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Challenges in data-driven geospatial modeling for environmental research and practice

Author

Listed:
  • Diana Koldasbayeva

    (Skolkovo Institute of Science and Technology)

  • Polina Tregubova

    (Skolkovo Institute of Science and Technology)

  • Mikhail Gasanov

    (Skolkovo Institute of Science and Technology)

  • Alexey Zaytsev

    (Skolkovo Institute of Science and Technology
    Yanqi Lake Beijing Institute of Mathematical Sciences and Applications (BIMSA))

  • Anna Petrovskaia

    (Skolkovo Institute of Science and Technology)

  • Evgeny Burnaev

    (Skolkovo Institute of Science and Technology
    Autonomous Non-Profit Organization Artificial Intelligence Research Institute (AIRI))

Abstract

Machine learning-based geospatial applications offer unique opportunities for environmental monitoring due to domains and scales adaptability and computational efficiency. However, the specificity of environmental data introduces biases in straightforward implementations. We identify a streamlined pipeline to enhance model accuracy, addressing issues like imbalanced data, spatial autocorrelation, prediction errors, and the nuances of model generalization and uncertainty estimation. We examine tools and techniques for overcoming these obstacles and provide insights into future geospatial AI developments. A big picture of the field is completed from advances in data processing in general, including the demands of industry-related solutions relevant to outcomes of applied sciences.

Suggested Citation

  • Diana Koldasbayeva & Polina Tregubova & Mikhail Gasanov & Alexey Zaytsev & Anna Petrovskaia & Evgeny Burnaev, 2024. "Challenges in data-driven geospatial modeling for environmental research and practice," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-55240-8
    DOI: 10.1038/s41467-024-55240-8
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