Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward
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arXiv
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| Format: | Preprint |
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2023
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| author | Tuia, Devis Schindler, Konrad Demir, Begüm Zhu, Xiao Xiang Kochupillai, Mrinalini Džeroski, Sašo van Rijn, Jan N. Hoos, Holger H. Del Frate, Fabio Datcu, Mihai Markl, Volker Saux, Bertrand Le Schneider, Rochelle Camps-Valls, Gustau |
| author_facet | Tuia, Devis Schindler, Konrad Demir, Begüm Zhu, Xiao Xiang Kochupillai, Mrinalini Džeroski, Sašo van Rijn, Jan N. Hoos, Holger H. Del Frate, Fabio Datcu, Mihai Markl, Volker Saux, Bertrand Le Schneider, Rochelle Camps-Valls, Gustau |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_08413 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward Tuia, Devis Schindler, Konrad Demir, Begüm Zhu, Xiao Xiang Kochupillai, Mrinalini Džeroski, Sašo van Rijn, Jan N. Hoos, Holger H. Del Frate, Fabio Datcu, Mihai Markl, Volker Saux, Bertrand Le Schneider, Rochelle Camps-Valls, Gustau Computer Vision and Pattern Recognition Image and Video Processing Applications 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. |
| title | Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward |
| topic | Computer Vision and Pattern Recognition Image and Video Processing Applications |
| url | https://arxiv.org/abs/2305.08413 |