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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Detalhes bibliográficos

Principais autores:	Changle Li, Yuewei Wang, Feifei Zhang, Hongwei Zhang
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IEEE 2026-01-01
coleção:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Assuntos:	Landslide susceptibility graph convolutional network (GCN) superpixel segmentation heterogeneous terrain remote sensing
Acesso em linha:	https://ieeexplore.ieee.org/document/11361010/
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