Domain-Agnostic Domain Adaption for Building Footprint Extraction

For global range satellite imaging mission, images captured from different areas may have large distribution biases due to different illuminations, shooting angles and atmospheric conditions. A straightforward idea to mitigate this problem is to categorize the images into different domains according the cities they belong to, and apply domain adaptation approaches. However, categorization by cities becomes unreasonable with the increase of the city number, and the emergence of inter-city similarity and intra-city discrepancy. With such consideration, this paper proposes a novel domain adaptation method named domain-agnostic domain adaptation (DADA) to reduce the distribution biases without explicitly defining the domain each image belongs to. To implement this, we augment the images to the styles of different domains by Generative Adversarial Networks (GAN) and contrastive learning to increase the generalizability of down-stream tasks. Experiments on Planetscope building footprint extraction datasets verify the effectiveness of our method.