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We introduce a highly multimodal transformer to represent many remote sensing modalities - multispectral optical, synthetic aperture radar, elevation, weather, pseudo-labels, and more - across space and time.
Red and photographic infrared linear combinations for monitoring vegetation
Tucker, C. J · 1979
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Landscan: a global population database for estimating populations at risk
Dobson, J. E., Bright, E. A., Coleman, P. R., Durfee, R. C., and Worley, B. A · 2000
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NASA shuttle radar topography mission global 1 arc second
NASA JPL · 2000
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Learning a similarity metric discriminatively, with application to face verification
Chopra, S., Hadsell, R., and LeCun, Y · 2005
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Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Scikit-learn: Machine learning in python
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Efficient estimation of word representations in vector space, 2013
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
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A review of applications of satellite earth observation data for global societal benefit and stewardship of planet earth
Kansakar, P. and Hossain, F · 2016
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Viirs night-time lights
Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., and Ghosh, T · 2017
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Google earth engine: Planetary-scale geospatial analysis for everyone
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R · 2017
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The kinetics human action video dataset
Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al · 2017
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Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I · 2018
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Helber, P., Bischke, B., Dengel, A., and Borth, D · 2019
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Augment your batch: better training with larger batches
Hoffer, E., Ben-Nun, T., Hubara, I., Giladi, N., Hoefler, T., and Soudry, D · 2019
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Tile2vec: Unsupervised representation learning for spatially distributed data
Jean, N., Wang, S., Samar, A., Azzari, G., Lobell, D., and Ermon, S · 2019
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Breizhcrops: A satellite time series dataset for crop type identification
Rußwurm, M., Lefèvre, S., and Körner, M · 2019
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Bigearthnet: A large-scale benchmark archive for remote sensing image understanding
Sumbul, G., Charfuelan, M., Demir, B., and Markl, V · 2019
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Machine learning for glacier monitoring in the hindu kush himalaya
Baraka, S., Akera, B., Aryal, B., Sherpa, T., Shresta, F., Ortiz, A., Sankaran, K., Ferres, J. L., Matin, M., and Bengio, Y · 2020
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Sen1floods11: A georeferenced dataset to train and test deep learning flood algorithms for sentinel-1
Bonafilia, D., Tellman, B., Anderson, T., and Issenberg, E · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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The era5 global reanalysis
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., et al · 2020
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Rapid response crop maps in data sparse regions
Kerner, H., Tseng, G., Becker-Reshef, I., Nakalembe, C., Barker, B., Munshell, B., Paliyam, M., and Hosseini, M · 2020
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Contrastive representation learning: A framework and review
Le-Khac, P. H., Healy, G., and Smeaton, A. F · 2020
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Sar-enhanced mapping of live fuel moisture content
Rao, K., Williams, A. P., Flefil, J. F., and Konings, A. G · 2020
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So2sat lcz42: A benchmark data set for the classification of global local climate zones [software and data sets]
Zhu, X. X., Hu, J., Qiu, C., Shi, Y., Kang, J., Mou, L., Bagheri, H., Haberle, M., Hua, Y., Huang, R., et al · 2020
Cited alongside, same era.
An empirical study of training self-supervised vision transformers
Satellite monitoring of terrestrial plastic waste
Kruse, C., Boyda, E., Chen, S., Karra, K., Bou-Nahra, T., Hammer, D., Mathis, J., Maddalene, T., Jambeck, J., and Laurier, F · 2023
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Considerations for ai-eo for agriculture in sub-saharan africa
Nakalembe, C. and Kerner, H · 2023
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Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning
Reed, C. J., Gupta, R., Li, S., Brockman, S., Funk, C., Clipp, B., Keutzer, K., Candido, S., Uyttendaele, M., and Darrell, T · 2023
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Lightweight, pre-trained transformers for remote sensing timeseries
Tseng, G., Cartuyvels, R., Zvonkov, I., Purohit, M., Rolnick, D., and Kerner, H · 2023
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Artificial intelligence to advance earth observation: a perspective
Tuia, D., Schindler, K., Demir, B., Camps-Valls, G., Zhu, X. X., Kochupillai, M., Džeroski, S., van Rijn, J. N., Hoos, H. H., Del Frate, F., et al · 2023
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Chen, X., Xie, S., and He, K · 2021
Cited alongside, same era.
Panoptic segmentation of satellite image time series with convolutional temporal attention networks
Garnot, V. S. F. and Landrieu, L · 2021
Cited alongside, same era.
Becoming good at ai for good
Kshirsagar, M., Robinson, C., Yang, S., Gholami, S., Klyuzhin, I., Mukherjee, S., Nasir, M., Ortiz, A., Oviedo, F., Tanner, D., et al · 2021
Cited alongside, same era.
Scalable deep learning to identify brick kilns and aid regulatory capacity
Lee, J., Brooks, N. R., Tajwar, F., Burke, M., Ermon, S., Lobell, D. B., Biswas, D., and Luby, S. P · 2021
Cited alongside, same era.
Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data
Manas, O., Lacoste, A., Giró-i Nieto, X., Vazquez, D., and Rodriguez, P · 2021
Cited alongside, same era.
Soilgrids 2.0: producing soil information for the globe with quantified spatial uncertainty
Poggio, L., De Sousa, L. M., Batjes, N. H., Heuvelink, G. B., Kempen, B., Ribeiro, E., and Rossiter, D · 2021
Cited alongside, same era.
Cropharvest: A global dataset for crop-type classification
Tseng, G., Zvonkov, I., Nakalembe, C. L., and Kerner, H · 2021
Cited alongside, same era.
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Worldcereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping
Van Tricht, K., Degerickx, J., Gilliams, S., Zanaga, D., Battude, M., Grosu, A., Brombacher, J., Lesiv, M., Bayas, J. C. L., Karanam, S., et al · 2023
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SSL4EO-S12: A large-scale multimodal, multitemporal dataset for self-supervised learning in Earth observation
Wang, Y., Braham, N. A. A., Xiong, Z., Liu, C., Albrecht, C. M., and Zhu, X. X · 2023
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Mapping smallholder cashew plantations to inform sustainable tree crop expansion in benin
Yin, L., Ghosh, R., Lin, C., Hale, D., Weigl, C., Obarowski, J., Zhou, J., Till, J., Jia, X., You, N., et al · 2023
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AnySat: An Earth observation model for any resolutions, scales, and modalities
Astruc, G., Gonthier, N., Mallet, C., and Landrieu, L · 2024
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Revisiting pre-trained remote sensing model benchmarks: Resizing and normalization matters
Corley, I., Robinson, C., Dodhia, R., Ferres, J. M. L., and Najafirad, P · 2024
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Rapid inundation mapping using the us national water model, satellite observations, and a convolutional neural network
Frame, J. M., Nair, T., Sunkara, V., Popien, P., Chakrabarti, S., Anderson, T., Leach, N. R., Doyle, C., Thomas, M., and Tellman, B · 2024
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CROMA: Remote sensing representations with contrastive radar-optical masked autoencoders
Fuller, A., Millard, K., and Green, J · 2024
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Learning and leveraging world models in visual representation learning
Garrido, Q., Assran, M., Ballas, N., Bardes, A., Najman, L., and LeCun, Y · 2024
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Assessing and addressing the global state of food production data scarcity
Kebede, E. A., Abou Ali, H., Clavelle, T., Froehlich, H. E., Gephart, J. A., Hartman, S., Herrero, M., Kerner, H., Mehta, P., Nakalembe, C., et al · 2024
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Detecting marine pollutants and sea surface features with deep learning in sentinel-2 imagery
Kikaki, K., Kakogeorgiou, I., Hoteit, I., and Karantzalos, K · 2024
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Geo-bench: Toward foundation models for earth monitoring
Lacoste, A., Lehmann, N., Rodriguez, P., Sherwin, E., Kerner, H., Lütjens, B., Irvin, J., Dao, D., Alemohammad, H., Drouin, A., et al · 2024
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Mmearth: Exploring multi-modal pretext tasks for geospatial representation learning, 2024
Nedungadi, V., Kariryaa, A., Oehmcke, S., Belongie, S., Igel, C., and Lang, N · 2024
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Rethinking transformers pre-training for multi-spectral satellite imagery
Noman, M., Naseer, M., Cholakkal, H., Anwer, R. M., Khan, S., and Khan, F. S · 2024
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Prithvi-eo-2.0: A versatile multi-temporal foundation model for earth observation applications
Szwarcman, D., Roy, S., Fraccaro, P., Gíslason, T. E., Blumenstiel, B., Ghosal, R., de Oliveira, P. H., Almeida, J. L. d. S., Sedona, R., Kang, Y., et al · 2024
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Satbird: a dataset for bird species distribution modeling using remote sensing and citizen science data
Teng, M., Elmustafa, A., Akera, B., Bengio, Y., Radi, H., Larochelle, H., and Rolnick, D · 2024
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H3: A hexagonal hierarchical geospatial indexing system
Uber · 2024
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Towards latent masked image modeling for self-supervised visual representation learning
Wei, Y., Gupta, A., and Morgado, P · 2024
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Neural plasticity-inspired foundation model for observing the earth crossing modalities
Xiong, Z., Wang, Y., Zhang, F., Stewart, A. J., Hanna, J., Borth, D., Papoutsis, I., Saux, B. L., Camps-Valls, G., and Zhu, X. X · 2024
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Introducing ai2’s beaker
Guerquin, M · 2025
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