Rotation-Aware Building Instance Segmentation From High-Resolution Remote Sensing Images

While extracting buildings from high-resolution remote sensing imagery has been widely conducted in automatic surveying and mapping, challenges remain in building extraction over complex scenes, particularly for densely rotated objects with fuzzy boundaries. This study proposes a rotation-aware building instance segmentation network (RotSegNet) that integrates a refined rotated detector to extract rotation equivariant and invariant features. A boundary refinement module is added to the segmentation network to extract fine-grained boundary features. We evaluated our method using the WHU building dataset and AFCities dataset. Our RotSegNet generated a minimum of 2.5% and 0.8% mean Average Precision (mAP) on the two datasets compared with other state-of-the-art methods, which shows the superiority of our method. Results also show that the proposed method can produce regularized buildings with high geometric accuracy.