Artificial intelligence to advance Earth observation: a perspective
Devis Tuia Konrad Schindler Begüm Demir Gustau Camps-Valls Xiao Xiang Zhu Mrinalini Kochupillai Sašo Džeroski Jan N. van Rijn Holger H. Hoos Fabio Del Frate Mihai Datcu Jorge-Arnulfo Quiané-Ruiz Volker Markl Bertrand Le Saux Rochelle Schneider
Earth observation (EO) is a prime instrument for monitoring land and ocean
processes, studying the dynamics at work, and taking the pulse of our planet.
This article gives a bird's eye view of the essential scientific tools and
approaches informing and supporting the transition from raw EO data to usable
EO-based information. The promises, as well as the current challenges of these
developments, are highlighted under dedicated sections. Specifically, we cover
the impact of (i) Computer vision; (ii) Machine learning; (iii) Advanced
processing and computing; (iv) Knowledge-based AI; (v) Explainable AI and
causal inference; (vi) Physics-aware models; (vii) User-centric approaches; and
(viii) the much-needed discussion of ethical and societal issues related to the
massive use of ML technologies in EO.