3D point cloud segmentation using GIS
This work addresses the challenge of accurately segmenting 3D point clouds for applications in urban mapping and geographic information systems, presenting an incremental improvement by combining existing GIS data with point cloud processing.
The paper tackles the problem of semantic segmentation of 3D point cloud data by integrating geographic information from 2D GIS layers, specifically using OpenStreetMap to identify and adjust building units for better alignment with point clouds, as demonstrated on drone-collected data of Trinity College Dublin campus.
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.