Multiscale Grid Method for Detection and Reconstruction of Building Roofs from Airborne LiDAR Data

This study proposes a multiscale grid method to detect and reconstruct building roofs from airborne LiDAR data. The method interpolates unorganized LiDAR point cloud into two sets of grids with different spatial scales. In the large-scale grid, building seed regions are obtained, including detection of initial building seed regions and refinement of building seed regions. In the small-scale grid, to detect the detailed features of building roofs with complicated top structures, a high-resolution depth image is generated by a new iterative morphological interpolation using gradually increasing scales, and then segmented by using a full λ-schedule algorithm. Based on the building seed regions, detailed roof features are detected for each building and 3-D building roof models are then reconstructed according to the elevation of these features. Experiments are analyzed from several aspects: the correctness and completeness, the elevation accuracy of building roof models, and the influence of elevation to 3-D roof reconstruction. The experimental results demonstrate promising correctness, completeness, and elevation accuracy, with a satisfactory 3-D building roof models. The strategy of hierarchical spatial scale (from large scale to small scale) obtains the complementary advantage between technical applicability in a large urban environment and high quality in 3-D reconstruction of building roofs with fine details.