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

^*Huvudförfattare för detta arbete

Handelsrätt, Helsingfors

Forskningsoutput: Tidskriftsbidrag › Översiktsartikel › Peer review

50 Citeringar (Scopus)

Sammanfattning

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.

Originalspråk	Engelska
Referentgranskad vetenskaplig tidskrift	IEEE Geoscience and Remote Sensing Magazine
Sidor (från-till)	2-25
ISSN	2473-2397
DOI	https://doi.org/10.1109/MGRS.2024.3425961
Status	Publicerad - 2024
MoE-publikationstyp	A2 Översiktsartikel i en vetenskaplig tidskrift

Nyckelord

113 Data- och informationsvetenskap
117,1 Geografi

Åtkomst av dokument

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

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.",

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

author = "Devis Tuia and Konrad Schindler and Beg{\"u}m Demir and Zhu, \{Xiao Xiang\} and Mrinalini Kochupillai and Sa{\v s}o D{\v z}eroski and \{Van Rijn\}, \{Jan N.\} and Hoos, \{Holger H.\} and \{Del Frate\}, Fabio and Mihai Datcu and Volker Markl and \{Le Saux\}, Bertrand and Rochelle Schneider and Gustau Camps-Valls",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2024",

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, s. 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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