Supervising Remote Sensing Change Detection Models with 3D Surface Semantics
This work addresses remote sensing applications by improving feature extraction for change detection, though it appears incremental as it builds on existing multimodal self-supervised pretraining methods.
The paper tackles the problem of remote sensing change detection by proposing Contrastive Surface-Image Pretraining (CSIP) to jointly learn from optical RGB and above ground level (AGL) map pairs, resulting in models that extract features relevant to building segmentation and change detection tasks.
Remote sensing change detection, identifying changes between scenes of the same location, is an active area of research with a broad range of applications. Recent advances in multimodal self-supervised pretraining have resulted in state-of-the-art methods which surpass vision models trained solely on optical imagery. In the remote sensing field, there is a wealth of overlapping 2D and 3D modalities which can be exploited to supervise representation learning in vision models. In this paper we propose Contrastive Surface-Image Pretraining (CSIP) for joint learning using optical RGB and above ground level (AGL) map pairs. We then evaluate these pretrained models on several building segmentation and change detection datasets to show that our method does, in fact, extract features relevant to downstream applications where natural and artificial surface information is relevant.