Integrative Few-Shot Classification and Segmentation for Landslide Detection

Abstract

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.

Deep Evidential Remote Sensing Landslide Image Classification With a New Divergence, Multiscale Saliency and an Improved Three-Branched Fusion

“…[14] also proposes a ShapeFormer for the same task. Except the mentioned works, many state-of-the-art learning techniques, including multiclass classification [15] and few-short learning [16], are also found effective. Nevertheless, pixel or object-level detection has a large computational burden, and [17] points out in many scenarios, the accurate identification of landslide boundary is challenging.…”