Procedural Roof Generation From a Single Satellite Image

Abstract

Urban procedural modeling has benefited from recent advances in deep learning and computer graphics. However, few, if any, approaches have automatically produced procedural building roof models from a single overhead satellite image. Large-scale roof modeling is important for a variety of applications in urban content creation and in urban planning (e.g., solar panel planning, heating/cooling/rainfall modeling). While the allure of modeling only from satellite images is clear, unfortunately structures obtained from the satellite images are often in low-resolution, noisy and heavily occluded, thus getting a clean and complete view of urban structures is difficult. In this paper, we present a framework that exploits the inherent structure present in man-made buildings and roofs by explicitly identifying the compact space of potential building shapes and roof structures. Then, we utilize this relatively compact space with a two-component solution combining procedural modeling and deep learning. Specifically, we use a building decomposition component to separate the building into roof parts and predict regularized building footprints in a procedural format, and use a roof ridge detection component to refine the individual roof parts by estimating the procedural roof ridge parameters. Our qualitative and quantitative assessments over multiple satellite datasets show that our method outperforms various state-of-the-art methods.