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A dual-encoder U-Net for landslide detection using Sentinel-2 and DEM data

文献类型：期刊论文

作者Lu, Wei; Hu, Yunfeng; Zhang, Zuopei; Cao, Wei
刊名LANDSLIDES
出版日期2023-06-06
关键词Landslide detection U-Net Semantic segmentation Deep learning Remote Sensing Medium-resolution imagery
ISSN号1612-5118
DOI10.1007/s10346-023-02089-5
产权排序1
文献子类Article ; Early Access
英文摘要Accurate and timely landslide mapping plays a critical role in emergency response and long-term land use planning. Deep learning-based methods represented by convolutional neural networks have been widely exploited in automatic landslide detection for their outstanding capability of feature representation and end-to-end learning mode. Most of the recent deep learning-based studies used toll-access high-resolution imagery for landslide detection. Considering demands for the future large-scale landslide mapping, this study aims to develop a new deep learning-based method to detect landslides using medium-resolution imagery and digital elevation model (DEM) data which are free-access and covered globally. Firstly, a workflow for constructing the landslide dataset is developed. Then, we design a semantic segmentation model to learn deep features and generate per-pixel landslide predictions. Specifically, the proposed network has a dual-encoder architecture with feature fusion to hierarchically represent deep features from the optical bands and DEM data. We also employ a self-attention module in the decoder of the proposed network to improve the performance. Experiments on two regions demonstrate that our method achieves the best F1 score of 79.24%, outperforming SegNet, U-Net, and Attention U-Net, the models popularly used in the semantic segmentation-based landslide detection. The proposed method may have an application potential in disaster risk assessment and post-disaster reconstruction and provide a technical reference for the large-scale landslide mapping in the future.
学科主题Engineering ; Geology
WOS关键词LOGISTIC-REGRESSION ; SUSCEPTIBILITY ; NETWORK ; IMAGE
WOS研究方向Engineering ; Geology
出版者SPRINGER HEIDELBERG
源URL[http://ir.igsnrr.ac.cn/handle/311030/193763]  
专题资源与环境信息系统国家重点实验室_外文论文
作者单位1.Chinese Academy of Sciences
2.Institute of Geographic Sciences & Natural Resources Research, CAS
3.University of Chinese Academy of Sciences, CAS
推荐引用方式
GB/T 7714		 Lu, Wei,Hu, Yunfeng,Zhang, Zuopei,et al. A dual-encoder U-Net for landslide detection using Sentinel-2 and DEM data[J]. LANDSLIDES,2023.
APA Lu, Wei,Hu, Yunfeng,Zhang, Zuopei,&Cao, Wei.(2023).A dual-encoder U-Net for landslide detection using Sentinel-2 and DEM data.LANDSLIDES.
MLA Lu, Wei,et al."A dual-encoder U-Net for landslide detection using Sentinel-2 and DEM data".LANDSLIDES (2023).

入库方式： OAI收割

来源：地理科学与资源研究所

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