New Directions in Mapping the Earth’s Surface with Citizen Science and Generative AI

20377 11 108788 2 329606 document 108788 PIIS2589004225001798.pdf application/pdf a33750166ee1f29f9f24ad9097ff2c67 MD5 27591738 2025-02-03 12:29:12 https://pure.iiasa.ac.at/id/eprint/20377/1/PIIS2589004225001798.pdf 20377 1 1 application/pdf text en public cc_by_4

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/108788 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/108788 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/108788 archive 353 disk0/00/02/03/77 2025-02-03 12:47:14 2026-01-26 10:54:07 2025-02-03 12:47:14 article show See L. 8571 0000-0002-2665-7065 Chen Q. Crooks A. Laso Bayas J.C. 2106 0000-0003-2844-3842 Fraisl D. 2123 0000-0001-7523-7967 Fritz S. 8130 0000-0003-0420-8549 Georgieva I. 2242 0000-0002-5556-794X Hager G. 2130 0000-0003-2259-0278 Hofer M. 2330 0000-0002-2867-8943 Lesiv M. 8477 0000-0001-9846-3342 Malek Z. 1957 0000-0002-6981-6708 Milenkovic M. 2387 Moorthy I. 2089 0000-0002-3664-3855 Orduña-Cabrera F. 2239 0000-0002-8558-0053 Pérez Guzmán K. 2167 0000-0001-5189-6570 Shchepashchenko D. 7435 0000-0002-7814-4990 Shchepashchenko M. 2090 0000-0003-1081-2902 Steinhauser J. 2331 0000-0002-5989-6855 McCallum I. 1731 0000-0002-5812-9988 New Directions in Mapping the Earth’s Surface with Citizen Science and Generative AI pub ASA NODES BNR AFE ECE IACC SI 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. 2025-03-21 published Cell doi:10.1016/j.isci.2025.111919 276 172 2612 98 3152 2851 3301 181 2606 3524 211 3130 3065 279 2841 3348 202 public iScience e111919 TRUE 2589-0042 Evolution of the Copernicus Land Service portfolio (EVOLAND, HE 101082130) Open-Earth-Monitor Cyberinfrastructure (OEMC, HE 101059548) yes info:eu-repo/grantAgreement/EC/HORIZON/101082130/EU//EVOLAND; info:eu-repo/grantAgreement/EC/HORIZON/101059548/EU//OEMC info:eu-repo/semantics/article info:eu-repo/semantics/openAccess