arXiv:2505.24528v3 Announce Type: replace-cross Abstract: Foundation Models (FMs) are large-scale, pre-trained artificial intelligence (AI) systems that have revolutionized natural language processing and computer vision, and are now advancing geospatial analysis and Earth Observation (EO). They promise improved generalization across tasks, scalability, and efficient adaptation with minimal labeled data. However, despite the rapid proliferation of geospatial FMs, their real-world utility and alignment with global sustainability goals remain underexplored. We introduce SustainFM, a comprehensiv
The proliferation of Foundation Models and the increasing urgency of sustainability goals are creating immediate demand for AI solutions that can address global challenges.
Sophisticated geospatial AI, specifically Foundation Models, will enable more precise and scalable progress on critical sustainability objectives, impacting resource management, climate adaptation, and disaster response.
The deployment of Geospatial Foundation Models will shift how environmental monitoring, urban planning, and resource allocation are conducted, moving from reactive to predictive and optimized approaches.
- · AI developers
- · Environmental monitoring agencies
- · Sustainable development organizations
- · Governments relying on Earth Observation data
- · Traditional geospatial analytics firms
- · Organizations relying on outdated data collection methods
Geospatial Foundation Models will accelerate data-driven decision-making for sustainable development.
Improved predictive capabilities will lead to more efficient allocation of resources and proactive mitigation strategies for environmental challenges.
The integration of AI into global sustainability efforts could foster new geopolitical cooperations and competitions over data and model supremacy.
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