Abstract

Accurate delineation of landslides in RGB optical remote sensing imagery supports rapid disaster mapping and post-event assessment. This remains difficult because landslides are often small and irregular, resemble bare soil or disturbed vegetation, and acquire blurred boundaries when images are resized. We developed AS-UNet, a lightweight U-Net variant with three targeted modifications. The Asymmetric Strip Attention Module uses horizontal and vertical depthwise strip convolutions with parallel channel-spatial reweighting to capture anisotropic landslide morphology. The Channel-Spatial Joint Gate uses decoder semantics to filter selected skip connections while retaining channel-specific spatial responses. The Poly-Harmonized Gradient Dice Loss (PGD Loss) combines pixel-wise, region-overlap, gradient-density, and probability-regularization terms for imbalanced segmentation. At 128 × 128 input resolution, AS-UNet achieved a best-validation IoU of 80.46 ± 0.03% and an independent-test IoU of 77.82 ± 0.32% across three random seeds. AS-UNet contains 8.634 M parameters and processed 380.79 frames per second on the reported hardware. These results indicate a favorable balance between segmentation accuracy and computational efficiency for RGB optical landslide mapping.

IPC Classification

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