Datasets UOPNOA and UOS2. Each dataset contains images and labels to train and test a semantic segmentation model for crop classification / land use with satellite or aircraft imagery. The region of intereset is the northern UPNOA is made out of PNOA aircraft imagery and uses RGB images. (34.000 images) UOS2 is made out of Sentinel-2 satellite imagery and uses 13 bands or channels per image. (2.000 images) Ground truth masks were made from SIGPAC data from the northern part of the Iberian Peninsula plateau in Spain. Originally trained with UNet and DeepLabv3+ Please cite the original paper, which can be found at: https://doi.org/10.3390/rs13122292 BibTex: @article{pedrayes2021evaluation, title={Evaluation of Semantic Segmentation Methods for Land Use with Spectral Imaging Using Sentinel-2 and PNOA Imagery}, author={Pedrayes, Oscar D and Lema, Dar{\'\i}o G and Garc{\'\i}a, Daniel F and Usamentiaga, Rub{\'e}n and Alonso, {\'A}ngela}, journal={Remote Sensing}, volume={13}, number={12}, pages={2292}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} }