pierreadorni/Open-Canopy-Small-Comparable

Open-Canopy: Towards Very High Resolution Forest Monitoring - Small Comparable Subset

This is not the official repository for the dataset "Open-Canopy". For the original work, please visit the following links:

• Datapaper : Pre-print on arXiv: https://arxiv.org/abs/2407.09392.
• Code : https://github.com/fajwel/Open-Canopy
• Dataset link : https://huggingface.co/datasets/AI4Forest/Open-Canopy.

Small Comparable Subset

The original Open-Canopy dataset is approx. 360Gb on disk. This repo contains a subset tailored for RS model benchmarking on the canopy height estimation task, weighing approx. 170Gb on disk. The val and test set are kept as-is for comparability purposes; for an even smaller version, please refer to the Non-Comparable subset: https://huggingface.co/datasets/pierreadorni/Open-Canopy-Small

Summary of the modifications

• Train set: reduced to 10% (66,339 -> 6,634)
• Val set: kept intact
• Test set: kept intact

Some Details

The train subset was uniformly selected in geographic space. The validation and test sets were kept intact to allow for direct comparison with the original dataset, thus the name Open-Canopy-Small-Comparable.

The non-selected samples of the train set were overwritten with zeros in the SPOT, lidar and lidar_classification tiles, to reduce their file size.

the SPOT, lidar, and lidar_classification files are kept but pruned, while the canopy_height_change, lidar_v2, pretrained_models, predictions, and .vrt files are removed.

The following sections of the README were copied from the original Open-Canopy dataset's for Citation, Ackowledgements, Licence and Authors: https://huggingface.co/datasets/AI4Forest/Open-Canopy

Reference

Please include a citation to the following article if you use the Open-Canopy dataset:

@article{fogel2024opencanopy,
      title={Open-Canopy: A Country-Scale Benchmark for Canopy Height Estimation at Very High Resolution},
      author={Fajwel Fogel and Yohann Perron and Nikola Besic and Laurent Saint-André and Agnès Pellissier-Tanon and Martin Schwartz and Thomas Boudras and Ibrahim Fayad and Alexandre d'Aspremont and Loic Landrieu and Philippe Ciais},
      year={2024},
      eprint={2407.09392},
      publisher = {arXiv},
      url={https://arxiv.org/abs/2407.09392},
}

Acknowledgements

This paper is part of the project AI4Forest, which is funded by the French National Research Agency (ANR), the German Aerospace Center (DLR) and the German federal ministry for education and research (BMBF).

The experiments conducted in this study were performed using HPC/AI resources provided by GENCI-IDRIS (Grant 2023-AD010114718 and 2023-AD011014781) and Inria.

Dataset license

The "OPEN LICENCE 2.0/LICENCE OUVERTE" is a license created by the French government specifically for the purpose of facilitating the dissemination of open data by public administration. If you are looking for an English version of this license, you can find it at the official github page.

As stated by the license :

• Applicable legislation: This licence is governed by French law.
• Compatibility of this licence: This licence has been designed to be compatible with any free licence that at least requires an acknowledgement of authorship, and specifically with the previous version of this licence as well as with the following licences: United Kingdom’s “Open Government Licence” (OGL), Creative Commons’ “Creative Commons Attribution” (CC-BY) and Open Knowledge Foundation’s “Open Data Commons Attribution” (ODC-BY).

Authors

Fajwel Fogel (ENS), Yohann Perron (LIGM, ENPC, CNRS, UGE, EFEO), Nikola Besic (LIF, IGN, ENSG), Laurent Saint-André (INRAE, BEF), Agnès Pellissier-Tanon (LSCE/IPSL, CEA-CNRS-UVSQ), Martin Schwartz (LSCE/IPSL, CEA-CNRS-UVSQ), Thomas Boudras (LSCE/IPSL, CEA-CNRS-UVSQ), Ibrahim Fayad (LSCE/IPSL, CEA-CNRS-UVSQ, Kayrros), Alexandre d'Aspremont (CNRS, ENS, Kayrros), Loic Landrieu (LIGM, ENPC, CNRS, UGE), Philippe Ciais (LSCE/IPSL, CEA-CNRS-UVSQ).