A Transfer Learning Approach for Landslide Semantic Segmentation Based on Visual Foundation Model
Landslides are one of the most destructive natural disasters in the world, threatening human life and safety. With excellent performance as a foundation model for image segmentation, the segment anything model (SAM) has provided a novel paradigm for semantic segmentation research. However, the lack...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10962290/ |
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| author | Changhong Hou Junchuan Yu Daqing Ge Liu Yang Laidian Xi Yunxuan Pang Yi Wen |
| author_facet | Changhong Hou Junchuan Yu Daqing Ge Liu Yang Laidian Xi Yunxuan Pang Yi Wen |
| author_sort | Changhong Hou |
| collection | DOAJ |
| description | Landslides are one of the most destructive natural disasters in the world, threatening human life and safety. With excellent performance as a foundation model for image segmentation, the segment anything model (SAM) has provided a novel paradigm for semantic segmentation research. However, the lack of remote sensing images in the SAM training data limits its ability to recognize landslides. In addition, despite the transfer learning approach can transfer SAM feature extraction capability to the landslide segmentation task, but it will consume a lot of computational resources and training time. In order to solve these challenges, this study proposes a TransLandSeg model that transfers the segmentation capability of SAM while learning landslide features at a low training cost. To limit model training parameters, the adaptive transfer learning (ATL) module is purposely designed, the image encoder is frozen during model training, only the ATL module and mask decoder are trained, and the knowledge learned from the ATL module is input into the original network. Moreover, to select the best ATL module, we also designed 9 kinds of ATL modules and analyzed the accuracy of the TransLandSeg model with different ATL modules. We selected the Bijie landslide dataset and the Landslide4Sense dataset for model training and testing. The experiment results show that the TransLandSeg model increases the mean intersection over union by 1.48% –13.01% compared to other state-of-the-art semantic segmentation models. In addition, TransLandSeg requires only 1.3% of SAM parameters to enable SAM's powerful capabilities to transfer to landslide segmentation. |
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| id | doaj-art-ab4355fd25a04e1e8d1cc3b3ed6a3193 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-ab4355fd25a04e1e8d1cc3b3ed6a31932025-08-20T03:49:23ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118115611157210.1109/JSTARS.2025.355988410962290A Transfer Learning Approach for Landslide Semantic Segmentation Based on Visual Foundation ModelChanghong Hou0https://orcid.org/0009-0002-9324-812XJunchuan Yu1https://orcid.org/0000-0003-2987-0504Daqing Ge2https://orcid.org/0009-0005-2779-8854Liu Yang3Laidian Xi4Yunxuan Pang5Yi Wen6College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, ChinaDepartment of Satellite Application Research, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing, ChinaDepartment of Satellite Application Research, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, ChinaSchool of Earth Sciences and Resources, China University of Geosciences, Beijing, ChinaSchool of Earth Sciences and Resources, China University of Geosciences, Beijing, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, ChinaLandslides are one of the most destructive natural disasters in the world, threatening human life and safety. With excellent performance as a foundation model for image segmentation, the segment anything model (SAM) has provided a novel paradigm for semantic segmentation research. However, the lack of remote sensing images in the SAM training data limits its ability to recognize landslides. In addition, despite the transfer learning approach can transfer SAM feature extraction capability to the landslide segmentation task, but it will consume a lot of computational resources and training time. In order to solve these challenges, this study proposes a TransLandSeg model that transfers the segmentation capability of SAM while learning landslide features at a low training cost. To limit model training parameters, the adaptive transfer learning (ATL) module is purposely designed, the image encoder is frozen during model training, only the ATL module and mask decoder are trained, and the knowledge learned from the ATL module is input into the original network. Moreover, to select the best ATL module, we also designed 9 kinds of ATL modules and analyzed the accuracy of the TransLandSeg model with different ATL modules. We selected the Bijie landslide dataset and the Landslide4Sense dataset for model training and testing. The experiment results show that the TransLandSeg model increases the mean intersection over union by 1.48% –13.01% compared to other state-of-the-art semantic segmentation models. In addition, TransLandSeg requires only 1.3% of SAM parameters to enable SAM's powerful capabilities to transfer to landslide segmentation.https://ieeexplore.ieee.org/document/10962290/Foundation modellandslide recognitionsemantic segmentationtransfer learning |
| spellingShingle | Changhong Hou Junchuan Yu Daqing Ge Liu Yang Laidian Xi Yunxuan Pang Yi Wen A Transfer Learning Approach for Landslide Semantic Segmentation Based on Visual Foundation Model IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Foundation model landslide recognition semantic segmentation transfer learning |
| title | A Transfer Learning Approach for Landslide Semantic Segmentation Based on Visual Foundation Model |
| title_full | A Transfer Learning Approach for Landslide Semantic Segmentation Based on Visual Foundation Model |
| title_fullStr | A Transfer Learning Approach for Landslide Semantic Segmentation Based on Visual Foundation Model |
| title_full_unstemmed | A Transfer Learning Approach for Landslide Semantic Segmentation Based on Visual Foundation Model |
| title_short | A Transfer Learning Approach for Landslide Semantic Segmentation Based on Visual Foundation Model |
| title_sort | transfer learning approach for landslide semantic segmentation based on visual foundation model |
| topic | Foundation model landslide recognition semantic segmentation transfer learning |
| url | https://ieeexplore.ieee.org/document/10962290/ |
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