Revolutionizing building damage detection: A novel weakly supervised approach using high-resolution remote sensing images

Abstract

Rapidly estimating post-disaster building damage via high-resolution remote sensing (HRRS) imagery is essential for initial disaster relief. However, the complex appearance of building damage poses challenges for existing methods. Specifically, relying solely on post-disaster images lacks building boundary guidance, while change detection methods using dual-temporal imageries are prone to introducing false changes. To address these issues, this paper presents a novel weakly supervised approach that leverages pre- and post-disaster HRRS images for building damage detection. The contributions of this paper are twofold. Firstly, a unique framework is proposed to utilize dual-temporal images. Precisely, the proposed method initially extracts fine-grained sub-building-level individuals from pre-disaster images by combining a fully convolutional neural network (FCN)-based method with superpixel segmentation. Then, these details serve as cues to effectively guide the detection of damaged building areas on post-disaster images, thereby enhancing accuracy. Secondly, we propose a weakly supervised method that solely relies on labeling building damage based on image patches but can ultimately yield pixel-level building damage results. Experiments conducted using HRRS images captured during the 2010 Haiti earthquake demonstrate that the proposed method outperforms existing methodologies. This effort of this paper will contribute to the sustainable development of cities and human settlements.