Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.18318 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910025249718272 |
|---|---|
| author | Bell, Aaron Aides, Amit Helmy, Amr Muslim, Arbaaz Barzilai, Aviad Slobodkin, Aviv Jaber, Bolous Schottlander, David Leifman, George Paul, Joydeep Sun, Mimi Sherman, Nadav Williams, Natalie Bjornsson, Per Lee, Roy Alcantara, Ruth Turnbull, Thomas Shekel, Tomer Silverman, Vered Gigi, Yotam Boulanger, Adam Ottenwess, Alex Ahmadalipour, Ali Carter, Anna Vahedi, Behzad Elliott, Charles Andre, David Aharoni, Elad Jung, Gia Thurston, Hassler Bien, Jacob McPike, Jamie Sapick, Jessica Rothenberg, Juliet Hegde, Kartik Markert, Kel Jablonski, Kim Philipp Houriez, Luc Bharel, Monica VanLee, Phing Sayag, Reuven Pilarski, Sebastian Cazares, Shelley Pasternak, Shlomi Jiang, Siduo Colthurst, Thomas Chen, Yang Refael, Yehonathan Blau, Yochai Carny, Yuval Maguire, Yael Hassidim, Avinatan Manyika, James Thelin, Tim Beryozkin, Genady Prasad, Gautam Barrington, Luke Matias, Yossi Efron, Niv Shetty, Shravya |
| author_facet | Bell, Aaron Aides, Amit Helmy, Amr Muslim, Arbaaz Barzilai, Aviad Slobodkin, Aviv Jaber, Bolous Schottlander, David Leifman, George Paul, Joydeep Sun, Mimi Sherman, Nadav Williams, Natalie Bjornsson, Per Lee, Roy Alcantara, Ruth Turnbull, Thomas Shekel, Tomer Silverman, Vered Gigi, Yotam Boulanger, Adam Ottenwess, Alex Ahmadalipour, Ali Carter, Anna Vahedi, Behzad Elliott, Charles Andre, David Aharoni, Elad Jung, Gia Thurston, Hassler Bien, Jacob McPike, Jamie Sapick, Jessica Rothenberg, Juliet Hegde, Kartik Markert, Kel Jablonski, Kim Philipp Houriez, Luc Bharel, Monica VanLee, Phing Sayag, Reuven Pilarski, Sebastian Cazares, Shelley Pasternak, Shlomi Jiang, Siduo Colthurst, Thomas Chen, Yang Refael, Yehonathan Blau, Yochai Carny, Yuval Maguire, Yael Hassidim, Avinatan Manyika, James Thelin, Tim Beryozkin, Genady Prasad, Gautam Barrington, Luke Matias, Yossi Efron, Niv Shetty, Shravya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18318 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Earth AI: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoning Bell, Aaron Aides, Amit Helmy, Amr Muslim, Arbaaz Barzilai, Aviad Slobodkin, Aviv Jaber, Bolous Schottlander, David Leifman, George Paul, Joydeep Sun, Mimi Sherman, Nadav Williams, Natalie Bjornsson, Per Lee, Roy Alcantara, Ruth Turnbull, Thomas Shekel, Tomer Silverman, Vered Gigi, Yotam Boulanger, Adam Ottenwess, Alex Ahmadalipour, Ali Carter, Anna Vahedi, Behzad Elliott, Charles Andre, David Aharoni, Elad Jung, Gia Thurston, Hassler Bien, Jacob McPike, Jamie Sapick, Jessica Rothenberg, Juliet Hegde, Kartik Markert, Kel Jablonski, Kim Philipp Houriez, Luc Bharel, Monica VanLee, Phing Sayag, Reuven Pilarski, Sebastian Cazares, Shelley Pasternak, Shlomi Jiang, Siduo Colthurst, Thomas Chen, Yang Refael, Yehonathan Blau, Yochai Carny, Yuval Maguire, Yael Hassidim, Avinatan Manyika, James Thelin, Tim Beryozkin, Genady Prasad, Gautam Barrington, Luke Matias, Yossi Efron, Niv Shetty, Shravya Artificial Intelligence 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. |
| title | Earth AI: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.18318 |