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Among traditional Light Detection And Ranging (LIDAR) data representations such as raster grid, triangulated irregular network, point clouds and octree, the explicit 3D nature of voxel-based representation makes it a promising alternative. Despite the benefit of voxel-based representation, voxel-based algorithms have rarely been used for building detection. In this paper, a voxel segmentation-based 3D building detection algorithm is developed for separating building and nonbuilding voxels. The proposed algorithm first voxelizes the LIDAR point cloud into a grayscale voxel structure in which the grayscale of the voxel corresponds to the quantized mean intensity of the LIDAR points within the voxel. The voxelized dataset is segmented into multiple 3D-connected regions depending on the connectivity and grayscale similarity among voxels. The 3D-connected regions corresponding to the building roof and facade are detected sequentially according to characteristics such as their area, density, elevation difference and location. The obtained results for the detected buildings are evaluated by the LIDAR data provided by working group III/4 of ISPRS, which demonstrate a high rate of success. Average completeness, correctness, quality, and kappa coefficient indexes values of 90.0%, 96.0%, 88.1% and 88.7%, respectively, are obtained for buildings. [ABSTRACT FROM AUTHOR]

The existing binary voxel model algorithm for 3D building detection (3BD) from airborne lidar cannot distinguish between connected buildings and non‐buildings. As a result, a greyscale voxel structure model, using the discretised mean intensity of lidar points, is presented to support subsequent building detection in areas where buildings are adjacent to non‐buildings but with different greyscales. The resulting 3BD algorithm first detects a building roof by selecting voxels characterised by a jump in elevation as seeds, labelling them and their 3D connected regions as rooftop voxels. Then voxels which fall into buffers and possess similar greyscales to that of the corresponding building outline are assigned as building façades. The results for detected buildings are evaluated using lidar data with different densities and demonstrate a high rate of success. The existing binary voxel model for 3D building detection using lidar cannot distinguish between connected buildings and non‐buildings. Here a new model is presented where buildings are adjacent to non‐buildings but with different greyscales. The resulting 3D building detection (3BD) algorithm first detects a building roof by selecting voxels characterised by a jump in elevation as seeds. Voxels relating to building facades are subsequently assigned. The results using lidar data with different densities demonstrate a high rate of success. [ABSTRACT FROM AUTHOR]