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See, L. 
ORCID: https://orcid.org/0000-0002-2665-7065, Chen, Q., Crooks, A., Laso Bayas, J.C. ORCID: https://orcid.org/0000-0003-2844-3842, Fraisl, D. ORCID: https://orcid.org/0000-0001-7523-7967, Fritz, S. ORCID: https://orcid.org/0000-0003-0420-8549, Georgieva, I. ORCID: https://orcid.org/0000-0002-5556-794X, Hager, G. ORCID: https://orcid.org/0000-0003-2259-0278, Hofer, M. ORCID: https://orcid.org/0000-0002-2867-8943, Lesiv, M. ORCID: https://orcid.org/0000-0001-9846-3342, Malek, Z. ORCID: https://orcid.org/0000-0002-6981-6708, Milenkovic, M., Moorthy, I., Orduña-Cabrera, F. ORCID: https://orcid.org/0000-0002-8558-0053, Pérez Guzmán, K. ORCID: https://orcid.org/0000-0001-5189-6570, Shchepashchenko, D. ORCID: https://orcid.org/0000-0002-7814-4990, Shchepashchenko, M., Steinhauser, J. ORCID: https://orcid.org/0000-0002-5989-6855, & McCallum, I. ORCID: https://orcid.org/0000-0002-5812-9988
(2025).
New Directions in Mapping the Earth’s Surface with Citizen Science and Generative AI.
iScience e111919. 10.1016/j.isci.2025.111919.
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Abstract
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.
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