Romain Loiseau
Elliot Vincent
Mathieu Aubry
Loic Landrieu
2023-04-19
<p>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.</p>
<p>You can download sequences individually or use zenodo-get to download all sequences at once:</p>
<p><strong><em>pip install zenodo-get</em></strong></p>
<p><strong><em>zenodo-get 7820686</em></strong></p>
<p>See companion <a href="https://github.com/romainloiseau/EarthParserDataset">github</a> repository and the dedicated <a href="https://imagine.enpc.fr/~loiseaur/learnable-earth-parser">wepage</a> for more information.</p>
<p><strong>Cite as:</strong></p>
<pre><code>@misc{loiseau2023learnable,
title={Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans},
author={Romain Loiseau and Elliot Vincent and Mathieu Aubry and Loic Landrieu},
year={2023},
eprint={2304.09704},
archivePrefix={arXiv},
primaryClass={cs.CV}
}</code></pre>
<p><strong>Acknowledgements :</strong></p>
<ul>
<li>This work was supported by ANR project READY3D ANR-19-CE23-0007.</li>
<li>The work of MA was partly supported by the European Research Council (ERC project DISCOVER, number 101076028).</li>
<li>The scenes of the Earth Parser Dataset were acquired and annotated by the <strong><a href="https://geoservices.ign.fr/lidarhd">LiDAR-HD</a></strong> project.</li>
<li>We thank Zenodo for hosting the dataset.</li>
</ul>
https://doi.org/10.48550/arXiv.2304.09704
oai:zenodo.org:7820686
Zenodo
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Earth Parser Dataset: A new dataset to train and evaluate parsing methods on large, uncurated aerial LiDAR scans
info:eu-repo/semantics/other