Remote Sensing and Deep Learning for Environmental Policy Support: From Theory to Practice

Data-driven environmental governance is gaining importance for tackling the current biodiversity and climate crises. Remote sensing can provide an efficient alternative to expensive and time-intensive in-situ monitoring. Deep learning is the current state-of-the art for knowledge extraction from remote sensing data. However, its practical, operational application for policy support remains limited. In this paper, we coupled the producer and user perspective to unravel the reasons behind this. We identified three main keys to success for an operational implementation of models on the interface between remote sensing and deep learning (technology) and environmental governance (policy): (i) a truly operational mindset, (ii) the use of generic model (components) and (iii) the availability of reference data. We argue that the road to success is paved with effective communication, a well-substantiated (prototype) use case selection and an optimal use of the scarce resource that is (the collection of) labeled reference data.

ISBN:978-1-6654-0369-6

Publication year:2021