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GeoAI as Collaborative Effort

Interview with Devis Tuia

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KI - Künstliche Intelligenz Aims and scope Submit manuscript

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Notes

  1. https://landsat.gsfc.nasa.gov.

  2. https://www.geo.uzh.ch/en/units/gco.html.

  3. https://www.epfl.ch/schools/enac/research/environmental-engineering-institute-iie/alpole-en/.

  4. https://trsc.org/en/.

  5. https://eceo.epfl.ch.

  6. https://www.dair-institute.org/about.

  7. https://ebird.org/home.

  8. https://www.belongielab.org.

  9. Example attributed to Ross Purves.

References

  1. Tollenaar V, Zekollari H, Tuia D, Kellenberger B, Rußwurm M, Lhermitte S, et al (2022) What determines the location of Antarctic blue ice areas? A deep learning approach. In: EGU General Assembly 2022. EGU22-1294. Vienna, Austria; Available from: https://doi.org/10.5194/egusphere-egu22-1294.

  2. Persello C, Wegner JD, Hänsch R, Tuia D, Ghamisi P, Koeva M et al (2022) Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities. IEEE Geosci Remote Sens Mag 10(2):172–200. https://doi.org/10.1109/MGRS.2021.3136100

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  3. Tuia D, Kellenberger B, Beery S, Costelloe BR, Zuffi S, Risse B et al (2022) Perspectives in machine learning for wildlife conservation. Nat Commun 13(1):792. https://doi.org/10.1038/s41467-022-27980-y

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Authors and Affiliations

  1. Department of Computing Science, Umeå University, 901 87, Umeå, Sweden

    Kai-Florian Richter

  2. Department of Human Geography and Spatial Planning, University Utrecht, Princetonlaan 8a, Utrecht, 3584 CB, Netherlands

    Simon Scheider

  3. Environmental Computational Science and Earth Observation laboratory, Ecole Polytechnique Fédérale de Lausanne, EPFL Valais Wallis, Rue de l’Industrie 17, Case postale 440, 1951, Sion, Switzerland

    Devis Tuia

Authors
  1. Kai-Florian Richter
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  2. Simon Scheider
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  3. Devis Tuia
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Correspondence to Kai-Florian Richter.

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Richter, KF., Scheider, S. & Tuia, D. GeoAI as Collaborative Effort. Künstl Intell 37, 99–105 (2023). https://doi.org/10.1007/s13218-022-00791-5

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