# Earth AI

Published articles for Earth AI.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Putting Earth AI to work: Geospatial agents for our planet's biggest challenges

DevFeed: [Putting Earth AI to work: Geospatial agents for our planet's biggest challenges](<https://devfeed.tech/articles/putting-earth-ai-to-work-geospatial-agents-for-our-planet-s-biggest-challenges-87290.md>)

Original publisher: [Read original article](<https://research.google/blog/putting-earth-ai-to-work-geospatial-agents-for-our-planets-biggest-challenges/>)

Published: 2026-10-14T16:14:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [geospatial](<https://devfeed.tech/topics/geospatial.md>), [agent orchestration](<https://devfeed.tech/topics/agent-orchestration.md>), [rlvr](<https://devfeed.tech/topics/rlvr.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [GeoJSON](<https://devfeed.tech/topics/geojson.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [earth](<https://devfeed.tech/tags/earth.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [evaluations](<https://devfeed.tech/tags/evaluations.md>), [general-science](<https://devfeed.tech/tags/general-science.md>), [geospatial](<https://devfeed.tech/tags/geospatial.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>)

### AI overview

Google Research describes an experimental Geospatial Reasoning agent that combines Gemini, Earth AI models, spatial tools, and upfront planning to analyze large geospatial datasets and train predictive models. Its evaluation finds that domain-specific skills improve performance on several benchmarks, while planning helps with open-ended, multi-step analyses. The agent is available to limited early users, and Google is releasing development splits for two benchmarks.

### Source excerpt

Earth AI

## Google Earth AI's Population Dynamics Foundation Model supports public health research

DevFeed: [Google Earth AI's Population Dynamics Foundation Model supports public health research](<https://devfeed.tech/articles/unlocking-earth-ai-s-planetary-geospatial-foundation-models-for-global-public-health-66369.md>)

Original publisher: [Read original article](<https://research.google/blog/earth-ais-planetary-geospatial-foundation-models-for-global-public-health/>)

Published: 2026-10-06T15:05:11Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [AI Foundation Models](<https://devfeed.tech/topics/ai-foundation-models.md>), [geospatial](<https://devfeed.tech/topics/geospatial.md>), [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [lunar foundation model](<https://devfeed.tech/topics/lunar-foundation-model.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [earth](<https://devfeed.tech/tags/earth.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [foundation-models](<https://devfeed.tech/tags/foundation-models.md>), [geospatial](<https://devfeed.tech/tags/geospatial.md>), [global](<https://devfeed.tech/tags/global.md>), [health](<https://devfeed.tech/tags/health.md>), [health-bioscience](<https://devfeed.tech/tags/health-bioscience.md>), [public-health](<https://devfeed.tech/tags/public-health.md>)

### AI overview

Google Research presents five partner-led case studies evaluating its Population Dynamics Foundation Model (PDFM), which turns privacy-preserving population and environmental signals into location embeddings for epidemiological workflows. Across studies involving measles vaccination, cardiovascular disease, dengue, postpartum depression and cholera, the embeddings supplemented existing data and models, with reported benefits varying by task and forecast horizon. The article also notes limitations including static snapshots and ongoing work on dynamic embeddings and geographic transfer.

### Source excerpt

Earth AI

## Introducing WeatherNext 3, our most advanced and accurate global weather AI model

DevFeed: [Introducing WeatherNext 3, our most advanced and accurate global weather AI model](<https://devfeed.tech/articles/introducing-weathernext-3-our-most-advanced-and-accurate-global-weather-ai-model-89302.md>)

Original publisher: [Read original article](<https://blog.google/innovation-and-ai/models-and-research/google-deepmind/introducing-weathernext-3/>)

Author: The WeatherNext team

Published: 2026-09-03T15:00:00Z

Content type: release

Language: en

Sources: [Google DeepMind](<https://devfeed.tech/sources/google-deepmind.md>)

Topics: [Climate / Weather / Ocean Modeling](<https://devfeed.tech/topics/climate-weather-ocean-modeling.md>), [Factuality scoring in AI](<https://devfeed.tech/topics/factuality-scoring-in-ai.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [bigquery](<https://devfeed.tech/tags/bigquery.md>), [cloud-storage](<https://devfeed.tech/tags/cloud-storage.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [generate](<https://devfeed.tech/tags/generate.md>), [google-deepmind](<https://devfeed.tech/tags/google-deepmind.md>), [none](<https://devfeed.tech/tags/none.md>), [weather](<https://devfeed.tech/tags/weather.md>)

### AI overview

Google DeepMind and Google Research introduce WeatherNext 3, a global weather forecasting model that uses hourly satellite observations and station data to produce more localized forecasts, including at resolutions as fine as 5 kilometers. The article describes improved precipitation forecasts, wind and solar energy predictions, and plans to make forecast data available through Google products and cloud data services.

### Source excerpt

Text "WeatherNext3" over a blue and yellow saturated image of clouds

## Planetary prediction engine: Automating global models via Earth AI

DevFeed: [Planetary prediction engine: Automating global models via Earth AI](<https://devfeed.tech/articles/planetary-prediction-engine-automating-global-models-via-earth-ai-6846.md>)

Original publisher: [Read original article](<https://research.google/blog/planetary-prediction-engine-automating-global-models-via-earth-ai/>)

Published: 2026-08-27T17:37:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [data-engineering](<https://devfeed.tech/topics/data-engineering.md>), [Google](<https://devfeed.tech/topics/google.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [Feature Engineering](<https://devfeed.tech/topics/feature-engineering.md>), [Model Development](<https://devfeed.tech/topics/model-development.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [analytics](<https://devfeed.tech/tags/analytics.md>), [data-engineering](<https://devfeed.tech/tags/data-engineering.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [feature-engineering](<https://devfeed.tech/tags/feature-engineering.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [insights](<https://devfeed.tech/tags/insights.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [mapping](<https://devfeed.tech/tags/mapping.md>), [research](<https://devfeed.tech/tags/research.md>), [training](<https://devfeed.tech/tags/training.md>), [validation](<https://devfeed.tech/tags/validation.md>), [workflow](<https://devfeed.tech/tags/workflow.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

Google Research introduces the Planetary Prediction Engine, an experimental Earth AI capability that autonomously performs geospatial data discovery, cleanup, feature engineering, model training, evaluation, and report generation from natural-language queries. The system targets applications including public health, food security, environmental risk, and socioeconomic analysis, reducing the stated workflow from weeks of manual data engineering to minutes.

### Source excerpt

Earth AI

## How mobility gives language models a deeper understanding of place

DevFeed: [How mobility gives language models a deeper understanding of place](<https://devfeed.tech/articles/how-mobility-gives-language-models-a-deeper-understanding-of-place-6814.md>)

Original publisher: [Read original article](<https://research.google/blog/how-mobility-gives-language-models-a-deeper-understanding-of-place/>)

Published: 2026-08-21T10:54:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [data](<https://devfeed.tech/topics/data.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [google](<https://devfeed.tech/tags/google.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [mobility](<https://devfeed.tech/tags/mobility.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [points](<https://devfeed.tech/tags/points.md>), [research](<https://devfeed.tech/tags/research.md>), [vector](<https://devfeed.tech/tags/vector.md>)

### AI overview

Google Research introduces Mobility-Embedded POIs (ME-POIs), a framework that combines language-model-based text representations of places with aggregated, anonymized mobility patterns. The resulting embeddings capture both a place's identity and its changing functional activity, improving predictions such as visit intent, price level, opening hours, and busyness.

### Source excerpt

Algorithms & Theory

## Expanding our Heat Resilience data to 50+ global cities

DevFeed: [Expanding our Heat Resilience data to 50+ global cities](<https://devfeed.tech/articles/expanding-our-heat-resilience-data-to-50-global-cities-6771.md>)

Original publisher: [Read original article](<https://research.google/blog/expanding-our-heat-resilience-data-to-50-global-cities/>)

Published: 2026-06-30T17:03:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [App](<https://devfeed.tech/topics/app.md>), [data](<https://devfeed.tech/topics/data.md>), [Resilience](<https://devfeed.tech/topics/resilience.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [app](<https://devfeed.tech/tags/app.md>), [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [heat](<https://devfeed.tech/tags/heat.md>), [mapping](<https://devfeed.tech/tags/mapping.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source-models-datasets](<https://devfeed.tech/tags/open-source-models-datasets.md>), [research](<https://devfeed.tech/tags/research.md>), [resilience](<https://devfeed.tech/tags/resilience.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>), [tools](<https://devfeed.tech/tags/tools.md>)

### AI overview

Google Research is expanding its building-level rooftop reflectivity dataset to cover more than 50 global cities. The data is available through a high-resolution Heat Resilience Earth Engine App and is intended to help urban planners prioritize cool-roof interventions that reduce heat exposure and protect vulnerable communities.

### Source excerpt

Climate & Sustainability

## From pixels to planning: Earth AI for nature restoration

DevFeed: [From pixels to planning: Earth AI for nature restoration](<https://devfeed.tech/articles/from-pixels-to-planning-earth-ai-for-nature-restoration-6782.md>)

Original publisher: [Read original article](<https://research.google/blog/from-pixels-to-planning-earth-ai-for-nature-restoration/>)

Published: 2026-06-16T17:30:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Google](<https://devfeed.tech/topics/google.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [data](<https://devfeed.tech/tags/data.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [open-source-models-datasets](<https://devfeed.tech/tags/open-source-models-datasets.md>), [research](<https://devfeed.tech/tags/research.md>), [resource](<https://devfeed.tech/tags/resource.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>), [uk](<https://devfeed.tech/tags/uk.md>), [vector](<https://devfeed.tech/tags/vector.md>)

### AI overview

Google Research describes a high-resolution deep learning approach that converts pixel-based maps of fine-scale ecological features into a vectorized dataset. The resource is intended to support nature restoration, carbon accounting, and biodiversity efforts across working landscapes in the UK while considering food security.

### Source excerpt

Climate & Sustainability

## The next chapter in flood resilience: Open sourcing Google's hydrology framework

DevFeed: [The next chapter in flood resilience: Open sourcing Google's hydrology framework](<https://devfeed.tech/articles/the-next-chapter-in-flood-resilience-open-sourcing-google-s-hydrology-framework-6892.md>)

Original publisher: [Read original article](<https://research.google/blog/the-next-chapter-in-flood-resilience-open-sourcing-googles-hydrology-framework/>)

Published: 2026-06-03T18:37:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Google](<https://devfeed.tech/topics/google.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [Resilience](<https://devfeed.tech/topics/resilience.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [Python](<https://devfeed.tech/topics/python.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [Google AI](<https://devfeed.tech/topics/google-ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [data](<https://devfeed.tech/tags/data.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [github](<https://devfeed.tech/tags/github.md>), [google](<https://devfeed.tech/tags/google.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [open-source-models-datasets](<https://devfeed.tech/tags/open-source-models-datasets.md>), [python](<https://devfeed.tech/tags/python.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [research](<https://devfeed.tech/tags/research.md>), [resilience](<https://devfeed.tech/tags/resilience.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

Google Research is open-sourcing a Python and PyTorch hydrology framework for AI-based riverine flood forecasting, enabling meteorological and hydrological agencies to use local data, train models, and improve flood warnings.

### Source excerpt

Climate & Sustainability

## Mapping the modern world: How S2Vec learns the language of our cities

DevFeed: [Mapping the modern world: How S2Vec learns the language of our cities](<https://devfeed.tech/articles/mapping-the-modern-world-how-s2vec-learns-the-language-of-our-cities-6835.md>)

Original publisher: [Read original article](<https://research.google/blog/mapping-the-modern-world-how-s2vec-learns-the-language-of-our-cities/>)

Published: 2026-03-24T17:42:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [multimodal-ai](<https://devfeed.tech/topics/multimodal-ai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [AI Foundation Models](<https://devfeed.tech/topics/ai-foundation-models.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [data](<https://devfeed.tech/tags/data.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [foundation-models](<https://devfeed.tech/tags/foundation-models.md>), [google](<https://devfeed.tech/tags/google.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mapping](<https://devfeed.tech/tags/mapping.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [research](<https://devfeed.tech/tags/research.md>)

### AI overview

S2Vec is a self-supervised framework that converts complex geospatial data about the built environment into general-purpose embeddings. The article describes how these embeddings support prediction of socioeconomic and environmental patterns, while noting stronger results for geographic adaptation and remaining limitations on environmental tasks.

### Source excerpt

Algorithms & Theory

## Protecting cities with AI-driven flash flood forecasting

DevFeed: [Protecting cities with AI-driven flash flood forecasting](<https://devfeed.tech/articles/protecting-cities-with-ai-driven-flash-flood-forecasting-6850.md>)

Original publisher: [Read original article](<https://research.google/blog/protecting-cities-with-ai-driven-flash-flood-forecasting/>)

Published: 2026-03-12T13:03:15Z

Content type: news

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Resilience](<https://devfeed.tech/topics/resilience.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-training](<https://devfeed.tech/tags/ai-training.md>), [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [data](<https://devfeed.tech/tags/data.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [flash](<https://devfeed.tech/tags/flash.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [news](<https://devfeed.tech/tags/news.md>), [open-source-models-datasets](<https://devfeed.tech/tags/open-source-models-datasets.md>), [research](<https://devfeed.tech/tags/research.md>), [resilience](<https://devfeed.tech/tags/resilience.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>)

### AI overview

Google Research announces urban flash flood forecasts that use an AI-powered methodology to provide up to 24 hours of advance warning. The article describes the forecasting challenge posed by rapidly developing floods, limited ground-truth data, and the need to expand early-warning coverage for vulnerable communities.

### Source excerpt

Climate & Sustainability

## Where wild things roam: Identifying wildlife with SpeciesNet

DevFeed: [Where wild things roam: Identifying wildlife with SpeciesNet](<https://devfeed.tech/articles/where-wild-things-roam-identifying-wildlife-with-speciesnet-6929.md>)

Original publisher: [Read original article](<https://research.google/blog/where-wild-things-roam-identifying-wildlife-with-speciesnet/>)

Published: 2026-03-06T17:59:38Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Data analysis](<https://devfeed.tech/topics/data-analysis.md>), [data](<https://devfeed.tech/topics/data.md>), [migration](<https://devfeed.tech/topics/migration.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [migration](<https://devfeed.tech/tags/migration.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [open-source-models-datasets](<https://devfeed.tech/tags/open-source-models-datasets.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>)

### AI overview

Google Research describes SpeciesNet, an open-source AI model that identifies nearly 2,500 animal categories in camera-trap images. Trained on 65 million labelled images, it is being used by research groups worldwide to support wildlife monitoring, conservation, and analysis of animal populations and patterns.

### Source excerpt

Climate & Sustainability

## Accelerating discovery in India through AI-powered science and education

DevFeed: [Accelerating discovery in India through AI-powered science and education](<https://devfeed.tech/articles/accelerating-discovery-in-india-through-ai-powered-science-and-education-6130.md>)

Original publisher: [Read original article](<https://deepmind.google/blog/accelerating-discovery-in-india-through-ai-powered-science-and-education/>)

Author: Demis Hassabis; Lila Ibrahim; Pushmeet Kohli

Published: 2026-02-17T13:42:20Z

Content type: article

Language: en

Sources: [Google DeepMind News](<https://devfeed.tech/sources/google-deepmind-news.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [Google](<https://devfeed.tech/topics/google.md>), [Frontier AI](<https://devfeed.tech/topics/frontier-ai.md>), [Earth AI](<https://devfeed.tech/topics/earth-ai.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [ai](<https://devfeed.tech/tags/ai.md>), [collaboration](<https://devfeed.tech/tags/collaboration.md>), [community](<https://devfeed.tech/tags/community.md>), [contests](<https://devfeed.tech/tags/contests.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [frontier-ai](<https://devfeed.tech/tags/frontier-ai.md>), [global](<https://devfeed.tech/tags/global.md>), [google](<https://devfeed.tech/tags/google.md>), [india](<https://devfeed.tech/tags/india.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [research](<https://devfeed.tech/tags/research.md>), [responsibility-safety](<https://devfeed.tech/tags/responsibility-safety.md>), [science](<https://devfeed.tech/tags/science.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

Google DeepMind is establishing a National Partnership for AI with Indian government bodies and local institutions to expand access to frontier AI capabilities for science and education. The collaboration with India's Anusandhan National Research Foundation includes access to AI models and tools, hackathons, community contests, training, and mentorship for students, researchers, and early-career professionals.

### Source excerpt

Google DeepMind brings National Partnerships for AI initiative to India, scaling AI for science and education

## NeuralGCM harnesses AI to better simulate long-range global precipitation

DevFeed: [NeuralGCM harnesses AI to better simulate long-range global precipitation](<https://devfeed.tech/articles/neuralgcm-harnesses-ai-to-better-simulate-long-range-global-precipitation-6837.md>)

Original publisher: [Read original article](<https://research.google/blog/neuralgcm-harnesses-ai-to-better-simulate-long-range-global-precipitation/>)

Published: 2026-01-12T17:52:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Google](<https://devfeed.tech/topics/google.md>), [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [AI Research](<https://devfeed.tech/topics/ai-research.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [general-science](<https://devfeed.tech/tags/general-science.md>), [global](<https://devfeed.tech/tags/global.md>), [google](<https://devfeed.tech/tags/google.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [nasa](<https://devfeed.tech/tags/nasa.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source-models-datasets](<https://devfeed.tech/tags/open-source-models-datasets.md>), [research](<https://devfeed.tech/tags/research.md>), [science](<https://devfeed.tech/tags/science.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>)

### AI overview

Google Research presents NeuralGCM, a hybrid atmospheric model that combines physics-based modeling with a neural network trained on NASA satellite precipitation observations. The model improves global precipitation simulations, including average rainfall, extreme events, and daily weather cycles, while supporting longer-range weather and climate research.

### Source excerpt

Climate & Sustainability

## WeatherNext 2: Our most advanced weather forecasting model

DevFeed: [WeatherNext 2: Our most advanced weather forecasting model](<https://devfeed.tech/articles/weathernext-2-our-most-advanced-weather-forecasting-model-6258.md>)

Original publisher: [Read original article](<https://deepmind.google/blog/weathernext-2-our-most-advanced-weather-forecasting-model/>)

Author: The WeatherNext team

Published: 2025-11-17T15:09:23Z

Content type: release

Language: en

Sources: [Google DeepMind News](<https://devfeed.tech/sources/google-deepmind-news.md>)

Topics: [Climate / Weather / Ocean Modeling](<https://devfeed.tech/topics/climate-weather-ocean-modeling.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model architecture](<https://devfeed.tech/topics/model-architecture.md>), [Open Data](<https://devfeed.tech/topics/open-data.md>), [Infrastructure as a Service (IaaS)](<https://devfeed.tech/topics/infrastructure-as-a-service-iaas.md>)

Tags: [agencies](<https://devfeed.tech/tags/agencies.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-model](<https://devfeed.tech/tags/ai-model.md>), [ai-platform](<https://devfeed.tech/tags/ai-platform.md>), [api](<https://devfeed.tech/tags/api.md>), [applications](<https://devfeed.tech/tags/applications.md>), [bigquery](<https://devfeed.tech/tags/bigquery.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [deepmind](<https://devfeed.tech/tags/deepmind.md>), [developer](<https://devfeed.tech/tags/developer.md>), [earth](<https://devfeed.tech/tags/earth.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [forecasting](<https://devfeed.tech/tags/forecasting.md>), [global](<https://devfeed.tech/tags/global.md>), [google](<https://devfeed.tech/tags/google.md>), [google-deepmind](<https://devfeed.tech/tags/google-deepmind.md>), [model](<https://devfeed.tech/tags/model.md>), [none](<https://devfeed.tech/tags/none.md>), [predictions](<https://devfeed.tech/tags/predictions.md>), [weather](<https://devfeed.tech/tags/weather.md>), [weather-forecasting](<https://devfeed.tech/tags/weather-forecasting.md>)

### AI overview

WeatherNext 2 generates hundreds of possible weather outcomes from one starting point, with each prediction taking under a minute on a single TPU. Its Functional Generative Network injects noise into the model so forecasts can represent interconnected weather patterns, though training uses individual weather variables. Google says it surpasses the previous WeatherNext model on 99.9% of variables and lead times from 0 to 15 days, with forecasts up to hourly resolution and eight times faster generation. Forecast data is available in Earth Engine and BigQuery, with early access to custom inference through Vertex AI. WeatherNext technology has also been added to Search, Gemini, Pixel Weather, and Google Maps Platform's Weather API; Google says it will power weather information in Google Maps in the coming weeks.

### Source excerpt

The new AI model delivers more efficient, more accurate and higher-resolution global weather predictions.

## Accelerating the magic cycle of research breakthroughs and real-world applications

DevFeed: [Accelerating the magic cycle of research breakthroughs and real-world applications](<https://devfeed.tech/articles/accelerating-the-magic-cycle-of-research-breakthroughs-and-real-world-applications-6745.md>)

Original publisher: [Read original article](<https://research.google/blog/accelerating-the-magic-cycle-of-research-breakthroughs-and-real-world-applications/>)

Published: 2025-10-31T07:40:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [data](<https://devfeed.tech/topics/data.md>), [dataset](<https://devfeed.tech/topics/dataset.md>)

Tags: [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [discovery](<https://devfeed.tech/tags/discovery.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [geospatial](<https://devfeed.tech/tags/geospatial.md>), [google](<https://devfeed.tech/tags/google.md>), [health-bioscience](<https://devfeed.tech/tags/health-bioscience.md>), [innovation](<https://devfeed.tech/tags/innovation.md>), [llms](<https://devfeed.tech/tags/llms.md>), [models](<https://devfeed.tech/tags/models.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [research](<https://devfeed.tech/tags/research.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>)

### AI overview

Google Research describes how advances in AI models, agentic tools, and open platforms are accelerating a cycle between scientific research and real-world applications. The article highlights Earth AI, including geospatial models and an LLM-powered reasoning agent that works across imagery, population, environmental data, and multiple datasets.

### Source excerpt

Climate & Sustainability

## Google Earth AI: Unlocking geospatial insights with foundation models and cross-modal reasoning

DevFeed: [Google Earth AI: Unlocking geospatial insights with foundation models and cross-modal reasoning](<https://devfeed.tech/articles/google-earth-ai-unlocking-geospatial-insights-with-foundation-models-and-cross-modal-reasoning-6797.md>)

Original publisher: [Read original article](<https://research.google/blog/google-earth-ai-unlocking-geospatial-insights-with-foundation-models-and-cross-modal-reasoning/>)

Published: 2025-10-23T08:08:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Earth AI](<https://devfeed.tech/topics/earth-ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [AI Foundation Models](<https://devfeed.tech/topics/ai-foundation-models.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [object-detection](<https://devfeed.tech/topics/object-detection.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [developers](<https://devfeed.tech/tags/developers.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [foundation-models](<https://devfeed.tech/tags/foundation-models.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [machine-perception](<https://devfeed.tech/tags/machine-perception.md>), [models](<https://devfeed.tech/tags/models.md>), [object-detection](<https://devfeed.tech/tags/object-detection.md>), [vision](<https://devfeed.tech/tags/vision.md>)

### AI overview

Google Research presents Google Earth AI, a family of geospatial AI models and reasoning agents that combines foundation models, Gemini-based orchestration, real-world data, datastores, and geospatial tools to answer complex planetary-scale questions. The article also introduces Remote Sensing Foundations models for satellite imagery analysis using vision-language models, open-vocabulary object detection, and adaptable vision backbones.

### Source excerpt

Climate & Sustainability