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.
@article{arxiv.2305.08413,
title = {Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward},
author = {Devis Tuia and Konrad Schindler and Begüm Demir and Xiao Xiang Zhu and Mrinalini Kochupillai and Sašo Džeroski and Jan N. van Rijn and Holger H. Hoos and Fabio Del Frate and Mihai Datcu and Volker Markl and Bertrand Le Saux and Rochelle Schneider and Gustau Camps-Valls},
journal= {arXiv preprint arXiv:2305.08413},
year = {2024}
}