KIBS: 3D detection of planar roof sections from a single satellite image

Abstract

Reconstructing urban areas in 3D from satellite raster images has been a long-standing problem for both academical and industrial research. While automatic methods achieving this objective at a Level Of Detail (LOD) 1 are mostly efficient today, producing LOD2 models is still a scientific challenge. In particular, the quality and resolution of satellite data is too low to infer accurately the planar roof sections in 3D by using traditional plane detection algorithms. Existing methods rely upon the exploitation of both strong urban priors that reduce their applicability to a variety of environments and multi-modal data, including some derived 3D products such as Digital Surface Models. In this work, we address this issue with KIBS (Keypoints Inference By Segmentation), a method that detects planar roof sections in 3D from a single-view satellite image. By exploiting large-scale LOD2 databases produced by human operators with efficient neural architectures, we manage to both segment roof sections in images and extract keypoints enclosing these sections in 3D to form 3D-polygons with a low-complexity. The output set of 3D-polygons can be used to reconstruct LOD2 models of buildings when combined with a plane assembly method. While conceptually simple, our method manages to capture roof sections as 3D-polygons with a good accuracy, from a single satellite image only by learning indirect 3D information contained in the image, in particular from the view inclination, the distortion of facades, the building shadows, roof peak and ridge perspective. We demonstrate the potential of KIBS by reconstructing large urban areas in a few minutes, with a Jaccard index for the 2D segmentation of individual roof sections of approximately 80%, and an altimetric error of the reconstructed LOD2 model of less than to 2 meters.