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Building segmentation for densely built urban regions using aerial LIDAR data
Abstract
A method for extracting a 3D terrain model for identifying at
least buildings and terrain from LIDAR data is disclosed,
comprising the steps of generating a point cloud representing
terrain and buildings mapped by LIDAR; classifying points in the
point cloud, the point cloud having ground and non-ground
points, the non-ground points representing buildings and
clutter; segmenting the non-ground points into buildings and
clutter; and calculating a fit between at least one building
segment and at least one rectilinear structure, wherein the fit
yields the rectilinear structure with the fewest number of
vertices. The step of calculating further comprises the steps
of (a) calculating a fit of a rectilinear structure to the at
least one building segment, wherein each of the vertices has an
angle that is a multiple of 90 degrees; (b) counting the number
of vertices; (c) rotating the at least one building segment
about an axis by a predetermined increment; and (d) repeating
steps (a)-(c) until a rectilinear structure with the least
number of vertices is found.
The reconstruction of level-of-detail 2 (LOD-2) buildings has drawn considerable attention over the past two decades. Since completely automatic reconstruction approaches still face many difficulties in industry solutions, efficient and robust interactive modeling tools are of huge demand. This paper proposes an interactive LOD-2 modeling approach that can generate boundary-precise and topology-correct building models efficiently from Digital Surface Model (DSM) and Digital Orthophoto Map (DOM) data. Click-based saliency segmentation is first adopted to extract the outline of building primitives, where both photometric and geometric information are unified. Those outlines decompose the complex-shaped buildings into predefined primitive types. The primitives are then further enriched with other semantic key points, which are essential for inferring the primitives’ shapes and positions. Finally, the models can be built based on the constructive solid geometry and assembly of partial primitives into complex buildings. The order of the primitives can be irrelevant, and the sketches and semantic key points do not necessarily be perfect. Users only need very few and simple operators to generate the integral models, mostly several clicks on a 2D platform. Experimental results on various dataset demonstrate the proposed approach can minimize the amount of interaction while maintaining the ability to deal with various roof types. The integral model quality indices at pixel and object level achieve the precision of 92.3% and 82.5%, accordingly.
The form and function of the modern city are defined by the three-dimensional contours of the built environment. The morphology of the urban landscape has significant implications for a city's sustainability, efficiency, and resilience. With advancements in remote sensing, especially airborne Light Detection and Ranging (LiDAR), the potential exists to model urban topography at an unprecedented spatial resolution and granularity and extract previously unavailable characteristics of individual buildings. In this study, we demonstrate the application of point-based voxelization techniques to extract design parameters in complex urban environments at unprecedented scale using New York City, and its more than 1,000,000 buildings, as a test case. Covering approximately 800 km², we develop a 1 m² resolution Digital Surface Model (DSM) derived from aerial LiDAR point cloud data, together with city administrative records, to calculate building massing, height, volume, exposed surface area, and compactness ratios for every building in the City. The proposed scalable approach creates a significant opportunity for city administrators, urban planners, architectural engineers, and building designers to understand the relationship between urban morphology and a range of infrastructure and environmental systems.
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