Edge-Aware Superpixel Dual-Graph GCN for Topographically Heterogeneous Landslide Susceptibility Assessment
Pixel-based landslide susceptibility assessment (LSA) is prone to boundary blurring, salt and pepper artifacts, and unstable generalization in topographically heterogeneous mountains. To address these issues, we propose an edge-aware superpixel graph framework with a dual-graph graph convolutional n...
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| Автори: | , , , |
|---|---|
| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
IEEE
2026-01-01
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| Серія: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Предмети: | |
| Онлайн доступ: | https://ieeexplore.ieee.org/document/11361010/ |
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