Abstract
Automatic building extraction and delineation from airborne LiDAR point cloud data of urban environments is still a challenging task due to the variety and complexity at which buildings appear. The Medial Axis Transform (MAT) is able to describe the geometric shape and topology of an object, but has never been applied for building roof outline extraction. It represents the shape of an object by its centerline, or skeleton structure instead of its boundary. Notably, end points of the MAT in principle coincide with corner points of building outlines. However, the MAT is sensitive to small boundary irregularities, which makes shape detection in airborne point clouds challenging. We propose a robust MAT-based method for detecting building corner points, which are then connected to form a building boundary polygon. First, we approximate the 2D MAT of a set of building edge points acquired by the alpha-shape algorithm to derive a so-called building roof skeleton. We then propose a hierarchical corner-aware segmentation to cluster skeleton points based on their properties which are the so-called separation angle, radius of the maximally inscribe circle, and defining edge point indices. From each segment, a corner point is then estimated by extrapolating the position of the zero radius inscribed circle based on the skeleton point positions within the segment. Our experiment uses point cloud datasets of Makassar, Indonesia and EYE-Amsterdam, The Netherlands. The average positional accuracy of the building outline results for Makassar and EYE-Amsterdam is 65 cm and 70 cm, respectively, which meet one-meter base map accuracy criteria. The results imply that skeletonization is a promising tool to extract relevant geometric information on e.g. building outlines even from far from perfect geographical point cloud data.
Highlights
Mapping building roof outlines, called building footprints, is essential for digital base map cartography, planning, surveillance, infrastructure management and sustainable city design
Given an airborne point cloud of an urban area, we propose a method for extracting building outlines automatically by detecting accurate roof corner points based on Medial Axis Transform (MAT) descriptors
Our research focuses on the adaptation of MAT for extracting building outlines from noisy point cloud data required for mapping and spatial modeling purposes
Summary
Called building footprints, is essential for digital base map cartography, planning, surveillance, infrastructure management and sustainable city design. Research on extracting building outlines automatically from high-resolution data remains challenging due to the complexity of roof structures and variations in the design of our urban environment. Medial axis transform (MAT), is a powerful shape extraction technique that provides a compact geometrical representation while preserving topological properties of the input shape [1,4]. The MAT was introduced by Blum [5] to describe biological shapes. Since it has been used for applications in image processing and computer vision. Wider deployment of MAT to extract shapes analysis from surveying quality data with its associated problems, is still challenging [4]
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