GLOBE Observer: A Case Study in Advancing Earth System Knowledge with AI-Powered Citizen Science

Citizen science and artificial intelligence (AI) complement each other by harnessing the strengths of both human and machine capabilities. Citizen science generates terabytes of raw numerical, text, and image data, the analysis of which requires automated techniques to process in an efficient manner...

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Detalles Bibliográficos
Autores: Nelson, Peder V., Azam, Farhat, Low, Russanne D., Carney, Ryan M., Kohl, Holli, Falk, Monika, Overoye, David, Garriga, Joan, Yang, Di, Schelkin, Larisa, Huang, Xiao, Boger, Rebecca, Chellappan, Sriram, Schwerin, Theresa
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/386979
Acceso en línea:http://hdl.handle.net/10261/386979
https://api.elsevier.com/content/abstract/scopus_id/85212326194
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Citizen science
Computer vision
Land cover
Mosquitoes
Smart phones
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network_name_str España
repository_id_str
dc.title.none.fl_str_mv GLOBE Observer: A Case Study in Advancing Earth System Knowledge with AI-Powered Citizen Science
title GLOBE Observer: A Case Study in Advancing Earth System Knowledge with AI-Powered Citizen Science
spellingShingle GLOBE Observer: A Case Study in Advancing Earth System Knowledge with AI-Powered Citizen Science
Nelson, Peder V.
Artificial intelligence
Citizen science
Computer vision
Land cover
Mosquitoes
Smart phones
title_short GLOBE Observer: A Case Study in Advancing Earth System Knowledge with AI-Powered Citizen Science
title_full GLOBE Observer: A Case Study in Advancing Earth System Knowledge with AI-Powered Citizen Science
title_fullStr GLOBE Observer: A Case Study in Advancing Earth System Knowledge with AI-Powered Citizen Science
title_full_unstemmed GLOBE Observer: A Case Study in Advancing Earth System Knowledge with AI-Powered Citizen Science
title_sort GLOBE Observer: A Case Study in Advancing Earth System Knowledge with AI-Powered Citizen Science
dc.creator.none.fl_str_mv Nelson, Peder V.
Azam, Farhat
Low, Russanne D.
Carney, Ryan M.
Kohl, Holli
Falk, Monika
Overoye, David
Garriga, Joan
Yang, Di
Schelkin, Larisa
Huang, Xiao
Boger, Rebecca
Chellappan, Sriram
Schwerin, Theresa
author Nelson, Peder V.
author_facet Nelson, Peder V.
Azam, Farhat
Low, Russanne D.
Carney, Ryan M.
Kohl, Holli
Falk, Monika
Overoye, David
Garriga, Joan
Yang, Di
Schelkin, Larisa
Huang, Xiao
Boger, Rebecca
Chellappan, Sriram
Schwerin, Theresa
author_role author
author2 Azam, Farhat
Low, Russanne D.
Carney, Ryan M.
Kohl, Holli
Falk, Monika
Overoye, David
Garriga, Joan
Yang, Di
Schelkin, Larisa
Huang, Xiao
Boger, Rebecca
Chellappan, Sriram
Schwerin, Theresa
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv NASA
National Science Foundation (US)
National Oceanic and Atmospheric Administration (US)
U.S. Geological Survey
European Commission
CSIC - Plataforma Temática Interdisciplinar del CSIC Salud Global (PTI Salud Global)
Dutch Research Agenda
Fundación la Caixa
Nelson, Peder V. [0000-0003-3979-9051]
Garriga, Joan [0000-0002-4561-7835]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Artificial intelligence
Citizen science
Computer vision
Land cover
Mosquitoes
Smart phones
topic Artificial intelligence
Citizen science
Computer vision
Land cover
Mosquitoes
Smart phones
description Citizen science and artificial intelligence (AI) complement each other by harnessing the strengths of both human and machine capabilities. Citizen science generates terabytes of raw numerical, text, and image data, the analysis of which requires automated techniques to process in an efficient manner. Conversely, AI computer vision technology can require tens of thousands of images during the training process, and citizen science projects are well suited to provide large libraries of data. Herein, we describe how AI tools are being applied across the GLOBE Observer citizen science data ecosystem, where image recognition algorithms are supporting data ingest processes, protecting user privacy and improving data fidelity. GLOBE citizen science data has been used to develop automated data classification routines that enable information discovery of mosquito larvae and land cover labels. These advances position GLOBE citizen scientist data for discovery and use in environmental and health research, as well as by machine learning scientists working in the general field of GeoAI.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/386979
https://api.elsevier.com/content/abstract/scopus_id/85212326194
url http://hdl.handle.net/10261/386979
https://api.elsevier.com/content/abstract/scopus_id/85212326194
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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#PLACEHOLDER_PARENT_METADATA_VALUE#
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info:eu-repo/grantAgreement/ERC//CA17108
info:eu-repo/grantAgreement/ERC//874735
info:eu-repo/grantAgreement/ERC//853271
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.5334/cstp.747
https://doi.org/10.5334/cstp.747
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Ubiquity Press
publisher.none.fl_str_mv Ubiquity Press
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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spelling GLOBE Observer: A Case Study in Advancing Earth System Knowledge with AI-Powered Citizen ScienceNelson, Peder V.Azam, FarhatLow, Russanne D.Carney, Ryan M.Kohl, HolliFalk, MonikaOveroye, DavidGarriga, JoanYang, DiSchelkin, LarisaHuang, XiaoBoger, RebeccaChellappan, SriramSchwerin, TheresaArtificial intelligenceCitizen scienceComputer visionLand coverMosquitoesSmart phonesCitizen science and artificial intelligence (AI) complement each other by harnessing the strengths of both human and machine capabilities. Citizen science generates terabytes of raw numerical, text, and image data, the analysis of which requires automated techniques to process in an efficient manner. Conversely, AI computer vision technology can require tens of thousands of images during the training process, and citizen science projects are well suited to provide large libraries of data. Herein, we describe how AI tools are being applied across the GLOBE Observer citizen science data ecosystem, where image recognition algorithms are supporting data ingest processes, protecting user privacy and improving data fidelity. GLOBE citizen science data has been used to develop automated data classification routines that enable information discovery of mosquito larvae and land cover labels. These advances position GLOBE citizen scientist data for discovery and use in environmental and health research, as well as by machine learning scientists working in the general field of GeoAI.The GLOBE Program is sponsored by the National Aeronautics and Space Administration, National Science Foundation, National Oceanic and Atmospheric Administration, and U.S. Department of State and managed by NASA. GLOBE Observer is supported by NASA Science Activation Award NNX16AE28A for the NASA Earth Science Education Collaborative (Theresa Schwerin, IGES, PI). Additional support for Peder Nelson is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G23AP00683 (GY23-GY27). The Global Mosquito Observation Dashboard was funded by the National Science Foundation under Grant No. IIS-2014547 to PI Ryan Carney (USF) and Co-PIs Sriram Chellappan (USF), and Russanne Low (IGES). Research by Xiao Huang and Di Yang was funded through NASA EPSCoR Grant #80NSSC21M0177. Initial AI classification models for GLOBE citizen science mosquito larvae was funded by NSF EAGER#1645154 to PI Rebecca Boger (Brooklyn College), Co-PIs Russanne Low (IGES) and Geoffrey Haines-Styles. Mosquito Alert is funded by (a) the European Commission, under Grants CA17108 (AIM-COST Action), 874735 (VEO), 853271 (H-MIP), and 2020/2094 (NextGenerationEU, through CSIC’s Global Health Platform, PTI Salud Global); (b) the Dutch National Research Agenda (NWA), under Grant NWA/00686468; and (c) “la Caixa” Foundation, under Grant HR19-00336.Peer reviewedUbiquity PressNASANational Science Foundation (US)National Oceanic and Atmospheric Administration (US)U.S. Geological SurveyEuropean CommissionCSIC - Plataforma Temática Interdisciplinar del CSIC Salud Global (PTI Salud Global)Dutch Research AgendaFundación la CaixaNelson, Peder V. [0000-0003-3979-9051]Garriga, Joan [0000-0002-4561-7835]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/386979https://api.elsevier.com/content/abstract/scopus_id/85212326194reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/ERC//CA17108info:eu-repo/grantAgreement/ERC//874735info:eu-repo/grantAgreement/ERC//853271The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.5334/cstp.747https://doi.org/10.5334/cstp.747Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3869792026-05-22T06:33:51Z
score 15,812455

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