PERSPECTIVE article
Front. Clim.
Sec. Climate, Ecology and People
Volume 7 - 2025 | doi: 10.3389/fclim.2025.1520242
This article is part of the Research TopicEcosystem Technology and Climate AdaptationView all 3 articles
Digital Applications Unlock Remote Sensing AI Foundation Models for Scalable Environmental Monitoring
Provisionally accepted
- 1The Earth Genome, Los Altos, California, United States
- 2AI - Journalism Resource Center, Oslo Metropolitan University, Oslo, Norway
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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.
Keywords: AI foundation models, satellite remote sensing, Environmental Monitoring, User interfaces, Human-AI interaction, AI model tuning, Ecotech
Received: 31 Oct 2024; Accepted: 25 Apr 2025.
Copyright: © 2025 Strong, Boyda, Kruse, Ingold and Maron. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Benjamin Strong, The Earth Genome, Los Altos, California, United States
Edward Boyda, AI - Journalism Resource Center, Oslo Metropolitan University, Oslo, Norway
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.