Investigating the landslide susceptibility assessment methods for multi-scale slope units based on SDGSAT-1 and Graph Neural Networks
Accurate assessment of landslide susceptibility is vital for risk prevention, yet existing methods often overlook remote but interconnected geographical features, leading to unreliable maps. To effectively address this issue, the complex mountainous terrain and geomorphological features involved in...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2468913 |
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| author | Xiangqi Lei Hanhu Liu Zhe Chen Shaoda Li Hang Chen Shuai Zeng Xiao Wang Wenqian Bai Wei Li Lorenzo Picco |
| author_facet | Xiangqi Lei Hanhu Liu Zhe Chen Shaoda Li Hang Chen Shuai Zeng Xiao Wang Wenqian Bai Wei Li Lorenzo Picco |
| author_sort | Xiangqi Lei |
| collection | DOAJ |
| description | Accurate assessment of landslide susceptibility is vital for risk prevention, yet existing methods often overlook remote but interconnected geographical features, leading to unreliable maps. To effectively address this issue, the complex mountainous terrain and geomorphological features involved in landslide formation were fully considered in this work. This was attained by introducing geographical environmental correlations from the perspectives of mapping units and susceptibility assessment models to achieve comprehensive linkage between the landslides and the affected environments, thereby enhancing accuracy. Significantly, in this work, the SDGSAT-1 data were innovatively applied to the field of landslide research and the landslide susceptibility in Jiulong County, Ganzi, was evaluated based on optimal-scale slope units and Graph Neural Networks (GNN). The results showed that: (1) SDGSAT-1 offers significant benefits to landslide research. Our analysis compared LANDSAT with similar resolutions from multiple perspectives and found that SDGSAT-1 has substantial advantages. (2) The landslide susceptibility assessment method proposed in this work, based on optimal-scale slope units and GNN, demonstrated superior performance, with various evaluation metrics, such as AUC, Accuracy, and Precision far exceeding those of other machine learning models. |
| format | Article |
| id | doaj-art-497de70005c44caab021930aa8d96c5a |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-497de70005c44caab021930aa8d96c5a2025-08-25T11:24:53ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2468913Investigating the landslide susceptibility assessment methods for multi-scale slope units based on SDGSAT-1 and Graph Neural NetworksXiangqi Lei0Hanhu Liu1Zhe Chen2Shaoda Li3Hang Chen4Shuai Zeng5Xiao Wang6Wenqian Bai7Wei Li8Lorenzo Picco9College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu, People’s Republic of ChinaCollege of Geography and Planning, Chengdu University of Technology, Chengdu, People’s Republic of ChinaCollege of Mathematics and Physics, Chengdu University of Technology, Chengdu, People’s Republic of ChinaCollege of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu, People’s Republic of ChinaCollege of Surveying, Mapping and Geoinformation, Sichuan Water Conservancy Vocational College, Chengdu, People’s Republic of ChinaSichuan Provincial Institute of Land Space Ecological Restoration and Geological Disaster Prevention and Control, Chengdu, People’s Republic of ChinaCollege of Architecture and Civil Engineering, Chengdu University, Chengdu, People’s Republic of ChinaCollege of Geography and Planning, Chengdu University of Technology, Chengdu, People’s Republic of ChinaCollege of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu, People’s Republic of ChinaDepartment of Land and Agroforest Environment, University of Padova, Padova, ItalyAccurate assessment of landslide susceptibility is vital for risk prevention, yet existing methods often overlook remote but interconnected geographical features, leading to unreliable maps. To effectively address this issue, the complex mountainous terrain and geomorphological features involved in landslide formation were fully considered in this work. This was attained by introducing geographical environmental correlations from the perspectives of mapping units and susceptibility assessment models to achieve comprehensive linkage between the landslides and the affected environments, thereby enhancing accuracy. Significantly, in this work, the SDGSAT-1 data were innovatively applied to the field of landslide research and the landslide susceptibility in Jiulong County, Ganzi, was evaluated based on optimal-scale slope units and Graph Neural Networks (GNN). The results showed that: (1) SDGSAT-1 offers significant benefits to landslide research. Our analysis compared LANDSAT with similar resolutions from multiple perspectives and found that SDGSAT-1 has substantial advantages. (2) The landslide susceptibility assessment method proposed in this work, based on optimal-scale slope units and GNN, demonstrated superior performance, with various evaluation metrics, such as AUC, Accuracy, and Precision far exceeding those of other machine learning models.https://www.tandfonline.com/doi/10.1080/17538947.2025.2468913SDGSAT-1landslideslandslide susceptibility assessmentautomatic extraction of slope unitsGraph Neural Network |
| spellingShingle | Xiangqi Lei Hanhu Liu Zhe Chen Shaoda Li Hang Chen Shuai Zeng Xiao Wang Wenqian Bai Wei Li Lorenzo Picco Investigating the landslide susceptibility assessment methods for multi-scale slope units based on SDGSAT-1 and Graph Neural Networks International Journal of Digital Earth SDGSAT-1 landslides landslide susceptibility assessment automatic extraction of slope units Graph Neural Network |
| title | Investigating the landslide susceptibility assessment methods for multi-scale slope units based on SDGSAT-1 and Graph Neural Networks |
| title_full | Investigating the landslide susceptibility assessment methods for multi-scale slope units based on SDGSAT-1 and Graph Neural Networks |
| title_fullStr | Investigating the landslide susceptibility assessment methods for multi-scale slope units based on SDGSAT-1 and Graph Neural Networks |
| title_full_unstemmed | Investigating the landslide susceptibility assessment methods for multi-scale slope units based on SDGSAT-1 and Graph Neural Networks |
| title_short | Investigating the landslide susceptibility assessment methods for multi-scale slope units based on SDGSAT-1 and Graph Neural Networks |
| title_sort | investigating the landslide susceptibility assessment methods for multi scale slope units based on sdgsat 1 and graph neural networks |
| topic | SDGSAT-1 landslides landslide susceptibility assessment automatic extraction of slope units Graph Neural Network |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2468913 |
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