Instance segmentation based building extraction in a dense urban area using multispectral aerial imagery data

Fast and automatic extraction of buildings from aerial imagery is essential for various applications. However, it remains a challenge, mainly because it requires the accurate recovery of buildings from high-resolution aerial imagery data. The task is particularly challenging due to the distinct nature of building shapes. The extraction of buildings from aerial imagery is often manually conducted by humans, which makes the process very time-consuming and costly. Furthermore, it is especially challenging to automatically extract the footprints of closely spaced buildings in dense urban areas through traditional segmentation techniques. This study proposes an instance segmentation-based building extraction technique for effective building detection and segmentation. The proposed method combines transfer learning with Mask Regional Convolutional Neural Network (Mask R-CNN) integrated with PointRend to locate and generate high-quality segmentation masks for building instances. In addition, several data augmentation strategies are also implemented to enlarge the training dataset. Results indicate that the proposed instance segmentation-based method for building extraction is able to execute distinctly efficient and effective automatic segmentation of distinctive and complex-shaped buildings in a dense urban area.