Earth Parser Dataset: A new dataset to train and evaluate parsing methods on large, uncurated aerial LiDAR scans

We introduce a new dataset to train and evaluate parsing methods on large, uncurated aerial LiDAR scans. We use data from the French Mapping Agency associated to the LiDAR-HD project. We selected 7 scenes, covering over 7.7km2 and a total of 98 million 3D points, with diverse content and complexity, such as dense habitations, forests, or complex industrial facilities.

You can download sequences individually or use zenodo-get to download all sequences at once:

pip install zenodo-get

zenodo-get 7820686

See companion github repository and the dedicated wepage for more information.

Cite as:

@article{loiseau2024learnable,
title={Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans},
author={Romain Loiseau and Elliot Vincent and Mathieu Aubry and Loic Landrieu}, journal={CVPR},
year={2024}
}

Acknowledgements :

This work was supported by ANR project READY3D ANR-19-CE23-0007.

This work was supported by ANR under the France 2030 program under the reference ANR-23-PEIA-0008

The work of MA was partly supported by the European Research Council (ERC project DISCOVER, number 101076028).

The scenes of the Earth Parser Dataset were acquired and annotated by the LiDAR-HD project.

We thank Zenodo for hosting the dataset.