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

In previous posts, we have written how large language models (LLMs) like ChatGPT can be used in various urban analytical applications. We have kept exploring this potential especially with respect to citizen science applications. To this end we have just published a new paper in iScience, entitled "New Directions in Mapping the Earth’s Surface with Citizen Science and Generative AI". In the paper, lead by Linda See, we discuss how multi-modal LLMs (MLLMs) which are like LMMs but can take different forms of inputs (e.g., text, images, video) and output multi-modal information (e.g., take an image and output a description) could be leveraged to enhance citizen science land cover/land use mapping campaigns. If this sounds of interest, below you can read the abstract to the paper, see some of the figures we use to build our argument, while at the bottom of the post you can see the full reference and a link to the actual paper.

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.

Visual interpretation tasks undertaken by ChatGPT for (a) a wetland/mangrove landscape in South America (b) an agricultural area in central Europe.

Visual interpretation tasks undertaken by ChatGPT for identification of natural and non-natural ecosystems where ChatGPT misclassified the images as non-natural for locations in (a) Chad and (b) Austria. In (c), the image from Colombia was classified as unsure by validators but natural by ChatGPT.

Integrating multi-modal Large Language Models (MLLMs) in a citizen science visual interpretation workflow.

Full reference : 

See, L., Chen, Q., Crooks, A., Bayas, J.C.L., Fraisl, D., Fritz, S., Georgieva, I., Hager, G., Hofer, M., and Lesiv, M., Malek, Ž., Milenković, M., Moorthy, I., Orduña-Cabrera, F., Pérez-Guzmán, K., Schepaschenko, D., Shchepashchenko, M., Steinhauser, J.and McCallum, I. (2025), New Directions in Mapping the Earth’s Surface with Citizen Science and Generative AI, iScience, doi: https://doi.org/10.1016/j.isci.2025.111919. (pdf)