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YOLOv11-SAFM: Enhancing Landslide Detection in Complex Mountainous Terrain Through Spatial Feature Adaptation

文献类型：期刊论文

作者Zhang, Cheng1,2,4; Tang, Bo-Hui1,2,3,4; Cai, Fangliang1,2,4; Li, Menghua1,2,4; Fan, Dong1,2,4
刊名REMOTE SENSING
出版日期2025-12-22
卷号18期号:1页码:24
关键词landslide detection remote sensing YOLOv11 Spatially Adaptive Feature Modulation multi-scale training deep learning
DOI10.3390/rs18010024
产权排序4
文献子类Article
英文摘要Highlights What are the main findings? YOLOv11-SAFM integrates a Spatially Adaptive Feature Modulation (SAFM) module, optimized MPDIoU bounding box regression loss, and a multi-scale training strategy, significantly improving small-scale landslide detection under complex mountainous conditions. Compared with Mask R-CNN and YOLOv8, the model shows notable improvements in precision, recall, F1-score, and mAP@0.5 for small landslide detection. What are the implications of the main findings? The SAFM module and MPDIoU loss enhance feature representation and localization accuracy, enabling robust and efficient automatic landslide detection. YOLOv11-SAFM has strong potential for application in geohazard monitoring and early warning systems in complex plateau environments.Highlights What are the main findings? YOLOv11-SAFM integrates a Spatially Adaptive Feature Modulation (SAFM) module, optimized MPDIoU bounding box regression loss, and a multi-scale training strategy, significantly improving small-scale landslide detection under complex mountainous conditions. Compared with Mask R-CNN and YOLOv8, the model shows notable improvements in precision, recall, F1-score, and mAP@0.5 for small landslide detection. What are the implications of the main findings? The SAFM module and MPDIoU loss enhance feature representation and localization accuracy, enabling robust and efficient automatic landslide detection. YOLOv11-SAFM has strong potential for application in geohazard monitoring and early warning systems in complex plateau environments.Abstract Landslide detection in mountainous regions remains highly challenging due to complex terrain conditions, heterogeneous surface textures, and the fragmented distribution of landslide features. To address these limitations, this study proposes an enhanced object detection framework named YOLOv11-SAFM, which integrates a Spatially Adaptive Feature Modulation (SAFM) module, an optimized MPDIoU-based bounding box regression loss, and a multi-scale training strategy. These improvements strengthen the model's ability to detect small-scale landslides with blurred edges under complex geomorphic conditions. A high-resolution remote sensing dataset was constructed using imagery from Bijie and Zhaotong in southwest China including GF-2 optical imagery at 1 m resolution and Sentinel-2 data at 10 m resolution for model training and validation, while independent data from Zhenxiong County were used to assess generalization capability. Experimental results demonstrate that YOLOv11-SAFM achieves a precision of 95.05%, recall of 90.10%, F1-score of 92.51%, and mAP@0.5 of 95.30% on the independent test set of the Zhaotong-Bijie dataset for detecting small-scale landslides in rugged plateau environments. Compared with the widely used Mask R-CNN, the proposed model improves precision by 13.87% and mAP@0.5 by 15.7%; against the traditional YOLOv8, it increases recall by 27.0% and F1-score by 22.47%. YOLOv11-SAFM enables efficient and robust automatic landslide detection in complex mountainous terrains and shows strong potential for integration into operational geohazard monitoring and early warning systems.
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WOS关键词EARTHQUAKE ; NETWORK
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001657732300001
出版者MDPI
源URL[http://ir.igsnrr.ac.cn/handle/311030/219746]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Tang, Bo-Hui
作者单位1.Yunnan Key Lab Quantitat Remote Sensing, Kunming 650093, Peoples R China;
2.Kunming Univ Sci & Technol, Fac Land Resources Engn, Kunming 650093, Peoples R China;
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Yunnan Int Joint Lab Integrated Sky Ground Intelli, Kunming 650093, Peoples R China;
推荐引用方式
GB/T 7714		 Zhang, Cheng,Tang, Bo-Hui,Cai, Fangliang,et al. YOLOv11-SAFM: Enhancing Landslide Detection in Complex Mountainous Terrain Through Spatial Feature Adaptation[J]. REMOTE SENSING,2025,18(1):24.
APA Zhang, Cheng,Tang, Bo-Hui,Cai, Fangliang,Li, Menghua,&Fan, Dong.(2025).YOLOv11-SAFM: Enhancing Landslide Detection in Complex Mountainous Terrain Through Spatial Feature Adaptation.REMOTE SENSING,18(1),24.
MLA Zhang, Cheng,et al."YOLOv11-SAFM: Enhancing Landslide Detection in Complex Mountainous Terrain Through Spatial Feature Adaptation".REMOTE SENSING 18.1(2025):24.

入库方式： OAI收割

来源：地理科学与资源研究所

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