Structured Building Extraction from High-Resolution Satellite Images with a Hybrid Convolutional Neural Network

Detecting buildings with structure information (e.g., rooflines) from satellite images is a significant yet challenging task. Existing methods usually suffer from the spatial and spectral diversity and complexity of the architectures. In this paper, a deep learning-based approach is proposed to extract structured building rooflines. We use convolutional neural networks to detect corner and line segment primitives. Meanwhile, a collaborative branch of semantic annotation information is combined to obtain the building segmentation map, which ensures the spatial and topological relations of the extracted primitives. Experiments on the SpaceNet dataset show that our proposed approach improves the accuracy of building extraction. Furthermore, the planar graph representation promotes three-dimensional (3D) reconstruction and other subsequent applications.