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Building and Road Extraction from LiDAR Data
Published in Jie Shan, Charles K. Toth, Topographic Laser Ranging and Scanning, 2018
Franz Rottensteiner, Simon Clode
Point cloud classification is only a first step for the extraction of objects such as buildings. The second step requires the definition of contiguous objects, for example, represented by boundary polygons. This can be achieved by modified convex hull algorithms (Sampath and Shan, 2007) or alpha shapes (Dorninger and Pfeifer, 2008), which have to be applied to segments of points classified as buildings. Alternatively, coarse building outlines can be determined on the basis of 3D line segments extracted in the subset of the point cloud classified as buildings (Lafarge and Mallet, 2012) or on a clustering of point cloud segments followed by a classification of boundary points (Poullis, 2013). In Section 16.2.4, a method for the definition of building objects on the basis of the output of a point-based CRF and a MRF is presented (Niemeyer et al., 2014).
Improving the reproducibility of geospatial scientific workflows: the use of geosocial media in facilitating disaster response
Published in Journal of Spatial Science, 2021
V. Cerutti, C. Bellman, A. Both, M. Duckham, B. Jenny, R. L. G. Lemmens, F. O. Ostermann
Shape reconstruction algorithms are not commonly used in CGSM studies. Galton and Duckham (2006) compare various methods for producing footprints, such as gift wrapping, swinging arm, close pairs, Delaunay triangulation (Arampatzis et al. 2006), Voronoi-based region approximation (Alani et al. 2010), alpha-shape (Edelsbrunner et al. 1983), and power crust (Amenta et al. 2001). Convex hull algorithms are the simplest and most frequently used, computing the smallest convex polygon that contains all the points in the given geometry, without having any angle that exceeds 180 degrees between two neighbouring edges. However, the convex hull does not represent well the boundaries of a given set of points with a pronounced non-convex distribution. Concave hull algorithms can be more efficient in these cases. A concave hull of a set of points can be defined as the shape which minimises the area of the containing shape, allowing any angle between the edges. The characteristic-shape algorithm used by Zhong et al. (2016) is an example of a concave hull algorithm.