A STUDY ON GENERALIZING BUILDING EXTRACTION MODELS TO UNSEEN DATASETS USING SOURCE DOMAIN TRANSFER

Building footprint detection from remote sensing imagery remains an activate field of research. In particular, training models that generalise well remains challenging. A common approach to building generic models is to leverage domain adaptation approaches based on style transfer to adapt labelled datasets to an unlabelled domain. These assume the availability of labelled and unlabelled data during training. In applications such as emergency mapping however, the target domain is not always known in advance, and there is therefore a need to build models that generalise to domains unseen at training time. Using SpaceNet data various domain adaptation approaches are evaluated to improve model gen-eralisation. This article demonstrates that domain adaptation improves model generalisation, and the choice of source and target domain can be more significant than the choice of style transfer algorithm.