Boundary shape-preserving model for building mapping from high-resolution remote sensing images

The building is a critical urban element for developing smart cities. Building extraction research has experienced rapid development due to abundant remote sensing image data availability. Building extraction methods have recently focused on enhancing regional accuracy without obtaining refined building boundaries, and do not fit the concave-convex shape of the building and have significant sawtooth noise. This paper proposes a building shape-preserving framework to solve the imperfect boundary problem. To delineate the concave and convex building boundaries from remote sensing images, we combine coarse and fine-grained information with the instance segmentation method, Mask region-based convolutional neural network (R-CNN), which can further refine the indeterminate boundary points, and perform inward fitting of the building boundary. A regular boundary network is designed to learn the features of the edges and footprints to calculate the boundary loss that can smoothen the boundary noise, considering the imperfect boundaries and sawtooth noise in the extracted building boundary. Furthermore, to enhance the ability to evaluate the extracted building shape, a building footprint information evaluation algorithm, the footprint distance metric, is proposed to calculate the footprint distance between each building boundary. Comparing the building extraction results indicate that the proposed method achieves excellent quantitative evaluation scores. Experiments performed using Spacenet and WHU datasets to verify the effectiveness of the proposed method are detailed. The experimental results indicate that the algorithm utilizes the powerful edge extraction capability of the method to exhibit excellent performance in terms of extracting building edges from complex remote sensing scenes. Code is available at https://github.com/AnnaCUG/Boundary-shape-preserving-model.