Update README.md

Udd preprint link

README.md

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 This is the official repository associated with the pre-print: "Open-Canopy: A Country-Scale Benchmark for Canopy Height Estimation at Very High Resolution".
 
-- **Datapaper :** The pre-print will be available on arXiv early July 2024.
+- **Datapaper :** Pre-print on arXiv: https://arxiv.org/abs/2407.09392.
 
 - **Size :** Approximately 300GB.
 - **Github link :** https://github.com/fajwel/Open-Canopy. 
@@ -55,6 +55,20 @@ Codes for preprocessing, training a custom model and evaluation will be availabl
 ### Evaluation of canopy height estimation
 ### Evaluation of canopy height change estimation -->
 
+## Reference
+Please include a citation to the following article if you use the Open-Canopy dataset:
+
+```bibtex
+@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 Phillipe 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](https://anr.fr/Projet-ANR-22-FAI1-0002)), the German Aerospace Center ([DLR](https://www.dlr.de/en)) and the German federal ministry for education and research ([BMBF](https://www.bmbf.de/bmbf/en/home/home_node.html)).
 The experiments conducted in this study were performed using HPC/AI resources provided by GENCI-IDRIS (Grant 2023-AD010114718 and 2023-AD011014781) and [Inria](https://inria.fr/fr).