mlsanityData to 14 Oct 2026

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

arXiv 2311.11057 ↗First posted 18 Nov 2023cs.LGcited by 1

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Abstract (arXiv)

With the rise of electronic data, particularly Earth observation data, data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research. Accurate geospatial predictions are vital for domain research based on ecosystem monitoring and quality assessment and for policy-making and action planning, considering effective management of natural resources. The accuracy and computation speed of ML has generally proved efficient. However, many questions have yet to be addressed to obtain precise and reproducible results suitable for further use in both research and practice. A better understanding of the ML concepts applicable to geospatial problems enhances the development of data science tools providing transparent information crucial for making decisions on global challenges such as biosphere degradation and climate change. This survey reviews common nuances in geospatial modelling, such as imbalanced data, spatial autocorrelation, prediction errors, model generalisation, domain specificity, and uncertainty estimation. We provide an overview of techniques and popular programming tools to overcome or account for the challenges. We also discuss prospects for geospatial Artificial Intelligence in environmental applications.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 237 references that are in the map. First posted on arXiv 18 Nov 2023.

Builds on

18 of 237 references.

References · 18

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6 more
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