Artificial intelligence is transforming Earth observation and mapping, which were traditionally labor-intensive, slow, and costly processes. Detailed maps of vegetation, land cover, topography, or erosion once required extensive fieldwork and manual interpretation. Today, AI-powered Earth observation models automate much of that work by processing vast amounts of satellite and sensor data to produce accurate, multi-layered geographic insights quickly, changing the way research, public planning, and commercial decision-making are conducted.
Central to this shift are Large Earth Observation Models (LEOMs), AI systems trained on diverse geospatial datasets that enable real-time cartographic analysis and environmental monitoring. These models can perform complex tasks such as identifying vegetation health, tracking land-use changes, and estimating biomass or carbon stocks. Their flexibility makes them valuable across climate science, urban planning, conservation, and natural resource management, offering analyses that would be technically challenging or prohibitively expensive without AI.
Some open-source LEOMs, like the model known as Clay, are gaining attention for their accessibility and potential to democratize advanced mapping tools. Foundations and research groups are using these models not only for academic purposes but also to guide practical projects — for instance, identifying areas suitable for renewable energy development or supporting humanitarian and environmental initiatives. While the technology is still evolving and not yet fully plug-and-play, its rapid development suggests these tools will soon shape how governments, NGOs, and businesses visualize and manage landscapes at scale.
Beyond environmental uses, LEOM-driven insights are increasingly paired with robotics and automation systems, such as autonomous harvesters or infrastructure-monitoring drones, hinting at a future where AI doesn’t just interpret the land but also helps act on those insights. As these models become more integrated into public infrastructure projects and commercial platforms, the impact of AI on understanding and responding to changes on Earth’s surface is poised to grow significantly.
