ree-dimensional models of buildings have a variety of applications, e.g., in urban planning, for making decision where to locate power lines, solar panels, cellular antennas, etc. O.en, 3D models are created from a LiDAR point cloud, however, this presents three challenges. First, to generate maps at a nationwide scale or even for a large city, it is essential to e.ectively store and process the data. Second, there is a need to produce a compact representation of the result, to avoid representing each building as thousands of points.ird, it is o.en required to seamlessly integrate computed models with non-geospatial features of the geospatial entities. In this paper, we demonstrate an end-to-end automation of a large-scale 3D-model creation for buildings.e tool compacts the point cloud and allows to e.ortlessly integrate the results with information stored in a database.e main motivation for our tool is 5G network planning, where antenna locations require careful consideration, given that buildings and trees could obstruct or re.ect high-frequency cellular transmissions.
CITATION STYLE
Brown, P. E., Kanza, Y., & Kounev, V. (2019). Height and facet extraction from liDAR point cloud for automatic creation of 3d building models. In GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems (pp. 596–599). Association for Computing Machinery. https://doi.org/10.1145/3347146.3359372
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