Learning Efficient Unsupervised Satellite Image-based Building Damage
  Detection
Yiyun Zhang Zijian Wang Yadan Luo Xin Yu Zi Huang
Existing Building Damage Detection (BDD) methods always require
labour-intensive pixel-level annotations of buildings and their conditions,
hence largely limiting their applications. In this paper, we investigate a
challenging yet practical scenario of BDD, Unsupervised Building Damage
Detection (U-BDD), where only unlabelled pre- and post-disaster satellite image
pairs are provided. As a pilot study, we have first proposed an advanced U-BDD
baseline that leverages pre-trained vision-language foundation models (i.e.,
Grounding DINO, SAM and CLIP) to address the U-BDD task. However, the apparent
domain gap between satellite and generic images causes low confidence in the
foundation models used to identify buildings and their damages. In response, we
further present a novel self-supervised framework, U-BDD++, which improves upon
the U-BDD baseline by addressing domain-specific issues associated with
satellite imagery. Furthermore, the new Building Proposal Generation (BPG)
module and the CLIP-enabled noisy Building Proposal Selection (CLIP-BPS) module
in U-BDD++ ensure high-quality self-training. Extensive experiments on the
widely used building damage assessment benchmark demonstrate the effectiveness
of the proposed method for unsupervised building damage detection. The
presented annotation-free and foundation model-based paradigm ensures an
efficient learning phase. This study opens a new direction for real-world BDD
and sets a strong baseline for future research.