{"title":"基于广义深度学习的同震滑坡快速测绘方法","authors":"Jing Yang;Mingtao Ding;Wubiao Huang;Zhenhong Li;Zhengyang Zhang;Jing Wu;Jianbing Peng","doi":"10.1109/JSTARS.2024.3457766","DOIUrl":null,"url":null,"abstract":"The rapid mapping of co-seismic landslides is essential for emergency management and loss assessment. Deep learning algorithms generally follow a supervised learning workflow, where the trained model is used to predict landslides in surrounding areas, achieving landslide mapping with high accuracy. For a new study area landslide extraction task, the performance of the model trained on a specific dataset will be greatly reduced due to the varying data distribution of co-seismic landslides. Considering the urgent need for large-scale co-seismic landslide mapping, we developed a generalized deep learning-based landslide identification method. First, a new model—ResU-SENet is developed to generate semantic segmentation maps of landslides. The proposed model adaptively emphasizes the channel-wise weights of the input data. Three multidomain models are then designed by combining annotated landslide samples from two different domains to improve the model generalization ability. Finally, the trained models are applied directly to completely unknown domains to test model generalizability. Experiments in Iburi and Jiuzhaigou showed that the proposed model yielded the recall values of 5.93% and 7.51% higher than ResU-Net. The adoption of multidomain models effectively reduced the number of new training samples required by 50% and maintained a similar identification performance as if trained entirely with new samples. Applying the models trained by Jiuzhaigou and Iburi samples directly to Palu, the F1-score under the ResU-SENet model reached 0.6875. Moreover, the connections between model generalization and data distribution was demonstrated. This work could provide a fast response for future large-scale co-seismic landslide mapping.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675730","citationCount":"0","resultStr":"{\"title\":\"A Generalized Deep Learning-Based Method for Rapid Co-Seismic Landslide Mapping\",\"authors\":\"Jing Yang;Mingtao Ding;Wubiao Huang;Zhenhong Li;Zhengyang Zhang;Jing Wu;Jianbing Peng\",\"doi\":\"10.1109/JSTARS.2024.3457766\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The rapid mapping of co-seismic landslides is essential for emergency management and loss assessment. Deep learning algorithms generally follow a supervised learning workflow, where the trained model is used to predict landslides in surrounding areas, achieving landslide mapping with high accuracy. For a new study area landslide extraction task, the performance of the model trained on a specific dataset will be greatly reduced due to the varying data distribution of co-seismic landslides. Considering the urgent need for large-scale co-seismic landslide mapping, we developed a generalized deep learning-based landslide identification method. First, a new model—ResU-SENet is developed to generate semantic segmentation maps of landslides. The proposed model adaptively emphasizes the channel-wise weights of the input data. Three multidomain models are then designed by combining annotated landslide samples from two different domains to improve the model generalization ability. Finally, the trained models are applied directly to completely unknown domains to test model generalizability. Experiments in Iburi and Jiuzhaigou showed that the proposed model yielded the recall values of 5.93% and 7.51% higher than ResU-Net. The adoption of multidomain models effectively reduced the number of new training samples required by 50% and maintained a similar identification performance as if trained entirely with new samples. Applying the models trained by Jiuzhaigou and Iburi samples directly to Palu, the F1-score under the ResU-SENet model reached 0.6875. Moreover, the connections between model generalization and data distribution was demonstrated. This work could provide a fast response for future large-scale co-seismic landslide mapping.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675730\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10675730/\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10675730/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A Generalized Deep Learning-Based Method for Rapid Co-Seismic Landslide Mapping
The rapid mapping of co-seismic landslides is essential for emergency management and loss assessment. Deep learning algorithms generally follow a supervised learning workflow, where the trained model is used to predict landslides in surrounding areas, achieving landslide mapping with high accuracy. For a new study area landslide extraction task, the performance of the model trained on a specific dataset will be greatly reduced due to the varying data distribution of co-seismic landslides. Considering the urgent need for large-scale co-seismic landslide mapping, we developed a generalized deep learning-based landslide identification method. First, a new model—ResU-SENet is developed to generate semantic segmentation maps of landslides. The proposed model adaptively emphasizes the channel-wise weights of the input data. Three multidomain models are then designed by combining annotated landslide samples from two different domains to improve the model generalization ability. Finally, the trained models are applied directly to completely unknown domains to test model generalizability. Experiments in Iburi and Jiuzhaigou showed that the proposed model yielded the recall values of 5.93% and 7.51% higher than ResU-Net. The adoption of multidomain models effectively reduced the number of new training samples required by 50% and maintained a similar identification performance as if trained entirely with new samples. Applying the models trained by Jiuzhaigou and Iburi samples directly to Palu, the F1-score under the ResU-SENet model reached 0.6875. Moreover, the connections between model generalization and data distribution was demonstrated. This work could provide a fast response for future large-scale co-seismic landslide mapping.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.