@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV	 = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
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@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})

@article{tseng_2023, title={Lightweight, Pre-trained Transformers for Remote Sensing Timeseries}, url={http://arxiv.org/abs/2304.14065}, DOI={10.48550/arXiv.2304.14065}, note={arXiv:2304.14065 [cs]}, number={arXiv:2304.14065}, publisher={arXiv}, author={Tseng, Gabriel and Cartuyvels, Ruben and Zvonkov, Ivan and Purohit, Mirali and Rolnick, David and Kerner, Hannah}, year={2023}, month=sep }

@misc{tseng2024lightweightpretrainedtransformersremote,
      title={Lightweight, Pre-trained Transformers for Remote Sensing Timeseries}, 
      author={Gabriel Tseng and Ruben Cartuyvels and Ivan Zvonkov and Mirali Purohit and David Rolnick and Hannah Kerner},
      year={2024},
      eprint={2304.14065},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2304.14065}, 
}

@misc{bastani2023satlaspretrainlargescaledatasetremote,
      title={SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding}, 
      author={Favyen Bastani and Piper Wolters and Ritwik Gupta and Joe Ferdinando and Aniruddha Kembhavi},
      year={2023},
      eprint={2211.15660},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2211.15660}, 
}

@misc{smith2024earthpttimeseriesfoundation,
      title={EarthPT: a time series foundation model for Earth Observation}, 
      author={Michael J. Smith and Luke Fleming and James E. Geach},
      year={2024},
      eprint={2309.07207},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2309.07207}, 
}

@misc{irvin2023usatunifiedselfsupervisedencoder,
      title={USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery}, 
      author={Jeremy Irvin and Lucas Tao and Joanne Zhou and Yuntao Ma and Langston Nashold and Benjamin Liu and Andrew Y. Ng},
      year={2023},
      eprint={2312.02199},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2312.02199}, 
}

@misc{xiong2024allunifiedfoundationmodels,
      title={One for All: Toward Unified Foundation Models for Earth Vision}, 
      author={Zhitong Xiong and Yi Wang and Fahong Zhang and Xiao Xiang Zhu},
      year={2024},
      eprint={2401.07527},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2401.07527}, 
}

@misc{wang2024mtpadvancingremotesensing,
      title={MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining}, 
      author={Di Wang and Jing Zhang and Minqiang Xu and Lin Liu and Dongsheng Wang and Erzhong Gao and Chengxi Han and Haonan Guo and Bo Du and Dacheng Tao and Liangpei Zhang},
      year={2024},
      eprint={2403.13430},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2403.13430}, 
}

@misc{jiang2024lemevitefficientvisiontransformer,
      title={LeMeViT: Efficient Vision Transformer with Learnable Meta Tokens for Remote Sensing Image Interpretation}, 
      author={Wentao Jiang and Jing Zhang and Di Wang and Qiming Zhang and Zengmao Wang and Bo Du},
      year={2024},
      eprint={2405.09789},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2405.09789}, 
}

@ARTICLE{10697182,
  author={An, Xiao and He, Wei and Zou, Jiaqi and Yang, Guangyi and Zhang, Hongyan},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Pretrain a Remote Sensing Foundation Model by Promoting Intra-Instance Similarity}, 
  year={2024},
  volume={62},
  number={},
  pages={1-15},
  keywords={Remote sensing;Feature extraction;Contrastive learning;Annotations;Training;Sensors;Buildings;Space exploration;Satellites;Reviews;Foundation model;intra-instance similarity;positive representation space;remote sensing;self-supervised learning (SSL)},
  doi={10.1109/TGRS.2024.3469568}}

@misc{szwarcman2025prithvieo20versatilemultitemporalfoundation,
      title={Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications}, 
      author={Daniela Szwarcman and Sujit Roy and Paolo Fraccaro and Þorsteinn Elí Gíslason and Benedikt Blumenstiel and Rinki Ghosal and Pedro Henrique de Oliveira and Joao Lucas de Sousa Almeida and Rocco Sedona and Yanghui Kang and Srija Chakraborty and Sizhe Wang and Carlos Gomes and Ankur Kumar and Myscon Truong and Denys Godwin and Hyunho Lee and Chia-Yu Hsu and Ata Akbari Asanjan and Besart Mujeci and Disha Shidham and Trevor Keenan and Paulo Arevalo and Wenwen Li and Hamed Alemohammad and Pontus Olofsson and Christopher Hain and Robert Kennedy and Bianca Zadrozny and David Bell and Gabriele Cavallaro and Campbell Watson and Manil Maskey and Rahul Ramachandran and Juan Bernabe Moreno},
      year={2025},
      eprint={2412.02732},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.02732}, 
}

@misc{li2024seamomultiseasonalmultimodalremote,
      title={SeaMo: A Multi-Seasonal and Multimodal Remote Sensing Foundation Model}, 
      author={Xuyang Li and Danfeng Hong and Chenyu Li and Jocelyn Chanussot},
      year={2024},
      eprint={2412.19237},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.19237}, 
}
@inproceedings{Reed_Gupta_Li_Brockman_Funk_Clipp_Keutzer_Candido_Uyttendaele_Darrell_2023, title={Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning}, url={http://openaccess.thecvf.com/content/ICCV2023/html/Reed_Scale-MAE_A_Scale-Aware_Masked_Autoencoder_for_Multiscale_Geospatial_Representation_Learning_ICCV_2023_paper.html}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, author={Reed, Colorado J. and Gupta, Ritwik and Li, Shufan and Brockman, Sarah and Funk, Christopher and Clipp, Brian and Keutzer, Kurt and Candido, Salvatore and Uyttendaele, Matt and Darrell, Trevor}, year={2023}, pages={4088–4099} }

 @article{dAndrimont2021, title={LUCAS Copernicus 2018: Earth-observation-relevant in situ data on land cover and use throughout the European Union}, volume={13}, ISSN={1866-3508}, DOI={10.5194/essd-13-1119-2021}, number={3}, journal={Earth System Science Data}, publisher={Copernicus GmbH}, author={d’Andrimont, Raphaël and Verhegghen, Astrid and Meroni, Michele and Lemoine, Guido and Strobl, Peter and Eiselt, Beatrice and Yordanov, Momchil and Martinez-Sanchez, Laura and van der Velde, Marijn}, year={2021}, month=mar, pages={1119–1133}, language={English} }


@misc{CopernicusDEM,
  author       = {The European Space Agency},
  title        = {{Copernicus Digital Elevation Model (DEM) GLO-30}},
  howpublished = {\url{https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM}},
  note         = {Accessed: 2025-02-28}
}

@misc{USDA_NASS_CDL2021,
  title        = {Cropland Data Layer},
  author       = {{USDA National Agricultural Statistics Service}},
  year         = {2021},
  howpublished = {\url{https://croplandcros.scinet.usda.gov/}},
  note         = {Published crop-specific data layer. USDA-NASS, USDA NASS Marketing and Information Services Office, Washington, DC. Accessed: 2025-02-23.}
}

@misc{astruc2024anysatearthobservationmodel,
      title={AnySat: An Earth Observation Model for Any Resolutions, Scales, and Modalities}, 
      author={Guillaume Astruc and Nicolas Gonthier and Clement Mallet and Loic Landrieu},
      year={2024},
      eprint={2412.14123},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.14123}, 
}

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  year={2023}
}

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@article{poggio2021soilgrids,
  title={SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty},
  author={Poggio, Laura and De Sousa, Luis M and Batjes, Niels H and Heuvelink, Gerard BM and Kempen, Bas and Ribeiro, Eloi and Rossiter, David},
  journal={Soil},
  volume={7},
  number={1},
  pages={217--240},
  year={2021},
  publisher={Copernicus Publications G{\"o}ttingen, Germany}
}

@inproceedings{robinson2023rapid,
  title={Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event},
  author={Robinson, Caleb and Nsutezo, Simone Fobi and Ortiz, Anthony and Sederholm, Tina and Dodhia, Rahul and Birge, Cameron and Richards, Kasie and Pitcher, Kris and Duarte, Paulo and Ferres, Juan M Lavista},
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@inproceedings{zvonkov2023openmapflow,
  title={Openmapflow: A library for rapid map creation with machine learning and remote sensing data},
  author={Zvonkov, Ivan and Tseng, Gabriel and Nakalembe, Catherine and Kerner, Hannah},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={12},
  pages={14655--14663},
  year={2023}
}

@book{nakalembe2023considerations,
  title={Considerations for AI-EO for agriculture in Sub-Saharan Africa},
  author={Nakalembe, Catherine and Kerner, Hannah},
  year={2023},
  publisher={Institute of Physics}
}

@inproceedings{garnot2019time,
  title={Time-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series},
  author={Garnot, V Sainte Fare and Landrieu, Loic and Giordano, Sebastien and Chehata, Nesrine},
  booktitle={IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium},
  pages={6247--6250},
  year={2019},
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@article{fuller2023croma,
  title={CROMA: Remote sensing representations with contrastive radar-optical masked autoencoders},
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@inproceedings{
tseng2025galileo,
title={Galileo: Learning Global \& Local Features of Many Remote Sensing Modalities},
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year={2025},
}

@inproceedings{reed2023scale,
  title={Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning},
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  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
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@article{cong2022satmae,
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@article{van2023worldcereal,
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@article{boogaard2020agrometeorological,
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@inproceedings{tseng2021cropharvest,
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@inproceedings{fibaek_phileo, title={PhilEO bench: Evaluating geo-spatial foundation models}, url={https://ieeexplore.ieee.org/abstract/document/10642780/}, booktitle={IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium}, publisher={IEEE}, author={Fibaek, Casper and Camilleri, Luke and Luyts, Andreas and Dionelis, Nikolaos and Le Saux, Bertrand}, year={2024}, pages={2739–2744} }

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