The supplementary material for our CROMA submission consists of:
1. Our Appendix
   1. Please see the file titled "croma_appendix.pdf"
2. Our pretrained models and preprocessed datasets (shared anonymously): https://zenodo.org/record/7954787
   1. Model and dataset files can be loaded with PyTorch, i.e., torch.load(file_name)
   2. Model files are PyTorch state-dictionaries that can be loaded into CROMA models
   3. Dataset files are python dictionaries that are split into train and validation sets
3. Our minimal codebase that can be used to replicate our results via pretraining or finetuning CROMA

We will happily provide more detailed documentation upon publication or request.

References for the Shared Datasets (on Zenodo)

@inproceedings{
    title        = {{Towards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification}},
    author       = {Jacques, Amanda A Boatswain and Diallo, Abdoulaye Banir{\'e} and Lord, Etienne},
    year         = {2021},
    booktitle    = {42nd Canadian Symposium on Remote Sensing: Understanding Our World: Remote Sensing for a Sustainable Future}
}

@article{
    title        = {{Dynamic World, Near real-time global 10 m land use land cover mapping}},
    author       = {Brown, Christopher F and Brumby, Steven P and Guzder-Williams, Brookie and Birch, Tanya and Hyde, Samantha Brooks and Mazzariello, Joseph and Czerwinski, Wanda and Pasquarella, Valerie J and Haertel, Robert and Ilyushchenko, Simon and others},
    year         = {2022},
    journal      = {Scientific Data}
}

@article{
    title        = {{MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data}},
    author       = {Kikaki, Katerina and Kakogeorgiou, Ioannis and Mikeli, Paraskevi and Raitsos, Dionysios E and Karantzalos, Konstantinos},
    year         = {2022},
    journal      = {PloS one}
}

@article{
    title        = {{2020 IEEE GRSS Data Fusion Contest: Global Land Cover Mapping With Weak Supervision}},
    author       = {Yokoya, Naoto and Ghamisi, Pedram and H{\"a}nsch, Ronny and Schmitt, Michael},
    year         = {2020},
    journal      = {IEEE Geoscience and Remote Sensing Magazine}
}

@inproceedings{
    title        = {{SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery}},
    author       = {Yezhen Cong and Samar Khanna and Chenlin Meng and Patrick Liu and Erik Rozi and Yutong He and Marshall Burke and David B. Lobell and Stefano Ermon},
    year         = {2022},
    booktitle    = {NeurIPS}
}