RegGAN: An End-to-End Network for Building Footprint Generation with Boundary Regularization

Abstract

Accurate and reliable building footprint maps are of great interest in many applications, e.g., urban monitoring, 3D building modeling, and geographical database updating. When compared to traditional methods, the deep-learning-based semantic segmentation networks have largely boosted the performance of building footprint generation. However, they still are not capable of delineating structured building footprints. Most existing studies dealing with this issue are based on two steps, which regularize building boundaries after the semantic segmentation networks are implemented, making the whole pipeline inefficient. To address this, we propose an end-to-end network for the building footprint generation with boundary regularization, which is termed RegGAN. Our method is based on a generative adversarial network (GAN). Specifically, a multiscale discriminator is proposed to distinguish the input between false and true, and a generator is utilized to learn from the discriminator’s response to generate more realistic building footprints. We propose to incorporate regularized loss in the objective function of RegGAN, in order to further enhance sharp building boundaries. The proposed method is evaluated on two datasets with varying spatial resolutions: the INRIA dataset (30 cm/pixel) and the ISPRS dataset (5 cm/pixel). Experimental results show that RegGAN is able to well preserve regular shapes and sharp building boundaries, which outperforms other competitors.