AUTHOR=Strong Benjamin , Boyda Edward , Kruse Cameron , Ingold Tom , Maron Mikel 

TITLE=Digital applications unlock remote sensing AI foundation models for scalable environmental monitoring

JOURNAL=Frontiers in Climate

VOLUME=Volume 7 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2025.1520242

DOI=10.3389/fclim.2025.1520242

ISSN=2624-9553

ABSTRACT=Remote sensing AI foundation models, which are large, pre-trained models adaptable to various tasks, dramatically reduce the resources required to perform environmental monitoring, a central task for developing ecosystem technologies. However, the unique challenges associated with remote sensing data necessitate the development of digital applications to effectively utilize these models. Here, we discuss early examples of user-centered digital applications that enhance the impact of remote sensing AI foundation models. By simplifying model training and inference, these applications open traditional machine learning tasks to a range of users, ultimately resulting in more locally-tuned, accurate, and practical data.