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

• URL: http://arxiv.org/abs/2510.18318v3
• Date: Fri, 07 Nov 2025 22:10:34 GMT
• Title: Earth AI: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoning
• Authors: Aaron Bell, Amit Aides, Amr Helmy, Arbaaz Muslim, Aviad Barzilai, Aviv Slobodkin, Bolous Jaber, David Schottlander, George Leifman, Joydeep Paul, Mimi Sun, Nadav Sherman, Natalie Williams, Per Bjornsson, Roy Lee, Ruth Alcantara, Thomas Turnbull, Tomer Shekel, Vered Silverman, Yotam Gigi, Adam Boulanger, Alex Ottenwess, Ali Ahmadalipour, Anna Carter, Behzad Vahedi, Charles Elliott, David Andre, Elad Aharoni, Gia Jung, Hassler Thurston, Jacob Bien, Jamie McPike, Jessica Sapick, Juliet Rothenberg, Kartik Hegde, Kel Markert, Kim Philipp Jablonski, Luc Houriez, Monica Bharel, Phing VanLee, Reuven Sayag, Sebastian Pilarski, Shelley Cazares, Shlomi Pasternak, Siduo Jiang, Thomas Colthurst, Yang Chen, Yehonathan Refael, Yochai Blau, Yuval Carny, Yael Maguire, Avinatan Hassidim, James Manyika, Tim Thelin, Genady Beryozkin, Gautam Prasad, Luke Barrington, Yossi Matias, Niv Efron, Shravya Shetty,
• Abstract summary: This paper introduces Earth AI, a family of geospatial AI models and agentic reasoning.<n>A Gemini-powered agent handles complex, multi-step queries.<n>On a new benchmark of real-world crisis scenarios, our agent demonstrates the ability to deliver critical and timely insights.
• Score: 11.611027504613324
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics [91.17301794848025]
  This paper presents GeoAgent, a model capable of reasoning closely with humans and deriving fine-grained address conclusions.<n>Previous RL-based methods have achieved breakthroughs in performance and interpretability but still remain concerns because of their reliance on AI-generated chain-of-thought (CoT) data and training strategies.
  arXiv  Detail & Related papers  (2026-02-13T04:48:05Z)
• Tobler's First Law in GeoAI: A Spatially Explicit Deep Learning Model for Terrain Feature Detection Under Weak Supervision [2.083046809527675]
  This paper reports our work in developing a deep learning model that enables object detection in a weakly supervised manner.<n>The model generalizes to both natural and human-made features on the surfaces of Earth and other planets.
  arXiv  Detail & Related papers  (2025-08-01T21:47:50Z)
• Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches [76.12691010182802]
  This survey focuses on enabling agentic artificial intelligence (AI) in satellite-augmented low-altitude economy and terrestrial networks (SLAETNs)<n>We introduce the architecture and characteristics of SLAETNs, and analyze the challenges that arise in integrating satellite, aerial, and terrestrial components.<n>We examine how these models empower agentic functions across three domains: communication enhancement, security and privacy protection, and intelligent satellite tasks.
  arXiv  Detail & Related papers  (2025-07-19T14:07:05Z)
• OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence [51.0456395687016]
  multimodal large language models (LLMs) have opened new frontiers in artificial intelligence.<n>We propose a MLLM (OmniGeo) tailored to geospatial applications.<n>By combining the strengths of natural language understanding and spatial reasoning, our model enhances the ability of instruction following and the accuracy of GeoAI systems.
  arXiv  Detail & Related papers  (2025-03-20T16:45:48Z)
• Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
  We introduce a comprehensive geolocation framework with three key components.<n>GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric.<n>We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
  arXiv  Detail & Related papers  (2025-02-19T14:21:25Z)
• PEACE: Empowering Geologic Map Holistic Understanding with MLLMs [64.58959634712215]
  Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface.<n>Despite their significance, current Multimodal Large Language Models (MLLMs) often fall short in geologic map understanding.<n>To quantify this gap, we construct GeoMap-Bench, the first-ever benchmark for evaluating MLLMs in geologic map understanding.
  arXiv  Detail & Related papers  (2025-01-10T18:59:42Z)
• A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys [13.150829303910385]
  We introduce a promptable foundation model for interpreting any geobodies across seismic surveys. The model integrates a pre-trained vision foundation model (VFM) with a sophisticated multi-modal prompt engine. Our approach establishes a new paradigm for geoscientific data interpretation, with broad potential for transfer to other tasks.
  arXiv  Detail & Related papers  (2024-09-08T03:44:23Z)
• Self-Supervised Representation Learning for Geospatial Objects: A Survey [21.504978593542354]
  Self-supervised learning (SSL) has garnered increasing attention for its ability to learn effective and generalizable representations directly from data without extensive labeled supervision.<n>This paper presents a survey of SSL techniques specifically applied to or developed for geospatial objects in three primary geometric vector types: Point, Polyline, and Polygon.<n>We examine the emerging trends in SSL for geospatial objects, particularly the gradual advancements towards geospatial foundation models.
  arXiv  Detail & Related papers  (2024-08-22T05:28:22Z)
• When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System [6.445323648941926]
  Geoscience foundation models (GFMs) are a paradigm-shifting solution, integrating extensive cross-disciplinary data to enhance the simulation and understanding of Earth system dynamics. The unique strengths of GFMs include flexible task specification, diverse input-output capabilities, and multi-modal knowledge representation. This review offers a comprehensive overview of emerging geoscientific research paradigms, emphasizing the untapped opportunities at the intersection of advanced AI techniques and geoscience.
  arXiv  Detail & Related papers  (2023-09-13T08:44:09Z)
• On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence [39.86997089245117]
  Foundations models (FMs) can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or zero-shot learning. We propose that one of the major challenges of developing a FM for GeoAI is to address the multimodality nature of geospatial tasks.
  arXiv  Detail & Related papers  (2023-04-13T19:50:17Z)
• A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
  We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
  arXiv  Detail & Related papers  (2022-11-04T09:58:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.