"Deep learning for the Earth Sciences -- A comprehensive approach to remote sensing, climate science and geosciences"
Editors: Gustau Camps-Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein
Publisher: Wiley & Sons, inc., 2021

@Book{CampsValls21wiley,
Title = {Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences},
Author = {Camps-Valls, G. and Tuia, D. and Zhu, X.X. and Reichstein, M.},
Publisher = {Wiley & Sons},
isbn = {978-1-119-64614-3},
Year = {2021},
}

https://books.google.es/books?id=5P5DzgEACAAJ&dq=%22Deep+learning+for+the+Earth+Sciences%22&hl=en&sa=X&ved=2ahUKEwimm8GygYXwAhW4BGMBHWpJDf8Q6AEwAHoECAIQAg

https://www.amazon.com/Deep-learning-Earth-Sciences-comprehensive/dp/1119646146/

by Gustau Camps-Valls, Xiao Xiang Zhu, Devis Tuia, and Markus Reichstein

Some review papers of interest:

• Remote sensing
  • X. X. Zhu et al., "Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources," in IEEE Geoscience and Remote Sensing Magazine, vol. 5, no. 4, pp. 8-36, Dec. 2017, doi: 10.1109/MGRS.2017.2762307. https://ieeexplore.ieee.org/abstract/document/8113128
  • L. Zhang, L. Zhang and B. Du, "Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art," in IEEE Geoscience and Remote Sensing Magazine, vol. 4, no. 2, pp. 22-40, June 2016, doi: 10.1109/MGRS.2016.2540798. https://ieeexplore.ieee.org/abstract/document/7486259
  • Ma, Lei, et al. "Deep learning in remote sensing applications: A meta-analysis and review." ISPRS journal of photogrammetry and remote sensing 152 (2019): 166-177. https://www.sciencedirect.com/science/article/pii/S0924271619301108
  • Yuan, Qiangqiang, et al. "Deep learning in environmental remote sensing: Achievements and challenges." Remote Sensing of Environment 241 (2020): 111716. https://www.sciencedirect.com/science/article/pii/S0034425720300857
• Climate science
  • Rasp, Stephan, Michael S. Pritchard, and Pierre Gentine. "Deep learning to represent subgrid processes in climate models." Proceedings of the National Academy of Sciences 115.39 (2018): 9684-9689. https://www.pnas.org/content/115/39/9684.short
  • Jones, Nicola. "How machine learning could help to improve climate forecasts." Nature News 548.7668 (2017): 379. https://www.nature.com/articles/548379a
  • Dueben, Peter D., and Peter Bauer. "Challenges and design choices for global weather and climate models based on machine learning." Geoscientific Model Development 11.10 (2018): 3999-4009. https://gmd.copernicus.org/articles/11/3999/2018/
• Geosciences
  • Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., & Carvalhais, N. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204. https://www.nature.com/articles/s41586-019-0912-1.
  • Bergen, Karianne J., et al. "Machine learning for data-driven discovery in solid Earth geoscience." Science 363.6433 (2019). https://science.sciencemag.org/content/363/6433/eaau0323.abstract

by Jose E. Adsuara, Manuel Campos-Taberner, Javier García-Haro, Carlo Gatta, Adriana Romero, and Gustau Camps-Valls

• EPLS algorithm in https://sites.google.com/site/adriromsor/epls

Gonzalo Mateo-García, Valero Laparra, Christian Requena-Mesa, and Luis Gómez-Chova

• GANs zoo: https://github.com/hindupuravinash/the-gan-zoo

by Ribana Roscher

by Devis Tuia, Diego Marcos, Konrad Schindler, and Bertrand Le Saux

• http://www2.isprs.org/commissions/comm3/wg4/semantic-labeling.html
• http://www.grss-ieee.org/community/technical-committees/data-fusion/2015-ieee-grss-data-fusion-contest/
• https://sites.google.com/site/michelevolpiresearch/data/zurich-dataset
• https://project.inria.fr/aerialimagelabeling/
• https://spacenetchallenge.github.io
• http://deepglobe.org/
• https://spacenet.ai/sn6-challenge/
• https://github.com/cloudtostreet/Sen1Floods11
• http://www.grss-ieee.org/community/technical-committees/data-fusion/2018-ieee-grss-data-fusion-contest/
• https://udayton.edu/engineering/research/centers/vision_lab/research/was_data_analysis_and_processing/dale.php
• http://www.grss-ieee.org/community/technical-committees/data-fusion/2017-ieee-grss-data-fusion-contest-2/
• https://mediatum.ub.tum.de/1454690
• http://dx.doi.org/10.21227/b9pt-8x03
• https://github.com/SorourMo/38-Cloud-A-Cloud-Segmentation-Dataset
• https://rcdaudt.github.io/oscd/
• https://github.com/czarmanu/lake-ice-ml

by Jian Ding, Jinwang Wang, Wen Yang, and Gui-Song Xia

• https://captain-whu.github.io/DOTA/
• http://aiskyeye.com/
• http://xviewdataset.org/
• https://github.com/dingjiansw101/RoITransformer_DOTA
• https://github.com/dingjiansw101/AerialDetection

by Benjamin Kellenberger, Onur Tasar, Bharath Bhushan Damodaran, Nicolas Courty, and Devis Tuia

• https://github.com/bkellenb/da-dl4eo
• https://kuzikus-namibia.de/xe_index.html

by Marco Körner and Marc Rußwurm

by Maria Vakalopoulou, Stergios Christodoulidis, Mihir Sahasrabudhe, and Nikos Paragios

• https://github.com/stergioc/smooth-transformer

by Wei He, Danfeng Hong, Giuseppe Scarpa, Tatsumi Uezato, and Naoto Yokoya

• BDSD: http://openremotesensing.net/wp-content/uploads/2015/01/pansharpeningtoolver_1_3.rar
• MTF-GLP: http://openremotesensing.net/wp-content/uploads/2015/01/pansharpeningtoolver_1_3.rar
• SIRF: http://cchen156.web.engr.illinois.edu/code/CODE_SIRF.zip
• PNN: http://www.grip.unina.it/download/prog/PNN/PNN_v0.1.zip
• DRPNN: https://github.com/Decri/DRPNN-Deep-Residual-Pan-sharpening-Neural-Network
• PanNet: https://xueyangfu.github.io/paper/2017/iccv/ICCV17_training_code.zip
• PNN+: https://github.com/sergiovitale/pansharpening-cnn
• http://naotoyokoya.com/Download.html
• https://github.com/qw245/BlindFuse
• https://github.com/alfaiate
• https://sites.google.com/site/harikanats/
• https://sites.google.com/view/renweidian/
• https://github.com/aicip/uSDN
• https://github.com/XieQi2015/MHF-net
• http://naotoyokoya.com/Download.html
• http://www1.cs.columbia.edu/CAVE/databases/

by Gencer Sumbul, Jian Kang, and Begüm Demir

• https://git.tu-berlin.de/rsim

by Mayur Mudigonda, Prabhat, Karthik Kashinath, Evan Racah, Ankur Mahesh, Yunjie Liu, Christopher Beckham, Jim Biard, Thorsten Kurth, Sookyung Kim, Samira Kahou, Tegan Maharaj, Burlen Loring, Christopher Pal, Travis O’Brien, Ken Kunkel, Michael F. Wehner, and William D. Collins

• Coded Surface Bulletin (CSB): http://www.nws.noaa.gov/noaaport/html/noaaport.shtml
• ClimateNet: https://www.nersc.gov/research-and-development/data-analytics/big-data-center/climatenet
• ClimateContoursTool: http://labelmegold.services.nersc.gov/climatecontoursgold/tool.html

by Xavier-Andoni Tibau, Christian Reimers, Christian Requena-Mesa, and Jakob Runge

• https://github.com/ClimateInformatics/SupernoVAE

by Peter D. Dueben, Peter Bauer, and Samantha Adams

• https://www.top500.org/
• https://www.ecmwf.int/en/about/what-we-do/scalability
• https://www.ecmwf.int/en/computing/our-facilities/data-handling-system

by Zhihan Gao, Xingjian Shi, Hao Wang, Dit-Yan Yeung, Wang-chun Woo, and Wai-Kin Wong

• WMO-No.1198 report: https://library.wmo.int/doc_num.php?explnum_id=3795
• HKO: https://rsmc.hko.gov.hk
• Com-SWIRLS: https://com-swirls.org

by David Malmgren-Hansen

• IASI - Atmospheric Temperature Profiles Code: https://github.com/damaha/iasi-atmosphere Data: https://data.dtu.dk/articles/dataset/IASI_dataset_v1/12999642

• ASIP - Sea Ice Concentrations from Satellite Sensor Fusion Code: https://github.com/damaha/asip-v1 Data: https://data.dtu.dk/articles/dataset/ASIP_Sea_Ice_Dataset_-_version_1/11920416

Automatic Ice charting in early operational use at the Danish Meteorological Institute: http://ocean.dmi.dk/asip/.

by Lin Liu

Here are the major data centers, repositories, and providers for cryospheric studies:

• US National Snow and Ice Data Center (https://nsidc.org)
• US National Science Foundation Arctic Data Center (https://arcticdata.io)
• US Antarctic Program Data Center (http://www.usap-dc.org})
• European Space Agency Climate Change Initiative (http://cci.esa.int)
  • Antarctic Ice Sheet (http://esa-icesheets-antarctica-cci.org)
  • Greenland Ice Sheet (http://esa-icesheets-greenland-cci.org)
  • Glaciers (http://www.esa-glaciers-cci.org)
  • Permafrost (http://cci.esa.int/Permafrost)
  • Sea ice (http://cci.esa.int/seaice)
  • Snow (http://cci.esa.int/node/274/)
• Canadian Cryospheric Information Network (https://www.ccin.ca)
• China National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/en/)

Below we list the data and codes published in the cryospheric studies reviewed in this chapter, grouped by the cryospheric components.

• Glaciers

  • Detection of glacier calving margins with convolutional neural networks \citep{mohajerani2019} Code and data: https://github.com/yaramohajerani/FrontLearning
  • Automatically delineating the calving front of Jakobshavn Isbr{\ae} from multitemporal TerraSAR-X images \citep{zhang2019} Training and test data: https://doi.org/10.1594/PANGAEA.897066
  • ALpine Parameterized Glacier Model (ALPGM) \citep{bolibar2020} Code and sample data: https://github.com/JordiBolibar/ALPGM

• Ice sheet

  • DeepBedMap: Antarctica Ice Sheet bed elevation using a super resolution deep neural network \citep{leong2020} Code: https://github.com/weiji14/deepbedmap Training experiments: https://www.comet.ml/weiji14/deepbedmap Digital bed elevation model: https://doi.org/10.17605/OSF.IO/96APW

• Snow

  • Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network \citep{Braakmann-Folgmann2019} Sample code and data: https://github.com/AnneBF/snownet

• Permafrost

  • Automatic mapping of thermokarst landforms from remote sensing images using deep learning \citep{huang2018} Code: https://github.com/yghlc/DeeplabforRS

  • Using deep learning to map retrogressive thaw slumps from CubeSat images \citep{huang2020} Code: https://github.com/yghlc/Landuse\_DL Training and test data: https://doi.pangaea.de/10.1594/PANGAEA.908909

    • High-resolution mapping of spatial heterogeneity in ice wedge polygon geomorphology near Prudhoe Bay, Alaska \citep{abolt2020} Code and data: https://doi.org/10.1594/PANGAEA.910178

• River ice

  • River ice segmentation with deep learning \citep{singh2019} Code: https://github.com/abhineet123/river\_ice\_segmentation Data: https://ieee-dataport.org/open-access/alberta-river-ice-segmentation-dataset

by Basil Kraft, Simon Besnard, and Sujan Koirala

• Code: https://github.com/bask0/dl4es_ch18
• For access to data / simulations contact the authors

by Chaopeng Shen and Kathryn Lawson

by Laure Zanna and Thomas Bolton

by Pierre Gentine, Veronika Eyring, and Tom Beucler

by Peter A. G. Watson

• https://github.com/PAGWatson/Lorenz96_and_neural_networks

by Markus Reichstein, Gustau Camps-Valls, Devis Tuia, and Xiao Xiang Zhu

• Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., & Carvalhais, N. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204. https://www.nature.com/articles/s41586-019-0912-1.
• Tuia, Devis, Ribana Roscher, Jan Dirk Wegner, Nathan Jacobs, Xiao Xiang Zhu, and Gustau Camps-Valls. "Towards a Collective Agenda on AI for Earth Science Data Analysis." arXiv preprint arXiv:2104.05107 (2021). https://arxiv.org/abs/2104.05107
• Camps-Valls, Gustau. "Perspective on Deep Learning for Earth Sciences." Generalization With Deep Learning: For Improvement On Sensing Capability (2021): 159-173. https://www.worldscientific.com/doi/abs/10.1142/9789811218842_0007