There has been an ongoing demand for monitoring landslides due to the heavy economic losses and casualties caused by such natural disasters. In this paper, we introduce a swift landslide detection system that can detect and segment landslides occurring on roads. To tackle the challenges of data collection, we propose an automatic annotation procedure to create a new landslide dataset consisting of 2963 images, termed the LandslidePTIT dataset. Additionally, we construct a novel deep learning architecture that can perform both classification and segmentation tasks well from a few annotated images of landslides. Specifically, the model consists of four main modules that are delicately designed to solve the few-shot segmentation problem using landslide images, namely hypercorrelation construction, attentive squeeze block, a cross-feature layer, and broadcast and squeeze layer. Experimental results exhibit the superiority of the proposed methods in comparison with competitive baselines, in terms of both quantitative and qualitative manners.

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