TlseHypDataSet

TlseHypDataSet is a Python library to flexibly load PyTorch datasets and run machine learning experiments on the Toulouse Hyperspectral Data Set.

• Getting started
• Dataset
• Spatially disjoint ground truth splits
• Core-set selection
• Transforms
• Utils
• Contribute

If you use the TlseHypDataSet library, please cite the following two articles:

@article{ROUPIOZ2023109109,
title = {Multi-source datasets acquired over Toulouse (France) in 2021 for urban microclimate studies during the CAMCATT/AI4GEO field campaign},
journal = {Data in Brief},
volume = {48},
pages = {109109},
year = {2023},
issn = {2352-3409},
doi = {https://doi.org/10.1016/j.dib.2023.109109},
url = {https://www.sciencedirect.com/science/article/pii/S2352340923002287},
author = {L. Roupioz and X. Briottet and K. Adeline and A. {Al Bitar} and D. Barbon-Dubosc and R. Barda-Chatain and P. Barillot and S. Bridier and E. Carroll and C. Cassante and A. Cerbelaud and P. Déliot and P. Doublet and P.E. Dupouy and S. Gadal and S. Guernouti and A. {De Guilhem De Lataillade} and A. Lemonsu and R. Llorens and R. Luhahe and A. Michel and A. Moussous and M. Musy and F. Nerry and L. Poutier and A. Rodler and N. Riviere and T. Riviere and J.L. Roujean and A. Roy and A. Schilling and D. Skokovic and J. Sobrino},
keywords = {Land surface temperature, Spectral emissivity, Spectral reflectance, Air temperature, Airborne LiDAR, Atmospheric data, Urban area},
}

@misc{thoreau2023toulouse,
  title={Toulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques},
  author={Romain Thoreau and Laurent Risser and Véronique Achard and Béatrice Berthelot and Xavier Briottet},
  year={2023},
  eprint={2311.08863},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
 }