There are now a few different AI foundation models available for Earth Observation (EO) data. These vast neural networks can be rapidly fine-tuned for many downstream tasks, making them a highly versatile and appealing tool.

Today on Impact AI, I am joined by Hamed Alemohammad, Associate Professor in the Department of Geography at Clark University, Director of the Clark Center for Geospatial Analytics, and former Chief Data Scientist of the Radiant Earth Foundation, to discuss the applications of foundation models for remote sensing. Hamed’s research interests lie at the intersection of geographic information science and geography, using observations and analytical methods like machine learning to better understand the changing systems of our planet.

In this episode, he shares his perspective on the myriad purposes that foundation models serve and offers insight into training and fine-tuning them for different downstream applications. We also discuss how to choose the right one for a given project, ethical considerations for using them responsibly, and more. For a glimpse at the future of foundation models for remote sensing, tune in today!

“[Foundation models] are pre-trained on a large amount of unlabeled data. Secondly, they use self-supervised learning techniques – The third property is that you can fine-tune this model with a very small set of labeled data for multiple downstream tasks.” — Hamed Alemohammad

“It takes a lot to train a model, but you would not [do it] as frequently as you would [fine-tune] the model. You can use shared resources from different teams to do that - share it as an open-source model, and then anybody can fine-tune it for their downstream application.” — Hamed Alemohammad

“The promising future [for foundation models] will be combining different modes of data as input.” — Hamed Alemohammad

“There is a lot to do and the community is eager to learn, so if people are looking for challenging problems, I would encourage them to explore [the foundation model domain] and work with domain experts.” — Hamed Alemohammad

Hamed Alemohammad, Clark University 

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