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.

在这篇论文中，我们研究了一种具有挑战性但实用的无监督建筑损害检测（U-BDD）场景，该场景下只提供了未标记的灾后和灾前卫星图像对。我们首先提出了一种先进的 U-BDD 基线模型，该模型利用预训练的视觉 - 语言基础模型来解决 U-BDD 任务。此外，我们还提出了一种新颖的自监督框架 U-BDD++，它通过解决与卫星图像相关的领域问题来改进 U-BDD 基线模型。通过对广泛使用的建筑损害评估基准进行大量实验证明了所提出的方法在无监督建筑损害检测方面的有效性。这项研究为真实世界的 BDD 开辟了新的方向，并为未来的研究奠定了坚实的基线。