Extraction of Building Roof Contours From LiDAR Data Using a Markov-Random-Field-Based Approach

Abstract

This paper proposes a method for the automatic extraction of building roof contours from a digital surface model (DSM) by regularizing light detection and ranging (LiDAR) data. The method uses two steps. First, to detect aboveground objects (buildings, trees, etc.), the DSM is segmented through a recursive splitting technique followed by a region-merging process. Vectorization and polygonization are used to obtain polyline representations of the detected aboveground objects. Second, building roof contours are identified from among the aboveground objects by optimizing a Markov-random-field-based energy function that embodies roof contour attributes and spatial constraints. The optimal configuration of building roof contours is found by minimizing the energy function using a simulated annealing algorithm. Experiments carried out with the LiDAR-based DSM show that the proposed method works properly, as it provides roof contour information with approximately 90% shape accuracy and no verified false positives.