# Earth AI: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoning

arXiv: https://arxiv.org/abs/2510.18318 · HTML: https://mlsanity.com/paper/2510.18318/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2025.

## Built on by (most cited first, 13 of 13)

- [Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems](https://mlsanity.com/paper/2601.01891/) (2026)
- [GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation Models](https://mlsanity.com/paper/2606.12821/) (2026)
- [RAPID: A Reproducible Multi-Agent Pipeline for Interpretable Disaster Damage Assessment from Satellite and Street-View Imagery](https://mlsanity.com/paper/2606.21819/) (2026)
- [Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings](https://mlsanity.com/paper/2608.26088/) (2026)
- [The Global-Local loop: what is missing in bridging the gap between geospatial data from numerous communities?](https://mlsanity.com/paper/2603.20305/) (2026)
- [Geospatial foundation-model embeddings improve population estimation unevenly across space and scale](https://mlsanity.com/paper/2605.01650/) (2026)
- [The Emerging Paradigm of Geospatial Foundation Models: From Pre-Training to Agentic Reasoning](https://mlsanity.com/paper/2607.12177/) (2026)
- [Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams](https://mlsanity.com/paper/2608.00012/) (2026)
- [EarthVerse: Benchmarking Scientific Agents Across Dynamic Earth Systems and Natural Hazards](https://mlsanity.com/paper/2608.23525/) (2026)
- [Physico-Geospatial Grounded Scene Interpretation for Mobile Robotics](https://mlsanity.com/paper/2609.06629/) (2026)
- [Planetary Geospatial Foundation Models: A New Paradigm for Global Public Health](https://mlsanity.com/paper/2610.05699/) (2026)
- [Google Earth AI Geospatial Reasoning Agent: Capabilities and a Systematic Evaluation](https://mlsanity.com/paper/2610.15750/) (2026)
- [Trust Before Autonomy: Rethinking Agentic Remote Sensing for Crisis Response](https://mlsanity.com/paper/2610.18789/) (2026)

## Abstract (arXiv)

Geospatial data offers immense potential for understanding our planet. However, the sheer volume and diversity of this data along with its varied resolutions, timescales, and sparsity pose significant challenges for thorough analysis and interpretation. This paper introduces Earth AI, a family of geospatial AI models and agentic reasoning that enables significant advances in our ability to unlock novel and profound insights into our planet. This approach is built upon foundation models across three key domains--Planet-scale Imagery, Population, and Environment--and an intelligent Gemini-powered reasoning engine. We present rigorous benchmarks showcasing the power and novel capabilities of our foundation models and validate that when used together, they provide complementary value for geospatial inference and their synergies unlock superior predictive capabilities. To handle complex, multi-step queries, we developed a Gemini-powered agent that jointly reasons over our multiple foundation models along with large geospatial data sources and tools. On a new benchmark of real-world crisis scenarios, our agent demonstrates the ability to deliver critical and timely insights, effectively bridging the gap between raw geospatial data and actionable understanding.