New directions in mapping the Earth’s surface with citizen science and generative AI
Summary: As more satellite imagery has become openly available, efforts in mapping the Earth’s surface have accelerated. Yet the accuracy of these maps is still limited by the lack of in situ data needed to train machine learning algorithms. Citizen science has proven to be a valuable approach for c...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225001798 |
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| author | Linda See Qingqing Chen Andrew Crooks Juan Carlos Laso Bayas Dilek Fraisl Steffen Fritz Ivelina Georgieva Gerid Hager Martin Hofer Myroslava Lesiv Žiga Malek Milutin Milenković Inian Moorthy Fernando Orduña-Cabrera Katya Pérez-Guzmán Dmitry Schepaschenko Maria Shchepashchenko Jan Steinhauser Ian McCallum |
| author_facet | Linda See Qingqing Chen Andrew Crooks Juan Carlos Laso Bayas Dilek Fraisl Steffen Fritz Ivelina Georgieva Gerid Hager Martin Hofer Myroslava Lesiv Žiga Malek Milutin Milenković Inian Moorthy Fernando Orduña-Cabrera Katya Pérez-Guzmán Dmitry Schepaschenko Maria Shchepashchenko Jan Steinhauser Ian McCallum |
| author_sort | Linda See |
| collection | DOAJ |
| description | Summary: As more satellite imagery has become openly available, efforts in mapping the Earth’s surface have accelerated. Yet the accuracy of these maps is still limited by the lack of in situ data needed to train machine learning algorithms. Citizen science has proven to be a valuable approach for collecting in situ data through applications like Geo-Wiki and Picture Pile, but better approaches for optimizing volunteer time are still required. Although machine learning is being used in some citizen science projects, advances in generative artificial intelligence (AI) are yet to be fully exploited. This paper discusses how generative AI could be harnessed for land cover/land use mapping by enhancing citizen science approaches with multi-modal large language models (MLLMs), including improvements to the spatial awareness of AI. |
| format | Article |
| id | doaj-art-a2f953edea7a4f2bb3390c5191d85088 |
| institution | OA Journals |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-a2f953edea7a4f2bb3390c5191d850882025-08-20T02:14:34ZengElsevieriScience2589-00422025-03-0128311191910.1016/j.isci.2025.111919New directions in mapping the Earth’s surface with citizen science and generative AILinda See0Qingqing Chen1Andrew Crooks2Juan Carlos Laso Bayas3Dilek Fraisl4Steffen Fritz5Ivelina Georgieva6Gerid Hager7Martin Hofer8Myroslava Lesiv9Žiga Malek10Milutin Milenković11Inian Moorthy12Fernando Orduña-Cabrera13Katya Pérez-Guzmán14Dmitry Schepaschenko15Maria Shchepashchenko16Jan Steinhauser17Ian McCallum18Novel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, Austria; Corresponding authorDepartment of Geography, University at Buffalo, The State University of New York, Buffalo, NY 14261, USADepartment of Geography, University at Buffalo, The State University of New York, Buffalo, NY 14261, USANovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, Austria; Department of Landscape Architecture, Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000 Ljubljana, SloveniaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaNovel Data Ecosystems for Sustainability (NODES) Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Lower Austria 2361, AustriaSummary: As more satellite imagery has become openly available, efforts in mapping the Earth’s surface have accelerated. Yet the accuracy of these maps is still limited by the lack of in situ data needed to train machine learning algorithms. Citizen science has proven to be a valuable approach for collecting in situ data through applications like Geo-Wiki and Picture Pile, but better approaches for optimizing volunteer time are still required. Although machine learning is being used in some citizen science projects, advances in generative artificial intelligence (AI) are yet to be fully exploited. This paper discusses how generative AI could be harnessed for land cover/land use mapping by enhancing citizen science approaches with multi-modal large language models (MLLMs), including improvements to the spatial awareness of AI.http://www.sciencedirect.com/science/article/pii/S2589004225001798Earth sciencesEnvironmental scienceRemote sensingCartography |
| spellingShingle | Linda See Qingqing Chen Andrew Crooks Juan Carlos Laso Bayas Dilek Fraisl Steffen Fritz Ivelina Georgieva Gerid Hager Martin Hofer Myroslava Lesiv Žiga Malek Milutin Milenković Inian Moorthy Fernando Orduña-Cabrera Katya Pérez-Guzmán Dmitry Schepaschenko Maria Shchepashchenko Jan Steinhauser Ian McCallum New directions in mapping the Earth’s surface with citizen science and generative AI iScience Earth sciences Environmental science Remote sensing Cartography |
| title | New directions in mapping the Earth’s surface with citizen science and generative AI |
| title_full | New directions in mapping the Earth’s surface with citizen science and generative AI |
| title_fullStr | New directions in mapping the Earth’s surface with citizen science and generative AI |
| title_full_unstemmed | New directions in mapping the Earth’s surface with citizen science and generative AI |
| title_short | New directions in mapping the Earth’s surface with citizen science and generative AI |
| title_sort | new directions in mapping the earth s surface with citizen science and generative ai |
| topic | Earth sciences Environmental science Remote sensing Cartography |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225001798 |
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