Multi-scale differential network for landslide extraction from remote sensing images with different scenarios
Landslides are major geological hazards globally, causing significant economic losses each year. Accurate landslide detection is essential for disaster prevention, risk assessment, and timely emergency response. Current extraction methods struggle to distinguish landslides from their surroundings an...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2441920 |
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| _version_ | 1846116428698091520 |
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| author | Bo Yu Meijiang Zhu Fang Chen Ning Wang Huichen Zhao Lei Wang |
| author_facet | Bo Yu Meijiang Zhu Fang Chen Ning Wang Huichen Zhao Lei Wang |
| author_sort | Bo Yu |
| collection | DOAJ |
| description | Landslides are major geological hazards globally, causing significant economic losses each year. Accurate landslide detection is essential for disaster prevention, risk assessment, and timely emergency response. Current extraction methods struggle to distinguish landslides from their surroundings and precisely define their boundaries. To address these challenges, we introduce the Multi-Scale Difference Enhancement Network (MSDENet), a framework for landslide extraction through time-based change detection. MSDENet incorporates three core components: the Difference Guided Attention Module (DGAM) for enhanced focus on landslide-specific changes, the Multi-Scale Feature Fusion Module (MSFFM) for improved boundary delineation, and the Multi-Scale Sensory Module (MSSM) to boost generalization by integrating multi-scale features. We validate MSDENet’s effectiveness on the Global Very-High-Resolution Landslide Mapping (GVLM) dataset, covering 17 diverse landslide events, and further assess its applicability on high-resolution Nepal and Wenchuan datasets. MSDENet outperforms six contemporary frameworks, achieving IoU improvements of 1.42% and 1.08% for the Kaikoura and Tbilisi datasets and demonstrating gains of 3.97% and 4.79% for the Nepal and Wenchuan datasets, confirming its effectiveness in varied conditions. |
| format | Article |
| id | doaj-art-e9a1591e221a46aa9466fdf2ec7a3f62 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-e9a1591e221a46aa9466fdf2ec7a3f622024-12-19T02:41:40ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552024-12-0117110.1080/17538947.2024.2441920Multi-scale differential network for landslide extraction from remote sensing images with different scenariosBo Yu0Meijiang Zhu1Fang Chen2Ning Wang3Huichen Zhao4Lei Wang5International Research Center of Big Data for Sustainable Development Goals, Beijing, People’s Republic of ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing, People’s Republic of ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing, People’s Republic of ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing, People’s Republic of ChinaInstitute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing, People’s Republic of ChinaLandslides are major geological hazards globally, causing significant economic losses each year. Accurate landslide detection is essential for disaster prevention, risk assessment, and timely emergency response. Current extraction methods struggle to distinguish landslides from their surroundings and precisely define their boundaries. To address these challenges, we introduce the Multi-Scale Difference Enhancement Network (MSDENet), a framework for landslide extraction through time-based change detection. MSDENet incorporates three core components: the Difference Guided Attention Module (DGAM) for enhanced focus on landslide-specific changes, the Multi-Scale Feature Fusion Module (MSFFM) for improved boundary delineation, and the Multi-Scale Sensory Module (MSSM) to boost generalization by integrating multi-scale features. We validate MSDENet’s effectiveness on the Global Very-High-Resolution Landslide Mapping (GVLM) dataset, covering 17 diverse landslide events, and further assess its applicability on high-resolution Nepal and Wenchuan datasets. MSDENet outperforms six contemporary frameworks, achieving IoU improvements of 1.42% and 1.08% for the Kaikoura and Tbilisi datasets and demonstrating gains of 3.97% and 4.79% for the Nepal and Wenchuan datasets, confirming its effectiveness in varied conditions.https://www.tandfonline.com/doi/10.1080/17538947.2024.2441920Remote sensingdeep learninglandslide extractionchange detection |
| spellingShingle | Bo Yu Meijiang Zhu Fang Chen Ning Wang Huichen Zhao Lei Wang Multi-scale differential network for landslide extraction from remote sensing images with different scenarios International Journal of Digital Earth Remote sensing deep learning landslide extraction change detection |
| title | Multi-scale differential network for landslide extraction from remote sensing images with different scenarios |
| title_full | Multi-scale differential network for landslide extraction from remote sensing images with different scenarios |
| title_fullStr | Multi-scale differential network for landslide extraction from remote sensing images with different scenarios |
| title_full_unstemmed | Multi-scale differential network for landslide extraction from remote sensing images with different scenarios |
| title_short | Multi-scale differential network for landslide extraction from remote sensing images with different scenarios |
| title_sort | multi scale differential network for landslide extraction from remote sensing images with different scenarios |
| topic | Remote sensing deep learning landslide extraction change detection |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2441920 |
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