Published April 19, 2023 | Version v1
Dataset Open

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

  • 1. LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS, France - LASTIG, Univ Gustave Eiffel, IGN, ENSG
  • 2. LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS, France - INRIA and DIENS (ENS-PSL, CNRS, INRIA)
  • 3. LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS, France

Description

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.

Files

crop_field.zip

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Additional details

Funding

DISCOVER – Discovering and Analyzing Visual Structures 101076028
European Commission
READY3D – Real-Time Analysis of Dynamic LiDAR 3D Point Clouds ANR-19-CE23-0007
Agence Nationale de la Recherche