Challenges in data-based geospatial modeling for environmental research
  and practice
Diana Koldasbayeva Polina Tregubova Mikhail Gasanov Alexey Zaytsev Anna Petrovskaia Evgeny Burnaev
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.