbsp;Faisal Mumtaz ,&nbsp;Jing Li ,&nbsp;Qinhuo Liu ,&nbsp;Junhua Bai ,&nbsp;Xue Liu ,&nbsp;Junyong Fang","doi":"10.1016/j.jag.2025.104810","DOIUrl":"10.1016/j.jag.2025.104810","url":null,"abstract":"<div><div>Leaf chlorophyll content (LCC) is a crucial parameter reflecting vegetation’s photosynthetic activity. Many LCC inversion algorithms based on satellite and unmanned aerial vehicle (UAV) data have been developed in recent decades. The one-dimensional radiative transfer model, like PROSAIL (1D model), has been a classic tool for LCC inversion. In recent years, three-dimensional radiative transfer models (3D model) have been developed rapidly. However, studies on 3D models for LCC inversion are limited, and their impact on inversion accuracy across different sensor resolutions remains unclear. This study focuses on winter wheat and integrates the DART, Adel-Wheat, and PROSPECT models to construct the 3D-model-derived look-up table (LUT). The 3D-model-based LUT and 1D-model-based LUT were applied to Sentinel-2 (S2) and UAV data to retrieve LCC. Validation results demonstrate that the 3D-model-based algorithm significantly improves LCC inversion accuracy for both S2 and UAV images. For UAV data, the root mean square error (RMSE) decreases from 9.90 μg/cm² to 7.97 μg/cm², and the coefficient of determination (R²) improves from 0.70 to 0.79. For S2 data, the RMSE decreases from 12.40 μg/cm² to 8.68 μg/cm², while R² increases from 0.66 to 0.85. Additionally, overestimation at low LAI levels and underestimation at high LCC levels are effectively reduced. The high accuracy achieved under varying LAI and LCC conditions allows the 3D model to capture temporal trends throughout the growing season better. The 3D-model-based LCC inversion algorithm can better utilize the high spatial resolution advantages, thereby playing a significant role in vegetation physiological monitoring and crop phenotyping.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104810"},"PeriodicalIF":8.6,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896293","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":"Enhancing satellite image compositing with temporal proximity weighting for deep learning–based cropland segmentation","authors":"Reza Maleki ,&nbsp;Falin Wu ,&nbsp;Guoxin Qu ,&nbsp;Amel Oubara ,&nbsp;Gongliu Yang","doi":"10.1016/j.jag.2025.104804","DOIUrl":"10.1016/j.jag.2025.104804","url":null,"abstract":"<div><div>Generating composite images from satellite data is crucial for crop mapping over defined periods. However, producing reliable composites for cropland segmentation presents challenges, particularly in maintaining temporal coherence and preserving key phenological stages in time series data. This study proposes a compositing method that improves temporal coherence for tracking phenological stages in deep learning–based cropland segmentation. The compositing method integrates the near–infrared to blue band reflectance ratio with a Gaussian weighting function to prioritize pixel selection based on temporal proximity to the center of the target month. Sentinel–2 monthly time series composites were generated using Google Earth Engine and evaluated through proximity analysis to assess pixel distribution within the target month and correlations with consecutive months. The performance of deep learning models trained on these composites was further assessed by comparing their segmentation results. To evaluate generalizability, the method was applied across various study areas and across different crop types and environmental conditions. The results consistently show that proposed method outperforms other techniques in preserving temporal continuity, reducing cloud–related noise, and maintaining the coherence necessary for deep learning models to effectively track crop growth patterns.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104804"},"PeriodicalIF":8.6,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889999","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":"Comprehensive geohazard detection along the Qinghai-Tibet Plateau transportation corridor based on multi-sourced earth observations","authors":"Zhenhong Li ,&nbsp;Chenglong Zhang ,&nbsp;Chen Yu ,&nbsp;Mingtao Ding ,&nbsp;Wu Zhu ,&nbsp;Trevor B. Hoey ,&nbsp;Bo Chen ,&nbsp;Jiantao Du ,&nbsp;Xinlong Li ,&nbsp;Jianbing Peng","doi":"10.1016/j.jag.2025.104811","DOIUrl":"10.1016/j.jag.2025.104811","url":null,"abstract":"<div><div>Geohazards are sudden and catastrophic. Due to the complicated topography, geology and climate conditions along the Qinghai-Tibet Plateau Transportation Corridor (QTPTC), many geohazards pose unprecedented challenges for engineering construction. Comprehensive and scientific geohazard detection has been infrequently performed in the QTPTC, so the study area still lacks a comprehensive geological hazard inventory. With the development of earth observation techniques, detecting geohazards in wide areas is possible. However, comprehensive geohazard detection over such a large spatial extent is considered impossible by individual remote sensing techniques and images. In this study, we used a combination of GACOS-assisted Interferometric Synthetic Aperture Radar (InSAR) phases, SAR amplitudes, and optical images to acquire deformational and geomorphological information of geohazards along the QTPTC. Based on deformational and geomorphological information, we establish a catalogue containing 2109 geohazards which were classified into five categories, i) actively deforming slopes (994); ii) reactivated historically deformed slopes (84); iii) stabilized historically deformed slopes (732); iv) glacier (283) and v) glacial lakes (16). A large percentage of geohazards are distributed at an elevation of 2500–5000 m with slope angles of 30-40°, five geohazards concentration regions are distributed on main active fault zones, and the types of geohazards in the five regions are influenced by precipitation and surface temperature. Finally, three field surveys were also carried out to verify 141 geohazards along the QTPTC. The above findings can improve disaster prevention and mitigation capabilities for construction and operation along the QTPTC.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104811"},"PeriodicalIF":8.6,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889998","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}