Automatic labelling of urban point clouds using data fusion
This work addresses the challenge of efficiently generating labeled datasets for urban point cloud segmentation, which is incremental as it builds on existing data fusion and segmentation techniques.
The paper tackles the problem of creating labeled datasets for semantic segmentation of urban point clouds by using data fusion from public sources like elevation data and maps to automatically label parts, reducing human effort and time needed. They applied this method to point clouds in Amsterdam and successfully trained a RandLA-Net model, demonstrating its potential for smart city applications.
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.