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.

电子数据和机器学习的地理空间建模已成为环境研究中广受欢迎的趋势，通过准确的地理空间预测有助于生态监测、质量评估、政策制定和行动计划等方面的有效资源管理。然而，对于进一步的研究和实践，需要解决许多问题，以获得精确可复现的结果。研究综述了地理空间建模中常见的问题和挑战，并提供了克服这些挑战的技术和流行编程工具，还讨论了地理空间人工智能在环境应用中的前景。