Cross-Resolution Distillation for Building Extraction in Medium-Resolution Satellite Imagery

Extracting buildings in Earth observation is a vital task for various applications. Medium-resolution remote sensing images can offer notable advantages like reduced storage needs and faster acquisition, facilitating more rapid revisits of a certain area. However, they often lack detailed building features, posing challenges for accurate segmentation. To address this, we present CDNet (Cross-resolution Distillation Network), a novel framework designed to boost building extraction accuracy on medium-resolution images, resulting in computational efficiency and low time cost. CDNet operates on a teacher-student network paradigm, employing cross-resolution knowledge distillation. The teacher network processes super-resolved images, endowing the student network with crucial priors to adeptly handle lower-resolution originals. Our method achieves a cutting-edge performance on the Multi-Temporal Urban Development SpaceNet (MUDS) Dataset, showcasing exceptional accuracy with a mean Intersection over Union (mIoU) of 62.97 and Boundary over Union (BIoU) of 29.35, respectively.