International Journal of Applied Earth Observation and Geoinformation最新文献

{"title":"A novel approach for Quasi-Global daily continuous surface soil moisture downscaling at 500-m resolution using CYGNSS observations","authors":"Jundong Wang, Ting Yang, Wanxue Zhu, Shiji Li, Zixuan Tang, Wei Wan, Zhigang Sun","doi":"10.1016/j.jag.2026.105137","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105137","url":null,"abstract":"Accurate Surface Soil Moisture (SSM) with high spatiotemporal resolution is essential for hydrological modeling and agricultural applications. The Cyclone Global Navigation Satellite System (CYGNSS) provides quasi-global SSM with relatively high temporal resolution, while its coarse spatial resolution limits regional usability. This study proposed a novel approach to generate daily continuous quasi-global SSM at 500-m resolution by integrating CYGNSS observations with optical imagery, without relying on in-situ SSM inputs. The method includes: (1) reconstruction of daily Surface Reflectivity (SR) using the Previously-Observed Behavior Interpolation (POBI) algorithm and retrieval of 9-km SSM via a modified Reflectivity-Vegetation-Roughness (R-V-R) model; (2) constructing a direct relationship between SSM and optical reflectance using the enhanced OPtical TRApezoid Model (OPTRAM) as the foundational basis for subsequent downscaling process; (3) downscale coarse-resolution CYGNSS SSM using the Bayesian algorithm without requiring any in-situ SSM observations as inputs. The framework is applied over August 2019–2022 and validated against in-situ SSM from 194 International Soil Moisture Network (ISMN) sites across diverse climate and land cover conditions. Results show improved spatial detail while maintaining accuracy, with a mean unbiased root-mean-square error (ubRMSE) of 0.061 cm<ce:sup loc=\"post\">3</ce:sup>/cm<ce:sup loc=\"post\">3</ce:sup> and a positive G<ce:inf loc=\"post\">DOWN</ce:inf> value of 0.017, indicating effective SSM downscaling. In addition, the proposed method outperforms both Linear and Random Forest models while maintaining robust performance. Overall, it offers a scalable solution for generating high-resolution, daily SSM products directly from satellite data.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"31 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KFIA-Net: a knowledge fusion and imbalance-aware network for multi-category SAR ship detection","authors":"Zhongzhen Sun, Xianghui Zhang, Xiangguang Leng, Xueqi Wu, Boli Xiong, Kefeng Ji, Gangyao Kuang","doi":"10.1016/j.jag.2026.105127","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105127","url":null,"abstract":"Multi-category synthetic aperture radar (SAR) ship detection is limited by heterogeneity in imaging mechanisms and severe class imbalance, yielding accurate localization but frequent misclassification. To address this issue, this paper proposes a Knowledge Fusion and Imbalance-Aware Network (KFIA-Net). Specifically, we first propose a Domain Knowledge Feature Extraction (DKFE) to extract and encode knowledge tokens from four priors. Second, a Knowledge Cross-Attention Fusion (KCAF) module is designed to perform interpretable and sparsely selectable channel modulation using cross-attention and FiLM decoding. Thirdly, we further design an Imbalance-Aware Loss Function (IALF) that combines prior calibration, minority category margin expansion, and knowledge-consistency weighting to reduce loss bias. Finally, systematic experiments and comparisons are conducted on three datasets: SRSDD-v1.0, FAIR-CSAR-v1.0, and NUDT-SARship-v1.0. Our KFIA-Net achieves mAP<ce:inf loc=\"post\">50</ce:inf> scores of 64.29%, 37.99%, and 78.26%, and mAP<ce:inf loc=\"post\">75</ce:inf> scores of 34.96%, 19.70%, and 66.36%, respectively. These results demonstrate knowledge injection simultaneously improves class accuracy and sustains robust localization. Furthermore, KFIA-Net requires only 11.47 M parameters and 66.79G FLOPs, achieving an inference speed of 47.21 FPS on a 1024 × 1024 input, achieving a good trade-off between accuracy and efficiency.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"236 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic modeling of InSAR-derived land subsidence hazard in New York City for transportation infrastructure damage risk assessments","authors":"Ntambila Daud, Oluwaseyi Dasho, Manoochehr Shirzaei","doi":"10.1016/j.jag.2026.105118","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105118","url":null,"abstract":"Land subsidence is a growing geohazard that poses a significant threat to critical infrastructure, particularly in urban coastal cities. This study uses Interferometric Synthetic Aperture Radar (InSAR) data from 2016 to 2024 to estimate angular distortion rates to assess infrastructure damage risk in New York City. We applied a probabilistic framework to evaluate multiple “what-if” scenarios and project long-term risks, providing actionable insights for resilience and mitigation planning. Results reveal persistent subsidence in low-elevation and reclaimed zones (∼-5 mm/yr) with localized uplift (∼+1.5 mm/yr), affecting major airports, subway segments, and highways. Fifty-year projections indicate high angular distortion probabilities (0.6–0.8), with economic exposure estimated at ∼$8.20 billion for ∼ 6.1 km of subway lines and ∼$10.54 billion for ∼ 7.8 km of highways exceeding –2 mm/yr. Despite their limited spatial extent, these segments represent a disproportionately large share of total exposure. The findings emphasize the need for continuous monitoring, proactive mitigation, and targeted investment, highlighting the value of integrating geodetic data with probabilistic modeling to address subsidence and climate-related hazards.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"26 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quad-pol reconstruction of dual-pol SAR data via a physically constrained diffusion model for building damage assessment","authors":"Zihuan Guo, Hong Zhang, Xiao-Ming Li, Yukun Fan, Haoxuan Duan, Qiming Zeng, Ji Ge, Chao Wang","doi":"10.1016/j.jag.2026.105132","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105132","url":null,"abstract":"Quad-polarimetric (quad-pol) synthetic aperture radar (SAR) data provides crucial polarimetric information for post-disaster building damage assessment. However, most current spaceborne SAR platforms prioritize dual-polarization (dual-pol) mode, which ensures high temporal and spatial data availability but limits damage analysis accuracy due to the absence of some polarimetric information. Existing methods for reconstructing dual-pol to quad-pol SAR data often fail to ensure that the reconstructed data meets fundamental physical properties, while traditional building damage detection methods still struggle to accurately capture complex depolarization effects. To address these challenges, this paper proposes a diffusion model-based method for reconstructing dual-pol data to quad-pol data, applied to post-earthquake building damage analysis. The method introduces a Positive Semi-definite Constraint Module and a Plug-and-Play SVD Parameter Fine-tuning Module to ensure the physical validity and accuracy of the reconstructed data. Additionally, a Stokes vector-based Degree of Polarization frequency analysis method is proposed to enhance the description of depolarization information. A multi-dimensional polarimetric feature combination is constructed for grid-level building damage assessment. Experiments on Gaofen-3, ALOS-2/PALSAR-2, and Sentinel-1 data show that the proposed method performs optimally in complex scenarios, with all pixels meeting the positive semi-definite constraint. Compared to the original dual-pol SAR data, building damage assessment using the reconstructed quad-pol SAR data resulted in an F1 score improvement of 16.3% and 8.4% for detecting moderately and severely damaged buildings, respectively. This research provides crucial technical support for fully harnessing the potential of dual-pol SAR data in building damage assessment.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"89 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Resolution (0.9 m) terrace mapping in Low-Latitude hilly regions using deep learning and Area–Slope Denoising: A case study from Guangdong Province, China","authors":"Yinghai Zhao, Hanquan Cheng, Siyuan Peng, Suhong Liu, Yun Xie, Baoyuan Liu","doi":"10.1016/j.jag.2026.105126","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105126","url":null,"abstract":"Terrace construction is a crucial human intervention for improving slope productivity and preventing soil erosion, especially in low-latitude hilly regions with heavy and concentrated rainfall. However, existing terrace mapping methods perform poorly in such areas. In this study, we generated the first high-resolution (0.9 m) terrace distribution map for low-latitude hilly regions by integrating deep learning with slope-area threshold denoising. Using Guangdong Province as a case study, we combined Google Earth imagery, SRTM DEM, and Global 10-m land-cover data. The results showed that the optimal 0.9 m resolution achieved 93.34 % overall accuracy (OA), 79.18 % F1-score, and 65.53 % intersection over union (IoU) on our Guangdong terrace dataset, processing 400 km<ce:sup loc=\"post\">2</ce:sup> in 89 min, showing an excellent accuracy–speed trade-off. Statistical analysis revealed that terraces in Guangdong are predominantly small (4725 m<ce:sup loc=\"post\">2</ce:sup> on average) and gently sloped (8.85°). Provincial validation confirmed superior performance, with a producer’s accuracy of 80.38 %. Spatially, terraces are mainly clustered in inland hilly areas, especially in northern Guangdong, with additional clusters in the peripheral zones of the Pearl River Delta and in the western and eastern regions. Cities with the largest terrace coverage are Qingyuan (801.98 km<ce:sup loc=\"post\">2</ce:sup>), Zhaoqing (733.11 km<ce:sup loc=\"post\">2</ce:sup>), and Shaoguan (701.53 km<ce:sup loc=\"post\">2</ce:sup>). This high-precision dataset supports agricultural management, soil erosion risk assessment, ecological conservation, and rural revitalisation, while also offering a transferable framework for mapping terrain features in complex landscapes worldwide using remote sensing imagery.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"84 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dual pixel-level and subpatch-level network with cross-temporal super resolution for change detection across spatial resolutions","authors":"Dawei Wen, Yunlong Zhang, Binqiang Zhang, Deng Chen, Xiaofeng Pan, Xin Huang","doi":"10.1016/j.jag.2026.105134","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105134","url":null,"abstract":"Remote sensing image change detection is critical for monitoring earth surface dynamics. Although deep learning has significantly improved change detection performance, traditional and existing deep super-resolution techniques for cross-resolution change detection often assume bi-temporal images share the same resolution, at least in the training phase. They also suffer from limitations including dependency on expensive high-resolution paired training data, suboptimal performance transfer from super-resolution to change detection accuracy, and heavy reliance on extensive pixel-level annotations. To address these limitations, we propose a novel dual pixel-level and subpatch-level network with cross-temporal super resolution (DPSNet) for change detection across spatial resolutions. Our method DPSNet, comprises two core components: 1) a Reference Image-Guided Generative Adversarial Network (RefIGM GAN) for cross-temporal super resolution; and 2) a Semi-supervised Dual-Path Network (SDNet) for pixel-level and subpatch-level change detection. A resource-efficient alternating optimization strategy is employed between RefIGM GAN and SDNet, creating a virtuous cycle in which super-resolution improves detection accuracy, and detection results optimize super-resolution reconstruction. Experiments were conducted on three datasets, i.e., CDD, SYSU, and HTCD, each characterized by distinct resolution variations. The CDD and SYSU datasets include bi-temporal images with 4 × and 8 × resolution differences, respectively, while the HTCD dataset contains both satellite and UAV imagery with inherent resolution disparities. The results demonstrate that by integrating reference-guided super resolution and semi-supervised learning, effective cross-resolution change detection can be achieved with only limited high-resolution data and pixel-level labels, showing great practical significance in scenarios where solely low-resolution historical images are available. Our source code will be released at <ce:inter-ref xlink:href=\"https://github.com/Flandre7155/DPSNet\" xlink:type=\"simple\">https://github.com/Flandre7155/DPSNet</ce:inter-ref>.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"29 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DreamCD: A change-label-free framework for change detection via a weakly conditional semantic diffusion model in optical VHR imagery","authors":"Kai Tang, Zhuo Zheng, Hongruixuan Chen, Xuehong Chen, Jin Chen","doi":"10.1016/j.jag.2026.105125","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105125","url":null,"abstract":"The Earth is experiencing continuous anthropogenic and natural changes. Very high resolution (VHR) remote sensing imagery-based change detection provides an effective means to monitor these dynamics at fine spatial scales. Although deep learning has significantly advanced supervised change detection (CD), it heavily relies on large amounts of human-labeled samples. In real-world CD application scenarios, acquiring sufficient change samples is challenging due to the labor-intensive nature of pixel-level labeling. This challenge has motivated the development of unsupervised change detection (UCD). However, existing UCD methods still struggle in complex scenes with bi-temporal domain shifts caused by different imaging conditions. This is largely due to the absence of high-quality samples needed to guide CD-oriented optimization. To address this challenge, we propose DreamCD, a change-label-free framework that synthesizes change samples for UCD. DreamCD consists of: (1) a weakly conditional semantic diffusion model trained with pseudo-semantic masks, (2) a Content-Semantic-Style synthesis strategy that synthesizes realistic pre- and post-event image pairs of the application domain, and (3) an arbitrary contemporal deep change detector trained solely on synthetic samples. We further introduce LsSCD-Ex, a large-scale semantic change detection (SCD) dataset consistent with OpenEarthMap semantics, enabling evaluation of synthetic-sample-based SCD. Experiments on the SECOND and LsSCD-Ex datasets demonstrate that DreamCD achieves state-of-the-art (SOTA) UCD performance, improving the average F1 score by 14.01% over existing methods for binary CD and outperforming the SOTA unsupervised SCD model, Changen2, by 2.15% in F1 and 3.63% in separated kappa coefficient (SCD metric). These results suggest that DreamCD provides a promising and extensible solution for CD in real-world remote sensing applications. Code and LsSCD-Ex dataset are available at <ce:inter-ref xlink:href=\"https://github.com/tangkai-RS/DreamCD\" xlink:type=\"simple\">https://github.com/tangkai-RS/DreamCD</ce:inter-ref>.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"9 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TSSMamba: A temporal–spectral–spatial state space model for multi-temporal remote sensing cloud removal","authors":"Chengyao Zhang, Fengyan Wang, Xuqing Zhang, Mingchang Wang, Feng Chen, Xiang Wu, Weitong Ma","doi":"10.1016/j.jag.2026.105131","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105131","url":null,"abstract":"Cloud cover in optical remote sensing imagery severely limits its effectiveness for downstream applications. Compared with cloud removal methods that rely on Synthetic Aperture Radar (SAR) data or single cloud‐free references, multi-temporal imagery provides richer, more stable and more reliable auxiliary information. However, existing methods often exploit only limited temporal observations and fail to fully capture the deep synergistic relationships among spatial, spectral, and temporal features, leading to reconstruction results that suffer from detail loss and spectral distortion in cloud-contaminated regions. To address this issue, we propose TSSMamba, a temporal–spectral–spatial state space model for multi-temporal cloud removal. The framework jointly models temporal dynamics, spectral responses and spatial structures of cloud-obscured imagery. A dual-stream architecture is designed to separately learn temporal–spectral and temporal–spatial dependencies, while a cross-dimensional fusion module strengthens interactions between the two streams and improves spatial coherence and spectral consistency in the restored images. Experiments on three public cloud removal datasets demonstrate that TSSMamba significantly outperforms state-of-the-art methods in both quantitative metrics and visual quality. On the STGAN Dataset, Sen2_MTC and SEN12MS-CR-TS, TSSMamba achieves PSNR gains of 0.98, 0.59, and 1.11 dB and SSIM improvements of 0.0123, 0.0130, and 0.0115, respectively, over MSC-GAN, thereby confirming its superior cloud removal capability. Code will be made available at: https://github.com/zhangcy23/TSSMamba.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"159 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A universal method combining spectral index and machine learning for monitoring onshore gas flaring from Sentinel-2 MSI images","authors":"Chen Feng, Xiuying Zhang, Chenglin Hu, Miaomiao Cheng, Meiling Shi","doi":"10.1016/j.jag.2026.105130","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105130","url":null,"abstract":"Gas flaring (GF) releases large amounts of greenhouse gases and pollutants, severely affecting global climate change and regional environmental quality. Quantifying global GF activities is essential to implement emission mitigation polices. Existing methods struggle with detecting weak GF activities under complex daytime background conditions. This study proposes a method combining the spectral index and machine learning methods, to detect onshore GF sites (GFs) using the images from the Multispectral Instrument (MSI) onboard Sentinel-2 satellites. First, the Thermal Anomaly Index (TAI, calculated in the near-infrared and short-wave infrared bands) and TAI increment (ΔTAI) are applied to detect thermal anomalies from MSI images, in which ΔTAI enhances the spatial-contextual background contrast. Then, nine flare-specific features are selected to further identify the GFs from the thermal anomalies using a random forest model. Finally, the detected GFs were validated worldwide, achieving an overall user accuracy of 93.34% and a producer accuracy of 95.31%. This approach enhances the sensitivity to weak flares and effectively addresses the interferences from other heat sources and highly reflective buildings, providing a universal solution for GFs detection in complex onshore scenes.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"72 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GIBN-Net for automated windthrown tree detection in eucalyptus plantations using UAV imagery","authors":"Jun Xiang, Xiuhao Liang, Jiawei Jiang, Dongxia Zou, Wei Wei, Dengkui Mo, Xiaoming Qiu, Chen Liang, Kai Lu","doi":"10.1016/j.jag.2026.105121","DOIUrl":"https://doi.org/10.1016/j.jag.2026.105121","url":null,"abstract":"Eucalyptus plantations, characterized by uniform stand structure and relatively sparse foliage, are highly susceptible to typhoon damage, which poses substantial economic risks to forestry operations and undermines regional ecosystem stability in southern China. This study focuses on windthrown eucalyptus forests in Shangsi and Bobai Counties, Guangxi Zhuang Autonomous Region, following Typhoons Ma-on and TALIM . We developed a specialized UAV (Unmanned Aerial Vehicle) based deep learning dataset and proposed a novel segmentation model, GIBN-Net, which incorporates an asymmetric global attention module to enhance feature extraction. Evaluation results, including ablation and comparative experiments, show that GIBN-Net outperforms mainstream models, achieving optimal accuracy (OA: 0.9405, Precision: 0.8879, Recall: 0.9105, IoU: 0.8248, F1-Score: 0.8722). The study also revealed that tailored preprocessing strategies significantly improved boundary detection, while simple stacking of the UAV-derived DSM with RGB imagery led to a decrease in model accuracy. This research is the first to develop a dedicated dataset and an optimized deep learning model for typhoon-damaged eucalyptus detection in southern China. The GIBN-Net model is capable of detecting wind damage in eucalyptus forests more accurately and efficiently in complex forest environments. The dedicated dataset, combined with spatial context information, improves boundary accuracy and facilitates faster damage mapping. This study enables rapid, large-scale, and automated assessment of typhoon-induced damage in eucalyptus plantations, thereby providing actionable insights for insurance claim verification, post-disaster resource allocation, and ecological restoration planning. Therefore, it can serve as a timely and effective decision-support tool for forestry management and disaster response.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"11 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}