Automatic labelling of urban point clouds using data fusion

08/31/2021
by   Daan Bloembergen, et al.
0

In this paper we describe an approach to semi-automatically create a labelled dataset for semantic segmentation of urban street-level point clouds. We use data fusion techniques using public data sources such as elevation data and large-scale topographical maps to automatically label parts of the point cloud, after which only limited human effort is needed to check the results and make amendments where needed. This drastically limits the time needed to create a labelled dataset that is extensive enough to train deep semantic segmentation models. We apply our method to point clouds of the Amsterdam region, and successfully train a RandLA-Net semantic segmentation model on the labelled dataset. These results demonstrate the potential of smart data fusion and semantic segmentation for the future of smart city planning and management.

READ FULL TEXT

page 1

page 2

page 3

research
09/19/2021

Efficient Urban-scale Point Clouds Segmentation with BEV Projection

Point clouds analysis has grasped researchers' eyes in recent years, whi...
research
06/18/2021

Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture

This paper explores the potential for performing temporal semantic segme...
research
04/27/2023

A Review of Panoptic Segmentation for Mobile Mapping Point Clouds

3D point cloud panoptic segmentation is the combined task to (i) assign ...
research
03/10/2023

Combining visibility analysis and deep learning for refinement of semantic 3D building models by conflict classification

Semantic 3D building models are widely available and used in numerous ap...
research
02/21/2022

Fast Semantic-Assisted Outlier Removal for Large-scale Point Cloud Registration

With current trends in sensors (cheaper, more volume of data) and applic...
research
08/14/2018

Treepedia 2.0: Applying Deep Learning for Large-scale Quantification of Urban Tree Cover

Recent advances in deep learning have made it possible to quantify urban...
research
06/14/2022

Monitoring Urban Forests from Auto-Generated Segmentation Maps

We present and evaluate a weakly-supervised methodology to quantify the ...

Please sign up or login with your details

Forgot password? Click here to reset