@String(PAMI = {{I}{E}{E}{E} {T}rans. {P}attern {A}nal. {M}ach. {I}ntell.})
@String(IJCV = {{I}nt. {J}. {C}omput. {V}is.})
@String(CVPR= {{I}{E}{E}{E} {C}onf. {C}omput. {V}is. {P}attern {R}ecog.})
@String(ICCV= {{I}nt. {C}onf. {C}omput. {V}is.})
@String(ECCV= {{E}ur. {C}onf. {C}omput. {V}is.})
@String(NIPS= {{A}dv. {N}eural {I}nform. {P}rocess. {S}yst.})
@String(ICPR = {{I}nt. {C}onf. {P}attern {R}ecog.})
@String(BMVC= {{B}rit. {M}ach. {V}is. {C}onf.})
@String(TOG= {{A}{C}{M} {T}rans. {G}raph.})
@String(TIP  = {{I}{E}{E}{E} {T}rans. {I}mage {P}rocess.})
@String(TVCG  = {{I}{E}{E}{E} {T}rans. {V}is. {C}omput. {G}raph.})
@String(TMM  = {{I}{E}{E}{E} {T}rans. {M}ultimedia})
@String(ACMMM= {{A}{C}{M} {I}nt. {C}onf. {M}ultimedia})
@String(ICME = {{I}nt. {C}onf. {M}ultimedia and {E}xpo})
@String(ICASSP=	{{I}{C}{A}{S}{S}{P}})
@String(ICIP = {{I}{E}{E}{E} {I}nt. {C}onf. {I}mage {P}rocess.})
@String(ACCV  = {{A}{C}{C}{V}})
@String(ICLR = {{I}nt. {C}onf. {L}earn. {R}epresent.})
@String(IJCAI = {{I}{J}{C}{A}{I}})
@String(PR   = {{P}attern {R}ecognition})
@String(AAAI = {{A}{A}{A}{I}})
@String(CVPRW= {{I}{E}{E}{E} {C}onf. {C}omput. {V}is. {P}attern {R}ecog. {W}orksh.})
@String(CSVT = {{I}{E}{E}{E} {T}rans. {C}ircuit {S}yst. {V}ideo {T}echnol.})

@String(SPL	= {{I}{E}{E}{E} {S}ign. {P}rocess. {L}etters})
@String(VR   = {{V}is. {R}es.})
@String(JOV	 = {{J}. {V}is.})
@String(TVC  = {{T}he {V}is. {C}omput.})
@String(JCST  = {{J}. {C}omput. {S}ci. {T}ech.})
@String(CGF  = {{C}omput. {G}raph. {F}orum})
@String(CVM = {{C}omputational {V}isual {M}edia})

@String(PAMI  = {{I}{E}{E}{E} {T}{P}{A}{M}{I}})
@String(IJCV  = {{I}{J}{C}{V}})
@String(CVPR  = {{C}{V}{P}{R}})
@String(ICCV  = {{I}{C}{C}{V}})
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@String(NIPS  = {{N}eur{I}{P}{S}})
@String(ICPR  = {{I}{C}{P}{R}})
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@String(TOG   = {{A}{C}{M} {T}{O}{G}})
@String(TIP   = {{I}{E}{E}{E} {T}{I}{P}})
@String(TVCG  = {{I}{E}{E}{E} {T}{V}{C}{G}})
@String(TCSVT = {{I}{E}{E}{E} {T}{C}{S}{V}{T}})
@String(TMM   =	{{I}{E}{E}{E} {T}{M}{M}})
@String(ACMMM = {{A}{C}{M} {M}{M}})
@String(ICME  =	{{I}{C}{M}{E}})
@String(ICASSP=	{{I}{C}{A}{S}{S}{P}})
@String(ICIP  = {{I}{C}{I}{P}})
@String(ACCV  = {{A}{C}{C}{V}})
@String(ICLR  = {{I}{C}{L}{R}})
@String(IJCAI = {{I}{J}{C}{A}{I}})
@String(PR = {{P}{R}})
@String(AAAI = {{A}{A}{A}{I}})
@String(CVPRW= {{C}{V}{P}{R}{W}})
@String(CSVT = {{I}{E}{E}{E} {T}{C}{S}{V}{T}})

@article{vaswani2017attention,
  title={{A}ttention is all you need},
  author={Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan Gomez and Łukasz Kaiser},
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  year={2017},
}

@article{mosaiks,
  title={{A} generalizable and accessible approach to machine learning with global satellite imagery},
  author={Rolf, Esther and Proctor, Jonathan and Carleton, Tamma and Bolliger, Ian and Shankar, Vaishaal and Ishihara, Miyabi and Recht, Benjamin and Hsiang, Solomon},
  journal={{N}ature communications},
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  publisher={Nature Publishing Group UK London}
}

@inproceedings{geopriors,
  title={{P}resence-{O}nly {G}eographical {P}riors for {F}ine-{G}rained {I}mage {C}lassification},
  author={Mac Aodha, Oisin and Cole, Elijah and Perona, Pietro},
  booktitle={{I}{C}{C}{V}},
  year={2019}
}
@inproceedings{loc1,
  title={{I}mproving image classification with location context},
  author={Tang, Kevin and Paluri, Manohar and Fei-Fei, Li and Fergus, Rob and Bourdev, Lubomir},
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  year={2015}
}
@inproceedings{loc2,
  title={{G}eo-aware networks for fine-grained recognition},
  author={Chu, Grace and Potetz, Brian and Wang, Weijun and Howard, Andrew and Song, Yang and Brucher, Fernando and Leung, Thomas and Adam, Hartwig},
  booktitle={{P}roceedings of the {I}{E}{E}{E}/{C}{V}{F} {I}nternational {C}onference on {C}omputer {V}ision {W}orkshops},
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  year={2019}
}

@inproceedings{torchgeo,
  title={{T}orchgeo: deep learning with geospatial data},
  author={Stewart, Adam J and Robinson, Caleb and Corley, Isaac A and Ortiz, Anthony and Ferres, Juan M Lavista and Banerjee, Arindam},
  booktitle={{P}roceedings of the 30th international conference on advances in geographic information systems},
  pages={1--12},
  year={2022}
}

@article{eurocrop,
  title={{E}uro{C}rops: {T}he largest harmonized open crop dataset across the {E}uropean {U}nion},
  author={Schneider, Maja and Schelte, Tobias and Schmitz, Felix and K{\"o}rner, Marco},
  journal={{S}cientific {D}ata},
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  number={1},
  pages={612},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@article{dem,
  title={{T}he {S}huttle {R}adar {T}opography {M}ission? a new source of near-global digital elevation data},
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}
@article{dw,
  title={{D}ynamic {W}orld: {N}ear 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},
  journal={{S}cientific {D}ata},
  volume={9},
  number={1},
  pages={251},
  year={2022},
  publisher={Nature Publishing Group UK London}
}
@inproceedings{inat,
  title={{T}he inaturalist species classification and detection dataset},
  author={Van Horn, Grant and Mac Aodha, Oisin and Song, Yang and Cui, Yin and Sun, Chen and Shepard, Alex and Adam, Hartwig and Perona, Pietro and Belongie, Serge},
  booktitle={{P}roceedings of the {I}{E}{E}{E} conference on {C}omputer {V}ision and {P}attern {R}ecognition},
  pages={8769--8778},
  year={2018}
}

@article{presto,
  title={{L}ightweight, pre-trained transformers for remote sensing timeseries},
  author={Tseng, Gabriel and Cartuyvels, Ruben and Zvonkov, Ivan and Purohit, Mirali and Rolnick, David and Kerner, Hannah},
  journal={ar{X}iv preprint ar{X}iv:2304.14065},
  year={2023}
}

@inproceedings{mmearth,
  title={{M}{M}{E}arth: {E}xploring multi-modal pretext tasks for geospatial representation learning},
  author={Nedungadi, Vishal and Kariryaa, Ankit and Oehmcke, Stefan and Belongie, Serge and Igel, Christian and Lang, Nico},
  booktitle={European Conference on Computer Vision},
  pages={164--182},
  year={2024},
  organization={Springer}
}

@article{eurosat,
  title={{E}urosat: {A} novel dataset and deep learning benchmark for land use and land cover classification},
  author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
  journal={{I}{E}{E}{E} {J}ournal of {S}elected {T}opics in {A}pplied {E}arth {O}bservations and {R}emote {S}ensing},
  volume={12},
  number={7},
  pages={2217--2226},
  year={2019},
  publisher={IEEE}
}

@article{so2sat,
  title={{S}o2Sat {L}{C}{Z}42: {A} benchmark dataset for global local climate zones classification},
  author={Zhu, Xiao Xiang and Hu, Jingliang and Qiu, Chunping and Shi, Yilei and Kang, Jian and Mou, Lichao and Bagheri, Hossein and H{\"a}berle, Matthias and Hua, Yuansheng and Huang, Rong and others},
  journal={ar{X}iv preprint ar{X}iv:1912.12171},
  year={2019}
}

@inproceedings{bigearthnet,
  title={{B}igearthnet: {A} large-scale benchmark archive for remote sensing image understanding},
  author={Sumbul, Gencer and Charfuelan, Marcela and Demir, Beg{\"u}m and Markl, Volker},
  booktitle={{I}{G}{A}{R}{S}{S} 2019-2019 {I}{E}{E}{E} {I}nternational {G}eoscience and {R}emote {S}ensing {S}ymposium},
  pages={5901--5904},
  year={2019},
  organization={IEEE}
}

@article{geobench,
  title={{G}eo-bench: {T}oward foundation models for earth monitoring},
  author={Lacoste, Alexandre and Lehmann, Nils and Rodriguez, Pau and Sherwin, Evan and Kerner, Hannah and L{\"u}tjens, Bj{\"o}rn and Irvin, Jeremy and Dao, David and Alemohammad, Hamed and Drouin, Alexandre and others},
  journal={{A}dvances in {N}eural {I}nformation {P}rocessing {S}ystems},
  volume={36},
  year={2024}
}

@inproceedings{he2016deep,
  title={{D}eep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={{P}roceedings of the {I}{E}{E}{E} conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@inproceedings{rtree,
  title={{R}-trees: {A} dynamic index structure for spatial searching},
  author={Guttman, Antonin},
  booktitle={{P}roceedings of the 1984 {A}{C}{M} {S}{I}{G}{M}{O}{D} international conference on {M}anagement of data},
  pages={47--57},
  year={1984}
}

@inproceedings{mac2019presence,
  title={{P}resence-only {G}eographical {P}riors for fine-grained image classification},
  author={Mac Aodha, Oisin and Cole, Elijah and Perona, Pietro},
  booktitle={{P}roceedings of the {I}{E}{E}{E}/{C}{V}{F} {I}nternational {C}onference on {C}omputer {V}ision},
  pages={9596--9606},
  year={2019}
}

@inproceedings{benson2024multi,
  title={{M}ulti-modal learning for geospatial vegetation forecasting},
  author={Benson, Vitus and Robin, Claire and Requena-Mesa, Christian and Alonso, Lazaro and Carvalhais, Nuno and Cort{\'e}s, Jos{\'e} and Gao, Zhihan and Linscheid, Nora and Weynants, M{\'e}lanie and Reichstein, Markus},
  booktitle={{P}roceedings of the {I}{E}{E}{E}/{C}{V}{F} {C}onference on {C}omputer {V}ision and {P}attern {R}ecognition},
  pages={27788--27799},
  year={2024}
}

@inproceedings{wang2020urban2vec,
  title={{U}rban2{V}ec: {I}ncorporating {S}treet {V}iew {I}magery and {P}{O}{I}{S} for {M}ulti-{M}odal {U}rban {N}eighborhood {E}mbedding},
  author={Wang, Zhecheng and Yu, Jinhua and Wu, Ziwei and Zhang, Rui and Mao, Jiuchuan and Li, Liang and Feng, Zhiyong and Yin, Jun},
  booktitle={{P}roceedings of the 26th {A}{C}{M} {S}{I}{G}{K}{D}{D} {I}nternational {C}onference on {K}nowledge {D}iscovery \& {D}ata {M}ining},
  pages={2068--2076},
  year={2020},
  organization={ACM}
}

@article{zanaga2022esa,
  title={{E}{S}{A} {W}orld{C}over 10 m 2021 v200},
  author={Zanaga, Daniele and Van De Kerchove, Ruben and Daems, Dirk and De Keersmaecker, Wanda and Brockmann, Carsten and Kirches, Grit and Wevers, Jan and Cartus, Oliver and Santoro, Maurizio and Fritz, Steffen and others},
  year={2022},
  publisher={Zenodo}
}

@inproceedings{unet,
  title={{U}-net: {C}onvolutional networks for biomedical image segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={{M}edical image computing and computer-assisted intervention--{M}{I}{C}{C}{A}{I} 2015: 18th international conference, {M}unich, {G}ermany, {O}ctober 5-9, 2015, proceedings, part {I}{I}{I} 18},
  pages={234--241},
  year={2015},
  organization={Springer}
}

@article{lenet,
  title={{G}radient-based learning applied to document recognition},
  author={LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
  journal={{P}roceedings of the {I}{E}{E}{E}},
  volume={86},
  number={11},
  pages={2278--2324},
  year={1998},
  publisher={Ieee}
}


@InProceedings{ipm,
  title = 	 {Resolving label uncertainty with implicit posterior models},
  author =       {Rolf, Esther and Malkin, Nikolay and Graikos, Alexandros and Jojic, Ana and Robinson, Caleb and Jojic, Nebojsa},
  booktitle = 	 {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence},
  pages = 	 {1707--1717},
  year = 	 {2022},
  editor = 	 {Cussens, James and Zhang, Kun},
  volume = 	 {180},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {01--05 Aug},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v180/rolf22a/rolf22a.pdf},
  url = 	 {https://proceedings.mlr.press/v180/rolf22a.html},
  abstract = 	 {We propose a method for jointly inferring labels across a collection of data samples, where each sample consists of an observation and a prior belief about the label. By implicitly assuming the existence of a generative model for which a differentiable predictor is the posterior, we derive a training objective that allows learning under weak beliefs. This formulation unifies various machine learning settings; the weak beliefs can come in the form of noisy or incomplete labels, likelihoods given by a different prediction mechanism on auxiliary input, or common-sense priors reflecting knowledge about the structure of the problem at hand. We demonstrate the proposed algorithms on diverse problems: classification with negative training examples, learning from rankings, weakly and self-supervised aerial imagery segmentation, co-segmentation of video frames, and coarsely supervised text classification.}
}

@article{naip,
  title={{L}and cover classification and feature extraction from {N}ational {A}griculture {I}magery {P}rogram ({N}{A}{I}{P}) {O}rthoimagery: {A} review},
  author={Maxwell, Aaron E and Warner, Timothy A and Vanderbilt, Brian C and Ramezan, Christopher A},
  journal={{P}hotogrammetric {E}ngineering \& {R}emote {S}ensing},
  volume={83},
  number={11},
  pages={737--747},
  year={2017},
  publisher={American Society for Photogrammetry and Remote Sensing}
}

@inproceedings{satclip,
  title={{S}at{CLIP}: {G}lobal, general-purpose location embeddings with satellite imagery},
  author={Klemmer, Konstantin and Rolf, Esther and Robinson, Caleb and Mackey, Lester and Ru{\ss}wurm, Marc},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  year={2025}
}


@inproceedings{vit,
  title     = {An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
  author    = {Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov
               and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner
               and Mostafa Dehghani and Matthias Minderer and Georg Heigold
               and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2021},
  note      = {arXiv:2010.11929},
  url       = {https://arxiv.org/abs/2010.11929}
}

@inproceedings{scalemae,
  title={{S}cale-mae: {A} scale-aware masked autoencoder for multiscale geospatial representation learning},
  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},
  booktitle={{P}roceedings of the {I}{E}{E}{E}/{C}{V}{F} {I}nternational {C}onference on {C}omputer {V}ision},
  pages={4088--4099},
  year={2023}
}

@inproceedings{siren,
  title={{G}eographic {L}ocation {E}ncoding with {S}pherical {H}armonics and {S}inusoidal {R}epresentation {N}etworks},
  author={Marc Ru{\ss}wurm and Konstantin Klemmer and Esther Rolf and Robin Zbinden and Devis Tuia},
  booktitle={{T}he {T}welfth {I}nternational {C}onference on {L}earning {R}epresentations},
  year={2024},
  url={https://openreview.net/forum?id=PudduufFLa}
}

@article{bigearthnetv2,
  publtype={informal},
  author={Kai Norman Clasen and Leonard W. Hackel and Tom Burgert and Gencer Sumbul and Begüm Demir and Volker Markl},
  title={re{BEN}: {R}efined {B}ig{E}arth{N}et dataset for {R}emote {S}ensing {I}mage {A}nalysis},
  year={2024},
  cdate={1704067200000},
  journal={{C}o{R}{R}},
  volume={abs/2407.03653},
  url={https://doi.org/10.48550/arXiv.2407.03653}
}

@article{enviroatlas,
  title={{E}nviro{A}tlas: {A} new geospatial tool to foster ecosystem services science and resource management},
  author={Pickard, Brian R and Daniel, Jessica and Mehaffey, Megan and Jackson, Laura E and Neale, Anne},
  journal={{E}cosystem {S}ervices},
  volume={14},
  pages={45--55},
  year={2015},
  publisher={Elsevier}
}

@inproceedings{rolfposition,
  title={{P}osition: {M}ission {C}ritical--{S}atellite {D}ata is a {D}istinct {M}odality in {M}achine {L}earning},
  author={Rolf, Esther and Klemmer, Konstantin and Robinson, Caleb and Kerner, Hannah},
  booktitle={{F}orty-first {I}nternational {C}onference on {M}achine {L}earning},
  year={2024}
}

@article{s2,
  title={{S}entinel-2: {E}{S}{A}'s optical high-resolution mission for {G}{M}{E}{S} operational services},
  author={Drusch, Matthias and Del Bello, Umberto and Carlier, S{\'e}bastien and Colin, Olivier and Fernandez, Veronica and Gascon, Ferran and Hoersch, Bianca and Isola, Claudia and Laberinti, Paolo and Martimort, Philippe and others},
  journal={{R}emote sensing of {E}nvironment},
  volume={120},
  pages={25--36},
  year={2012},
  publisher={Elsevier}
}

@article{osm,
  title={{O}pen{S}treet{M}ap: {U}ser-generated street maps},
  author={Haklay, Mordechai and Weber, Patrick},
  journal={{I}{E}{E}{E} {P}ervasive computing},
  volume={7},
  number={4},
  pages={12--18},
  year={2008},
  publisher={Ieee}
}

@article{nlcd,
  title={{A} new generation of the {U}nited {S}tates {N}ational {L}and {C}over {D}atabase: {R}equirements, research priorities, design, and implementation strategies},
  author={Yang, Limin and Jin, Suming and Danielson, Patrick and Homer, Collin and Gass, Leila and Bender, Stacie M and Case, Adam and Costello, Catherine and Dewitz, Jon and Fry, Joyce and others},
  journal={{I}{S}{P}{R}{S} journal of photogrammetry and remote sensing},
  volume={146},
  pages={108--123},
  year={2018},
  publisher={Elsevier}
}

@inproceedings{sustainbench,
  title={{S}ustain{B}ench: {B}enchmarks for {M}onitoring the {S}ustainable {D}evelopment {G}oals with {M}achine {L}earning},
  author={Christopher Yeh and Chenlin Meng and Sherrie Wang and Anne Driscoll and Erik Rozi and Patrick Liu and Jihyeon Lee and Marshall Burke and David B. Lobell and Stefano Ermon},
  booktitle={{T}hirty-fifth {C}onference on {N}eural {I}nformation {P}rocessing {S}ystems {D}atasets and {B}enchmarks {T}rack (Round 2)},
  year={2021},
  url={https://openreview.net/forum?id=5HR3vCylqD}
}

@inproceedings{ermonsus,
  title={{F}arm {P}arcel {D}elineation using {S}patio-{T}emporal {C}onvolutional {N}etworks},
  author={Aung, Han Lin and Uzkent, Burak and Burke, Marshall and Lobell, David and Ermon, Stefano},
  booktitle={{P}roceedings of the {I}{E}{E}{E}/{C}{V}{F} conference on computer vision and {P}attern {R}ecognition {W}orkshops},
  pages={76--77},
  year={2020}
}

@InProceedings{deit,
  title={{T}raining data-efficient image transformers \& distillation through attention},
  author={Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and Massa, Francisco and Sablayrolles, Alexandre and Jegou, Herve},
  booktitle={{P}roceedings of the 38th {I}nternational {C}onference on {M}achine {L}earning},
  pages={10347--10357},
  year={2021},
  editor={Meila, Marina and Zhang, Tong},
  volume={139},
  series={Proceedings of Machine Learning Research},
  month={18--24 Jul},
  publisher={PMLR},
  pdf={http://proceedings.mlr.press/v139/touvron21a/touvron21a.pdf},
  url={https://proceedings.mlr.press/v139/touvron21a.html},
  abstract={Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. These high-performing vision transformers are pre-trained with hundreds of millions of images using a large infrastructure, thereby limiting their adoption. In this work, we produce competitive convolution-free transformers trained on ImageNet only using a single computer in less than 3 days. Our reference vision transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop) on ImageNet with no external data. We also introduce a teacher-student strategy specific to transformers. It relies on a distillation token ensuring that the student learns from the teacher through attention, typically from a convnet teacher. The learned transformers are competitive (85.2% top-1 acc.) with the state of the art on ImageNet, and similarly when transferred to other tasks. We will share our code and models.}
}

@inproceedings{ssltransformer,
  title={{E}merging properties in self-supervised vision transformers},
  author={Caron, Mathilde and Touvron, Hugo and Misra, Ishan and J{\'e}gou, Herv{\'e} and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand},
  booktitle={{P}roceedings of the {I}{E}{E}{E}/{C}{V}{F} international conference on computer vision},
  pages={9650--9660},
  year={2021}
}

@inproceedings{bert,
  title={ {B}{E}{R}{T}: {P}re-training of {D}eep {B}idirectional {T}ransformers for {L}anguage {U}nderstanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  editor={Burstein, Jill and Doran, Christy and Solorio, Thamar},
  booktitle={{P}roceedings of the 2019 {C}onference of the {N}orth {A}merican {C}hapter of the {A}ssociation for {C}omputational {L}inguistics: {H}uman {L}anguage {T}echnologies, {V}olume 1 (Long and Short Papers)},
  month=jun,
  year={2019},
  address={Minneapolis, Minnesota},
  publisher={Association for Computational Linguistics},
  url={https://aclanthology.org/N19-1423/},
  doi={10.18653/v1/N19-1423},
  pages={4171--4186},
  abstract={We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).}
}

@inproceedings{vpt,
  title={{V}isual {P}rompt {T}uning},
  author={Jia, Menglin and Tang, Luming and Chen, Bor-Chun and Cardie, Claire and Belongie, Serge and Hariharan, Bharath and Lim, Ser-Nam},
  booktitle={{E}uropean {C}onference on {C}omputer {V}ision ({E}{C}{C}{V})},
  year={2022}
}

@inproceedings{tile2vec,
  title={{T}ile2{V}ec: {U}nsupervised representation learning for spatially distributed data},
  author={Jean, Neal and Wang, Sherrie and Samar, Anshul and Azzari, George and Lobell, David and Ermon, Stefano},
  booktitle={{P}roceedings of the {A}{A}{A}{I} {C}onference on {A}rtificial {I}ntelligence},
  volume={33},
  number={01},
  pages={3967--3974},
  year={2019}
}

@inproceedings{seco,
  title={{S}easonal contrast: {U}nsupervised pre-training from uncurated remote sensing data},
  author={Manas, Oscar and Lacoste, Alexandre and Gir{\'o}-i-Nieto, Xavier and Vazquez, David and Rodriguez, Pau},
  booktitle={{P}roceedings of the {I}{E}{E}{E}/{C}{V}{F} {I}nternational {C}onference on {C}omputer {V}ision},
  pages={9414--9423},
  year={2021}
}

@inproceedings{rotate,
  title={{U}nsupervised representation learning by predicting image rotations},
  author={Komodakis, Nikos and Gidaris, Spyros},
  booktitle={{I}nternational {C}onference on {L}earning {R}epresentations ({I}{C}{L}{R})},
  year={2018}
}

@inproceedings{rapid,
  title={{M}ulti-scale object detection in satellite imagery based on {Y}{O}{L}{T}},
  author={Li, Wentong and Li, Wanyi and Yang, Feng and Wang, Peng},
  booktitle={{I}{G}{A}{R}{S}{S} 2019-2019 {I}{E}{E}{E} {I}nternational {G}eoscience and {R}emote {S}ensing {S}ymposium},
  pages={162--165},
  year={2019},
  organization={IEEE}
}

@article{satmae,
  title={{S}at{MAE}: {P}re-training transformers for temporal and multi-spectral satellite imagery},
  author={Cong, Yezhen and Khanna, Samar and Meng, Chenlin and Liu, Patrick and Rozi, Erik and He, Yutong and Burke, Marshall and Lobell, David and Ermon, Stefano},
  journal={{A}dvances in {N}eural {I}nformation {P}rocessing {S}ystems},
  volume={35},
  pages={197--211},
  year={2022}
}

@article{geoclip,
  title={{G}eoclip: {C}lip-inspired alignment between locations and images for effective worldwide geo-localization},
  author={Vivanco Cepeda, Vicente and Nayak, Gaurav Kumar and Shah, Mubarak},
  journal={{A}dvances in {N}eural {I}nformation {P}rocessing {S}ystems},
  volume={36},
  pages={8690--8701},
  year={2023}
}

@inproceedings{gps2vec,
  title={{GPS}2{V}ec: {T}owards generating worldwide {GPS} embeddings},
  author={Yin, Yifang and Liu, Zhenguang and Zhang, Ying and Wang, Sheng and Shah, Rajiv Ratn and Zimmermann, Roger},
  booktitle={{P}roceedings of the 27th {A}{C}{M} {S}{I}{G}{S}{P}{A}{T}{I}{A}{L} {I}nternational {C}onference on {A}dvances in {G}eographic {I}nformation {S}ystems},
  pages={416--419},
  year={2019}
}

@inproceedings{csp,
  title={{C}sp: {S}elf-supervised contrastive spatial pre-training for geospatial-visual representations},
  author={Mai, Gengchen and Lao, Ni and He, Yutong and Song, Jiaming and Ermon, Stefano},
  booktitle={{I}nternational {C}onference on {M}achine {L}earning},
  pages={23498--23515},
  year={2023},
  organization={PMLR}
}

@inproceedings{clip,
  title={{L}earning transferable visual models from natural language supervision},
  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
  booktitle={{I}nternational conference on {M}achine {L}earning},
  pages={8748--8763},
  year={2021},
  organization={PmLR}
}

@inproceedings{opensentinelmap,
  title={{O}pensentinelmap: {A} large-scale land use dataset using {O}pen{S}treet{M}ap and {S}entinel-2 imagery},
  author={Johnson, Noah and Treible, Wayne and Crispell, Daniel},
  booktitle={{P}roceedings of the {I}{E}{E}{E}/{C}{V}{F} {C}onference on {C}omputer {V}ision and {P}attern {R}ecognition},
  pages={1333--1341},
  year={2022}
}

@article{osm2,
  title={{A}utomatic extraction and filtering of openstreetmap data to generate training datasets for land use land cover classification},
  author={Fonte, Cid{\'a}lia C and Patriarca, Joaquim and Jesus, Ismael and Duarte, Diogo},
  journal={{R}emote {S}ensing},
  volume={12},
  number={20},
  pages={3428},
  year={2020},
  publisher={MDPI}
}

@article{osm3,
  title={{A}utomatic conversion of {O}{S}{M} data into {L}{U}{L}{C} maps: comparing {F}{O}{S}{S}4{G} based approaches towards an enhanced performance},
  author={Patriarca, Joaquim and Fonte, CC and Estima, Jacinto and de Almeida, J-P and Cardoso, Alberto},
  journal={{O}pen {G}eospatial {D}ata, {S}oftware and {S}tandards},
  volume={4},
  pages={1--19},
  year={2019},
  publisher={Springer}
}

@inproceedings{registers,
  title={{V}ision {T}ransformers {N}eed {R}egisters},
  author={Timoth{\'e}e Darcet and Maxime Oquab and Julien Mairal and Piotr Bojanowski},
  booktitle={{T}he {T}welfth {I}nternational {C}onference on {L}earning {R}epresentations},
  year={2024},
  url={https://openreview.net/forum?id=2dnO3LLiJ1}
}

@inproceedings{vpt2,
  author={Yuzhu Wang and Lechao Cheng and Chaowei Fang and Dingwen Zhang and Manni Duan and Meng Wang},
  title={{R}evisiting the {P}ower of {P}rompt for {V}isual {T}uning},
  year={2024},
  cdate={1704067200000},
  url={https://openreview.net/forum?id=2Y93PtAqCl},
  booktitle={{I}{C}{M}{L}}
}

@article{resnet,
  title={{D}eep {R}esidual {L}earning for {I}mage {R}ecognition},
  author={Kaiming He and X. Zhang and Shaoqing Ren and Jian Sun},
  journal={2016 {I}{E}{E}{E} {C}onference on {C}omputer {V}ision and {P}attern {R}ecognition ({C}{V}{P}{R})},
  year={2015},
  pages={770-778},
  url={https://api.semanticscholar.org/CorpusID:206594692}
}

@inproceedings{vitscaling,
  title={{G}etting {V}i{T} in {S}hape: {S}caling {L}aws for {C}ompute-{O}ptimal {M}odel {D}esign},
  author={Ibrahim Alabdulmohsin and Xiaohua Zhai and Alexander Kolesnikov and Lucas Beyer},
  booktitle={{T}hirty-seventh {C}onference on {N}eural {I}nformation {P}rocessing {S}ystems},
  year={2023},
  url={https://openreview.net/forum?id=en4LGxpd9E}
}


@ARTICLE{datacentric,
  author={Roscher, Ribana and Russwurm, Marc and Gevaert, Caroline and Kampffmeyer, Michael and Dos Santos, Jefersson A. and Vakalopoulou, Maria and Hänsch, Ronny and Hansen, Stine and Nogueira, Keiller and Prexl, Jonathan and Tuia, Devis},
  journal={IEEE Geoscience and Remote Sensing Magazine}, 
  title={Better, Not Just More: Data-centric machine learning for Earth observation}, 
  year={2024},
  volume={12},
  number={4},
  pages={335-355},
  keywords={Data models;Training;Geospatial analysis;Data integrity;Computational modeling;Terminology;Systematics;Machine learning;Analytical models;Accuracy;Earth Observing System},
  doi={10.1109/MGRS.2024.3470986}}

% paper survey for commonly used segmentation architectures in SatML

@inproceedings{long2015fully,
  title={Fully convolutional networks for semantic segmentation},
  author={Long, Jonathan and Shelhamer, Evan and Darrell, Trevor},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3431--3440},
  year={2015}
}

@inproceedings{ronneberger2015u,
  title={U-Net: Convolutional networks for biomedical image segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={234--241},
  publisher={Springer},
  year={2015}
}

@article{hou2021cunet,
  title={C-UNet: Complement UNet for remote sensing road extraction},
  author={Hou, Yuewu and Liu, Zhaoying and Zhang, Ting and Li, Yujian},
  journal={Sensors},
  volume={21},
  number={6},
  pages={2153},
  year={2021},
  doi={10.3390/s21062153}
}

@article{badrinarayanan2017segnet,
  title={SegNet: A deep convolutional encoder–decoder architecture for image segmentation},
  author={Badrinarayanan, Vijay and Kendall, Alex and Cipolla, Roberto},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={39},
  number={12},
  pages={2481--2495},
  year={2017},
  doi={10.1109/TPAMI.2016.2644615}
}

@article{weng2020water,
  title={Water areas segmentation from remote sensing images using a separable residual SegNet network},
  author={Weng, Liguo and Xu, Yiming and Xia, Min and Zhang, Yonghong and Liu, Jia and Xu, Yiqing},
  journal={ISPRS International Journal of Geo-Information},
  volume={9},
  number={4},
  pages={256},
  year={2020},
  doi={10.3390/ijgi9040256}
}

@inproceedings{zhao2017pyramid,
  title={Pyramid scene parsing network},
  author={Zhao, Hengshuang and Shi, Jianping and Qi, Xiaojuan and Wang, Xiaogang and Jia, Jiaya},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={2881--2890},
  year={2017}
}

@article{yuan2022shift,
  title={Shift pooling PSPNet: Rethinking PSPNet for building extraction in remote sensing images from entire local feature pooling},
  author={Yuan, Wei and Wang, Jin and Xu, Wenbo},
  journal={Remote Sensing},
  volume={14},
  number={19},
  pages={4889},
  year={2022},
  doi={10.3390/rs14194889}
}

@inproceedings{xie2021segformer,
  title={SegFormer: Simple and efficient design for semantic segmentation with transformers},
  author={Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M. and Luo, Ping},
  booktitle={Advances in Neural Information Processing Systems},
  volume={34},
  pages={12077--12090},
  year={2021}
}

@article{fan2020ma,
  title={MA-Net: Multi-scale Attention Network for liver and tumor segmentation},
  author={Fan, Ting and Wang, Xu and Cheng, Mingxi and Tao, Dacheng},
  journal={IEEE Access},
  volume={8},
  pages={179683--179691},
  year={2020},
  doi={10.1109/ACCESS.2020.3025372}
}

@inproceedings{chen2018encoder,
  title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
  author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
  booktitle={ECCV},
  year={2018}
}

@misc{multimae,
      title={MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks}, 
      author={Jose Sosa and Danila Rukhovich and Anis Kacem and Djamila Aouada},
      year={2025},
      eprint={2505.14951},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.14951}, 
}

@article{opentopo,
  title={Continental Europe Digital Terrain Model at 30 m resolution based on GEDI, ICESat-2, AW3D, GLO-30, EUDEM, MERIT DEM and background layers},
  author={Hengl, Tomislav and Leal Parente, Leandro and Krizan, Josip and Bonannella, Carmelo},
  journal={Version Dataset v3. 0. Zenodo},
  year={2020}
}