Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward

Devis Tuia; Konrad Schindler; Begüm Demir; Xiao Xiang Zhu; Mrinalini Kochupillai; Sašo Džeroski; Jan N. Van Rijn; Holger H. Hoos; Fabio Del Frate; Mihai Datcu; Volker Markl; Bertrand Le Saux; Rochelle Schneider; Gustau Camps-Valls

doi:10.1109/MGRS.2024.3425961

Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward

Devis Tuia^*
, Konrad Schindler
, Begüm Demir
, Xiao Xiang Zhu
, Mrinalini Kochupillai
, Sašo Džeroski
, Jan N. Van Rijn
, Holger H. Hoos
, Fabio Del Frate
, Mihai Datcu
, Volker Markl
, Bertrand Le Saux
, Rochelle Schneider
, Gustau Camps-Valls

^*Corresponding author for this work

Commercial Law, Helsinki

Research output: Contribution to journal › Review Article › peer-review

35 Citations (Scopus)

Abstract

Earth observation (EO) is increasingly used for mapping and monitoring processes occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a global view, consistent in time, of the state of our forests, oceans, and growing urban areas. However, such a wealth of data has little value without appropriate processing chains able to convert the pixel values to information useful for decision makers.

Original language	English
Peer-reviewed scientific journal	IEEE Geoscience and Remote Sensing Magazine
Pages (from-to)	2-25
ISSN	2473-2397
DOIs	https://doi.org/10.1109/MGRS.2024.3425961
Publication status	Published - 2024
MoE publication type	A2 Review article in a scientific journal

Keywords

113 Computer and information sciences
117,1 Geosciences
adaptation models
artificial intelligence
earth
data models
computational modeling
monitoring
data mining

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10.1109/MGRS.2024.3425961

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Tuia, D., Schindler, K., Demir, B., Zhu, X. X., Kochupillai, M., Džeroski, S., Van Rijn, J. N., Hoos, H. H., Del Frate, F., Datcu, M., Markl, V., Le Saux, B., Schneider, R., & Camps-Valls, G. (2024). Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward. IEEE Geoscience and Remote Sensing Magazine, 2-25. https://doi.org/10.1109/MGRS.2024.3425961

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abstract = "Earth observation (EO) is increasingly used for mapping and monitoring processes occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a global view, consistent in time, of the state of our forests, oceans, and growing urban areas. However, such a wealth of data has little value without appropriate processing chains able to convert the pixel values to information useful for decision makers.",

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doi = "10.1109/MGRS.2024.3425961",

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Tuia, D, Schindler, K, Demir, B, Zhu, XX, Kochupillai, M, Džeroski, S, Van Rijn, JN, Hoos, HH, Del Frate, F, Datcu, M, Markl, V, Le Saux, B, Schneider, R & Camps-Valls, G 2024, 'Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward', IEEE Geoscience and Remote Sensing Magazine, pp. 2-25. https://doi.org/10.1109/MGRS.2024.3425961

TY - JOUR

T1 - Artificial Intelligence to Advance Earth Observation

T2 - A review of models, recent trends, and pathways forward

AU - Tuia, Devis

AU - Schindler, Konrad

AU - Demir, Begüm

AU - Zhu, Xiao Xiang

AU - Kochupillai, Mrinalini

AU - Džeroski, Sašo

AU - Van Rijn, Jan N.

AU - Hoos, Holger H.

AU - Del Frate, Fabio

AU - Datcu, Mihai

AU - Markl, Volker

AU - Le Saux, Bertrand

AU - Schneider, Rochelle

AU - Camps-Valls, Gustau

PY - 2024

Y1 - 2024

N2 - Earth observation (EO) is increasingly used for mapping and monitoring processes occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a global view, consistent in time, of the state of our forests, oceans, and growing urban areas. However, such a wealth of data has little value without appropriate processing chains able to convert the pixel values to information useful for decision makers.

AB - Earth observation (EO) is increasingly used for mapping and monitoring processes occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a global view, consistent in time, of the state of our forests, oceans, and growing urban areas. However, such a wealth of data has little value without appropriate processing chains able to convert the pixel values to information useful for decision makers.

KW - 113 Computer and information sciences

KW - 117,1 Geosciences

KW - adaptation models

KW - artificial intelligence

KW - earth

KW - data models

KW - computational modeling

KW - monitoring

KW - data mining

UR - https://www.scopus.com/pages/publications/85218939273

U2 - 10.1109/MGRS.2024.3425961

DO - 10.1109/MGRS.2024.3425961

M3 - Review Article

AN - SCOPUS:85218939273

SN - 2473-2397

SP - 2

EP - 25

JO - IEEE Geoscience and Remote Sensing Magazine

JF - IEEE Geoscience and Remote Sensing Magazine

ER -

Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward

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Keywords

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