Computational Challenges for Artificial Intelligence and Machine Learning in Environmental Research

Abstract

In the last decades, environmental research has started to adopt a data-driven perspective enabled by huge sensor networks, satellite-based Earth observation, and almost ubiquitous Internet access. Some of these data-driven approaches are expected to make visions of a sustainable future come true. For example, by enabling societies to live in sustainable smart cities, or to feed the world with precision agriculture. Or by fighting environmental pollution or global deforestation with increased observational power. However, there is a serious gap between some of the current expectations put into data-driven techniques and the maturity of the field of spatial machine learning and artificial intelligence or computer science in general. We give a few examples of open research issues that computer science has to solve in order to make data-driven approaches to environmental sciences successful.