# Climate & Sustainability

Published articles for Climate & Sustainability.

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

## Mapping global methane emissions from space with deep learning

DevFeed: [Mapping global methane emissions from space with deep learning](<https://devfeed.tech/articles/mapping-global-methane-emissions-from-space-with-deep-learning-6833.md>)

Original publisher: [Read original article](<https://research.google/blog/mapping-global-methane-emissions-from-space-with-deep-learning/>)

Published: 2026-09-01T18:40:00Z

Content type: article

Language: en

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

Topics: [Deep neural networks](<https://devfeed.tech/topics/deep-neural-networks.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [earth-ai](<https://devfeed.tech/tags/earth-ai.md>), [framework](<https://devfeed.tech/tags/framework.md>), [global](<https://devfeed.tech/tags/global.md>), [google](<https://devfeed.tech/tags/google.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [mapping](<https://devfeed.tech/tags/mapping.md>), [nasa](<https://devfeed.tech/tags/nasa.md>), [research](<https://devfeed.tech/tags/research.md>), [space](<https://devfeed.tech/tags/space.md>)

### AI overview

The article presents MAPL-EMIT, a deep-learning framework for automating global detection, enhancement prediction, and source estimation of methane plumes from EMIT hyperspectral satellite measurements.

### Source excerpt

Climate & Sustainability

## The power of collaboration: How we can reduce traffic congestion

DevFeed: [The power of collaboration: How we can reduce traffic congestion](<https://devfeed.tech/articles/the-power-of-collaboration-how-we-can-reduce-traffic-congestion-6894.md>)

Original publisher: [Read original article](<https://research.google/blog/the-power-of-collaboration-how-we-can-reduce-traffic-congestion/>)

Published: 2026-07-07T16:42:08Z

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>), [Algorithms, Complexity](<https://devfeed.tech/topics/algorithms-complexity.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Routing (disambiguation)](<https://devfeed.tech/topics/routing.md>), [App](<https://devfeed.tech/topics/app.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [collaboration](<https://devfeed.tech/tags/collaboration.md>), [data-mining-modeling](<https://devfeed.tech/tags/data-mining-modeling.md>), [driving](<https://devfeed.tech/tags/driving.md>), [google](<https://devfeed.tech/tags/google.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [reduce](<https://devfeed.tech/tags/reduce.md>), [routing](<https://devfeed.tech/tags/routing.md>), [transportation](<https://devfeed.tech/tags/transportation.md>), [us](<https://devfeed.tech/tags/us.md>)

### AI overview

Google Research describes a large-scale routing experiment in 10 major US cities. By guiding a small fraction of trips toward alternative routes, the study reports improved overall traffic conditions, faster driving speeds, and reduced emissions.

### 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>), [datasets](<https://devfeed.tech/topics/datasets.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

## A low-carbon computing platform from your retired phones

DevFeed: [A low-carbon computing platform from your retired phones](<https://devfeed.tech/articles/a-low-carbon-computing-platform-from-your-retired-phones-6739.md>)

Original publisher: [Read original article](<https://research.google/blog/a-low-carbon-computing-platform-from-your-retired-phones/>)

Published: 2026-06-12T17:37:00Z

Content type: article

Language: en

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

Topics: [Green Software](<https://devfeed.tech/topics/green-software.md>), [cloud-computing](<https://devfeed.tech/topics/cloud-computing.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cloud-computing](<https://devfeed.tech/tags/cloud-computing.md>), [compute](<https://devfeed.tech/tags/compute.md>), [datacenter](<https://devfeed.tech/tags/datacenter.md>), [distributed-systems-parallel-computing](<https://devfeed.tech/tags/distributed-systems-parallel-computing.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [energy](<https://devfeed.tech/tags/energy.md>), [energy-efficiency](<https://devfeed.tech/tags/energy-efficiency.md>), [google](<https://devfeed.tech/tags/google.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [memory](<https://devfeed.tech/tags/memory.md>), [performance](<https://devfeed.tech/tags/performance.md>), [servers](<https://devfeed.tech/tags/servers.md>), [smartphones](<https://devfeed.tech/tags/smartphones.md>), [storage](<https://devfeed.tech/tags/storage.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>)

### AI overview

Researchers at the University of California San Diego, with Google's support, are developing a low-carbon cloud computing platform from retired smartphones. A planned datacenter using 2,000 Pixel smartphones aims to provide low-cost computing while reducing the need for newly manufactured hardware and its associated emissions.

### 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

## 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 & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.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

## Introducing Groundsource: Turning news reports into data with Gemini

DevFeed: [Introducing Groundsource: Turning news reports into data with Gemini](<https://devfeed.tech/articles/introducing-groundsource-turning-news-reports-into-data-with-gemini-6825.md>)

Original publisher: [Read original article](<https://research.google/blog/introducing-groundsource-turning-news-reports-into-data-with-gemini/>)

Published: 2026-03-12T10:42:00Z

Content type: article

Language: en

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

Topics: [datasets](<https://devfeed.tech/topics/datasets.md>), [Ground truth / benchmark quality](<https://devfeed.tech/topics/ground-truth-benchmark-quality.md>), [data](<https://devfeed.tech/topics/data.md>), [Resilience](<https://devfeed.tech/topics/resilience.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [flash](<https://devfeed.tech/tags/flash.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.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 introduces Groundsource, a scalable methodology that uses Gemini to convert unstructured global news reports into verified historical data. Its first open-access dataset contains 2.6 million flash-flood records from more than 150 countries, supporting research, forecasting, and urban crisis resilience.

### 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

## How AI trained on birds is surfacing underwater mysteries

DevFeed: [How AI trained on birds is surfacing underwater mysteries](<https://devfeed.tech/articles/how-ai-trained-on-birds-is-surfacing-underwater-mysteries-6812.md>)

Original publisher: [Read original article](<https://research.google/blog/how-ai-trained-on-birds-is-surfacing-underwater-mysteries/>)

Published: 2026-02-09T18:38:00Z

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>), [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Google](<https://devfeed.tech/topics/google.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>)

Tags: [agile](<https://devfeed.tech/tags/agile.md>), [ai](<https://devfeed.tech/tags/ai.md>), [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [deep-dive](<https://devfeed.tech/tags/deep-dive.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [google](<https://devfeed.tech/tags/google.md>), [google-cloud](<https://devfeed.tech/tags/google-cloud.md>), [models](<https://devfeed.tech/tags/models.md>), [open-source-models-datasets](<https://devfeed.tech/tags/open-source-models-datasets.md>), [research](<https://devfeed.tech/tags/research.md>), [sound-accoustics](<https://devfeed.tech/tags/sound-accoustics.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

### AI overview

Google Research describes how Perch 2.0, a bioacoustics foundation model trained primarily on birds and other terrestrial animals, transfers effectively to underwater audio tasks. The article highlights whale-vocalization classification, marine-ecosystem research, and a Google Colab tutorial using NOAA acoustic data through Google Cloud.

### Source excerpt

Climate & Sustainability

## 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: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.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

## Separating natural forests from other tree cover with AI for deforestation-free supply chains

DevFeed: [Separating natural forests from other tree cover with AI for deforestation-free supply chains](<https://devfeed.tech/articles/separating-natural-forests-from-other-tree-cover-with-ai-for-deforestation-free-supply-chains-6870.md>)

Original publisher: [Read original article](<https://research.google/blog/separating-natural-forests-from-other-tree-cover-with-ai-for-deforestation-free-supply-chains/>)

Published: 2025-11-13T19:04:00Z

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>), [Google](<https://devfeed.tech/topics/google.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [data](<https://devfeed.tech/topics/data.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.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>), [global](<https://devfeed.tech/tags/global.md>), [google](<https://devfeed.tech/tags/google.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [open-source-models-datasets](<https://devfeed.tech/tags/open-source-models-datasets.md>), [research](<https://devfeed.tech/tags/research.md>), [responsible-ai](<https://devfeed.tech/tags/responsible-ai.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>)

### AI overview

Google Research and Google DeepMind are releasing Natural Forests of the World 2020, an AI-powered map and dataset that distinguishes natural forests from other tree cover. The globally consistent 10-meter-resolution map is intended to support deforestation and degradation monitoring, supply-chain due diligence, and compliance with deforestation-free goals, including the European Union's regulation on deforestation-free products.

### Source excerpt

Climate & Sustainability

## Forecasting the future of forests with AI: From counting losses to predicting risk

DevFeed: [Forecasting the future of forests with AI: From counting losses to predicting risk](<https://devfeed.tech/articles/forecasting-the-future-of-forests-with-ai-from-counting-losses-to-predicting-risk-6777.md>)

Original publisher: [Read original article](<https://research.google/blog/forecasting-the-future-of-forests-with-ai-from-counting-losses-to-predicting-risk/>)

Published: 2025-11-05T15:41:00Z

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>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Google](<https://devfeed.tech/topics/google.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [data](<https://devfeed.tech/topics/data.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [benchmark](<https://devfeed.tech/tags/benchmark.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>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [global](<https://devfeed.tech/tags/global.md>), [google](<https://devfeed.tech/tags/google.md>), [models](<https://devfeed.tech/tags/models.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [open-source-models-datasets](<https://devfeed.tech/tags/open-source-models-datasets.md>), [research](<https://devfeed.tech/tags/research.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>)

### AI overview

Google Research and Google DeepMind introduce ForestCast, a deep learning-powered benchmark and public dataset for forecasting deforestation risk. The approach uses satellite data to predict future risk consistently across regions, addressing limitations of backward-looking forest-loss monitoring and patchy, outdated input maps. The released input, training, and evaluation data are intended to support reproducibility and further research.

### Source excerpt

Climate & Sustainability

## 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>), [LLMs](<https://devfeed.tech/topics/llms.md>), [data](<https://devfeed.tech/topics/data.md>), [datasets](<https://devfeed.tech/topics/datasets.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>), [foundation-models](<https://devfeed.tech/topics/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