3D point cloud segmentation using GIS
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Abstract (arXiv)
In this paper we propose an approach to perform semantic segmentation of 3D point cloud data by importing the geographic information from a 2D GIS layer (OpenStreetMap). The proposed automatic procedure identifies meaningful units such as buildings and adjusts their locations to achieve best fit between the GIS polygonal perimeters and the point cloud. Our processing pipeline is presented and illustrated by segmenting point cloud data of Trinity College Dublin (Ireland) campus constructed from optical imagery collected by a drone.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 13 Aug 2021.
Builds on
6 of 17 references.
References · 6
- 2017SEGCloud: Semantic Segmentation of 3D Point Cloudscited by 178
- 2017Learning Aerial Image Segmentation from Online Mapscited by 30
- 2017Automatic Discovery and Geotagging of Objects from Street View Imagerycited by 17
- 2019From Google Maps to a Fine-Grained Catalog of Street treescited by 15
- 2016Efficient Volumetric Fusion of Airborne and Street-Side Data for Urban Reconstructioncited by 6
- 2017Classification of Aerial Photogrammetric 3D Point Cloudscited by 2
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