Deep Learning for Cross-Domain Building Change Detection from Multi-Source Very High-Resolution Satellite Imagery

Detecting building changes using earth observation datasets has numerous applications, particularly in humanitarian emergency responses. Deep learning models for change detection have shown considerable progress, but they require large annotated datasets for training and validation. Although semi-supervised change detection methods utilize a large amount of unlabeled data from the same domain, their effectiveness is limited when applied across different datasets due to shifts in data distribution. We proposed a cross-domain semi-supervised change detection approach that incorporates consistency regularization and a deep change domain mapping strategy to handle data distribution shifts. This method achieved a change class Intersection over Union (IoU) of 0.57 and 0.22 in the challenging cross-domain scenarios of LEVIR-CD → WUHAN-CD and LEVIR-CD → EGY-BCD open datasets, respectively. Additionally, we tested this approach for monitoring building changes in temporary settlements hosting forcibly displaced people (FDP), using cross-domain data from open datasets and images of these temporary settlements. Despite the improvements introduced by domain mapping, the results are not yet sufficient for practical use in emergency response operations.