VLM-BCD: Unsupervised Building Change Detection

Yiyun Zhang, Zijian Wang

Abstract: Building Change Detection (BCD) is one of the most important parts of remote sensing analysis. However, most of the existing BCD approaches require a large amount of pixel-level annotation, which limits their applicability due to intensive labour costs. To alleviate this issue, we propose a vision-language model-based framework, VLM-BCD, which performs BCD tasks without requiring any labels. Specifically, the proposed framework consists of two stages: 1) Bi-temporal building localisation by leveraging open-vocabulary DETR. 2) Unchanged mask suppressing by the Change Resolver module to detect the building change in bi-temporal satellite images. An application with an interactive dashboard is implemented to maximise the usability of the developed framework.