  Unsupervised multimodal change detection is pivotal for time-sensitive tasks
and comprehensive multi-temporal Earth monitoring. In this study, we explore
unsupervised multimodal change detection between two key remote sensing data
sources: optical high-resolution imagery and OpenStreetMap (OSM) data.
Specifically, we propose to utilize the vision foundation model Segmentation
Anything Model (SAM), for addressing our task. Leveraging SAM's exceptional
zero-shot transfer capability, high-quality segmentation maps of optical images
can be obtained. Thus, we can directly compare these two heterogeneous data
forms in the so-called segmentation domain. We then introduce two strategies
for guiding SAM's segmentation process: the 'no-prompt' and 'box/mask prompt'
methods. The two strategies are designed to detect land-cover changes in
general scenarios and to identify new land-cover objects within existing
backgrounds, respectively. Experimental results on three datasets indicate that
the proposed approach can achieve more competitive results compared to
representative unsupervised multimodal change detection methods.
