Classification of building infrastructure and automatic building footprint delineation using airborne laser swath mapping data

Three-dimensional (3D) models of urban infrastructure comprise critical data for planners working on problems in wireless communications, environmental monitoring, civil engineering, and urban planning, among other tasks. Photogrammetric methods have been the most common approach to date to extract building models. However, Airborne Laser Swath Mapping (ALSM) observations offer a competitive alternative because they overcome some of the ambiguities that arise when trying to extract 3D information from 2D images.;Regardless of the source data, the building extraction process requires segmentation and classification of the data and building identification. In this work, approaches for classifying ALSM data, separating building and tree points, and delineating ALSM footprints from the classified data are described. Digital aerial photographs are used in some cases to verify results, but the objective of this work is to develop methods that can work on ALSM data alone.;A robust approach for separating tree and building points in ALSM data is presented. The method is based on supervised learning of the classes (tree vs. building) in a high dimensional feature space that yields good class separability. Features used for classification are based on the generation of local mappings, from three-dimensional space to two-dimensional space, known as "spin images" for each ALSM point to be classified. The method discriminates ALSM returns in compact spaces and even where the classes are very close together or overlapping spatially. A modified algorithm of the Hough Transform is used to orient the spin images, and the spin image parameters are specified such that the mutual information between the spin image pixel values and class labels is maximized. This new approach to ALSM classification allows us to fully exploit the 3D point information in the ALSM data while still achieving good class separability, which has been a difficult trade-off in the past.;Supported by the spin image analysis for obtaining an initial classification, an automatic approach for delineating accurate building footprints is presented. The physical fact that laser pulses that happen to strike building edges can produce very different 1st and last return elevations has been long recognized. However, in older generation ALSM systems (<50 kHz pulse rates) such points were too few and far between to delineate building footprints precisely. Furthermore, without the robust separation of nearby trees and vegetation from the buildings, simply extracting ALSM shots where the elevation of the first return was much higher than the elevation of the last return, was not a reliable means of identifying building footprints. However, with the advent of ALSM systems with pulse rates in excess of 100 kHz, and by using spin-imaged based segmentation, it is now possible to extract building edges from the point cloud. A refined classification resulting from incorporating "on-edge" information is developed for obtaining quadrangular footprints. The footprint fitting process involves line generalization, least squares-based clustering and dominant points finding for segmenting individual building edges. In addition, an algorithm for fitting complex footprints using the segmented edges and data inside footprints is also proposed.