# 3D point cloud segmentation using GIS

arXiv: https://arxiv.org/abs/2108.06306 · HTML: https://mlsanity.com/paper/2108.06306/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 13 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [SEGCloud: Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/1710.07563/) (2017)
- [Learning Aerial Image Segmentation from Online Maps](https://mlsanity.com/paper/1707.06879/) (2017)
- [Automatic Discovery and Geotagging of Objects from Street View Imagery](https://mlsanity.com/paper/1708.08417/) (2017)
- [From Google Maps to a Fine-Grained Catalog of Street trees](https://mlsanity.com/paper/1910.02675/) (2019)
- [Efficient Volumetric Fusion of Airborne and Street-Side Data for Urban Reconstruction](https://mlsanity.com/paper/1609.01345/) (2016)
- [Classification of Aerial Photogrammetric 3D Point Clouds](https://mlsanity.com/paper/1705.08374/) (2017)

## 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.