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Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward

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arxiv 2305.08413 v2 pith:R266CKKT submitted 2023-05-15 cs.CV eess.IVstat.AP

classification cs.CVeess.IVstat.AP
keywords approachesearthmodelsobservationadvanceadvancedarticleartificial
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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