Sustainability & crisis resilience Skip to main content Explore our many areas of focus Explore all research areas Applied AI & sciences Earth AI Health AI Science AI Sustainability & crisis resilience Foundational ML & algorithms Algorithms & theory Information retrieval Machine intelligence Machine perception Natural language processing People, systems & quantum AI Human-computer interaction and visualization Networking Quantum AI Responsible AI Anti abuse Software engineering Software systems Learn More Publications Projects Building a collaborative ecosystem Datasets Access high-quality datasets to accelerate your research. Tools & services Explore our latest AI models and products. Open source Discover open-source code and collaborate with the community. Shaping the future together See all programs Faculty programs Participating in the academic research community through meaningful engagement with university faculty. Student programs Supporting the next generation of researchers through a wide range of programming. Locations Find your place in our global offices and research labs. Translating discovery into real-world impact People Our researchers drive advancements in computer science through both fundamental and applied research. Teams Collaborative groups tackling the world's most challenging AI problems. Research Explore our many areas of focus Explore all research areas Applied AI & sciences Earth AI Health AI Science AI Sustainability & crisis resilience Foundational ML & algorithms Algorithms & theory Information retrieval Machine intelligence Machine perception Natural language processing People, systems & quantum AI Human-computer interaction and visualization Networking Quantum AI Responsible AI Anti abuse Software engineering Software systems Learn More Publications Projects Resources Building a collaborative ecosystem Datasets Access high-quality datasets to accelerate your research. Tools & services Explore our latest AI models and products. Open source Discover open-source code and collaborate with the community. Conferences & events Careers Shaping the future together See all programs Faculty programs Participating in the academic research community through meaningful engagement with university faculty. Student programs Supporting the next generation of researchers through a wide range of programming. Locations Find your place in our global offices and research labs. Blog About Translating discovery into real-world impact People Our researchers drive advancements in computer science through both fundamental and applied research. Teams Collaborative groups tackling the world's most challenging AI problems. Google Research Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Research Resources Conferences & events Careers Blog About Search Sustainability & crisis resilience We’re re-imagining what’s possible with AI to create a more sustainable, resilient future for all. We’re re-imagining what’s possible with AI to create a more sustainable, resilient future for all. Building community resilience in times of crises Our north star is that no one should be surprised by a natural disaster. We lead cutting-edge AI research to predict and detect severe weather and natural disasters, in close partnership with the global scientific community. Our state-of-the-art models help governments and organizations respond to crises, and keep communities safe and informed. Many are also part of the Google Earth AI collection of models and datasets for planetary intelligence. Building a more sustainable future We’re harnessing the power of AI to accelerate global sustainability efforts, with innovations in areas from transportation to efficiency. By partnering with cities, governments and businesses, we transform our research breakthroughs into actionable initiatives. Flood forecasting Our state-of-the-art hydrologic models can forecast riverine floods up to 7 days in advance. Our flash floods model, trained on our new Groundsource dataset, helps to predict flash floods in urban areas up to 48 hours in advance. Learn more Wildfires We’re leveraging AI and satellite imagery to detect wildfire boundaries. We’re also working to launch more satellites for FireSat, a purpose-built satellite constellation to enable the detection of a garage-sized wildfire anywhere on Earth within 20 minutes. Learn more Weather Our weather models include MetNet for hyperprecise nowcasts and WeatherNext 2 for 15 day forecasts and cyclone predictions, in partnership with Google DeepMind. NeuralGCM is for longer range weather and climate predictions. These power a range of applications for users, developers and weather agencies. Learn more Open Buildings Our large-scale open dataset contains 1.8 billion building detections across Africa, South Asia, South-East Asia, Latin America and the Caribbean. Organizations are using the dataset for post-disaster humanitarian response, urban planning, population mapping and environmental science. Additionally, our new 2.5D Temporal dataset quantifies changes over time in the urban environment. Learn more Green Light Green Light uses AI and Google Maps driving trends to build intelligent recommendations for cities to optimize traffic flow. It operates in cities around the world, reducing vehicle emissions at intersections from Boston to Hamburg. Learn more Contrails Using satellite imagery and computer vision, we can predict when and where contrails — the heat-trapping condensation trails left behind by airplanes — are likely to form. Pilots from American Airlines and Eurocontrol are using these predictions to adjust flight altitudes in real time, helping mitigate aviation’s climate impact. Learn more Publications Neural general circulation models for modeling precipitation Stephan Hoyer Dmitrii Kochkov Janni Yuval Ian Langmore Science Advances (2026) Preview Preview abstract Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. While hybrid models combining machine learning and physics have emerged with the premise of improving precipitation simulations, none have proven sufficiently skillful or stable enough to outperform existing models in simulating precipitation. Here, we present the first hybrid model that is trained directly on precipitation observations. The model runs at 2.8 degrees resolution and is built on the differentiable NeuralGCM framework. This model is stable for decadal simulations and demonstrates significant improvements over existing GCMs, ERA5 reanalysis, and a Global Cloud-Resolving Model in simulating precipitation. Our approach yields reduced biases, a more realistic precipitation distribution, improved representation of extremes, and a more accurate diurnal cycle. Furthermore, it outperforms the ECMWF ensemble for mid-range weather forecasting. This advance paves the way for more reliable simulations of current climate and for the ability to fully utilize the abundance of existing observations to further improve GCMs. View details Study of Arterials in the City of Rio de Janeiro for Traffic Coordination Ori Rottenstreich Eliav Buchnik Avishai Zagoury Danny Veikherman Dan Karliner Tom Kalvari Shai Ferster Ron Tsibulsky Jack Haddad Dotan Emanuel Avinatan Hassidim 2025 Preview Preview abstract Urban traffic congestion is a growing challenge, and optimizing signal timing strategies is crucial for improving traffic flow and reducing emissions. The coordination of signalized intersections improves both traffic operations and environmental aspects. Coordination is particularly important along arterials, sequences of signalized intersections that serve as the primary routes and carry a high volume of traffic. In this paper we analyze real data from the city of Rio de Janeiro to study properties of arterials. We refer to their length, the distance between intersections and to the properties of the traffic light plans such as cycle time. We then study their in practice level of coordination in terms of number of stops and their common locations along the arterials. We dive into particular arterials and provide insights that can be useful for efficient design of arterials in additional cities. Based on the analysis, we show how simple traffic properties can indicate the potential upon coordinating two adjacent intersections as part of an arterial in improving traffic performance. View details Day-of-the-week Awareness in Time of Day Breakpoints for Traffic Light Plans Eliav Buchnik Ori Rottenstreich 2025 Preview Preview abstract Time-of-day breakpoints (TODs) refer to the times over the day in which the plan of a traffic light is changed. Traditionally, TODs are selected jointly for all weekdays (Monday-Friday), typically with additional TODs dedicated to weekends. In this paper, we present an alternative approach motivated by traffic characteristics that can differ among the weekdays Monday-Friday and consider TODs which are day-of-the-week aware. The traffic-aware approach studies similarities among days and computes TODs that can be shared among days with similar characteristics but can also have other forms for weekdays with unique characteristics. Based on traffic properties derived from anonymized trajectories, we apply the new methodology to compute time-of-day breakpoints that are day-of-the-week aware in the city of Rio de Janeiro, Brazil and estimate the impact of the new methodology. View details Enhancing Column Average CO2 tracking with GOES East Aarón Sonabend Vishal Batchu Carl Elkin Christopher Van Arsdale John Platt Anna Michalak (2025) Preview Preview abstract The need for characterizing global variability of atmospheric carbon dioxide (CO2) is quickly increasing, with a growing urgency for tracking greenhouse gasses with sufficient resolution, precision and accuracy so as to support independent verification of CO2 fluxes at local to global scales. The current generation of space-based sensors, however, can only provide sparse observations in space and/or in time, by design. While upcoming missions may address some of these challenges, most are still years away from launch. This challenge has fueled interest in the potential use of data from existing missions originally developed for other applications for inferring global greenhouse gas variability. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-East), operational since 2017, provides full coverage of much of the western hemisphere at 10-minute intervals from geostationary orbit at 16 wavelengths. We leverage this high temporal resolution by developing a single-pixel, fully-connected neural network to estimate dry-air column CO2 mole fractions (XCO2). The model employs a time series of GOES-East's 16 spectral bands, which aids in disentangling atmospheric CO2 from surface reflectance, alongside ECMWF ERA5 lower tropospheric meteorology, solar angles, and day of year. Training used collocated GOES-East and OCO-2/OCO-3 observations (2017-2020, within 5 km and 10 minutes), with validation and testing performed on 2021 data. The model successfully captures monthly latitudinal XCO2 gradients and shows reasonable agreement with ground-based TCCON measurements. Furthermore, we demonstrate the model's ability to detect elevated XCO2 signals from high-emitting power plants, particularly over low-reflectance surfaces. We also confirm that removing bands 5 (1.6 µm) and 16 (13.3 µm) substantially decreases performance, indicating that the model is able to extract useful information from these bands. Although GOES-East derived XCO2 precision may not rival dedicated instruments, its unprecedented combination of contiguous geographic coverage, 10-minute temporal frequency, and multi-year record offers the potential to observe aspects of atmospheric CO2 variability currently unseen from space, with further potential through spatio-temporal aggregation. View details Heterogeneous graph neural networks for species distribution modeling Lauren Harrell Christine Kaeser-Chen Burcu Karagol Ayan Keith Anderson Michelangelo Conserva Elise Kleeman Maxim Neumann Matt Overlan Millie Chapman Drew Purves arxiv (2025) Preview Preview abstract Species distribution models (SDMs) are necessary for measuring and predicting occurrences and habitat suitability of species and their relationship with environmental factors. We introduce a novel presence-only SDM with graph neural networks (GNN). In our model, species and locations are treated as two distinct node sets, and the learning task is predicting detection records as the edges that connect locations to species. Using GNN for SDM allows us to model fine-grained interactions between species and the environment. We evaluate the potential of this methodology on the six-region dataset compiled by National Center for Ecological Analysis and Synthesis (NCEAS) for benchmarking SDMs. For each of the regions, the heterogeneous GNN model is comparable to or outperforms previously-benchmarked single-species SDMs as well as a feed-forward neural network baseline model. View details Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model Gan Zhang Megha Rao Janni Yuval Ming Zhao Environmental Research Letters (2025) Preview Preview abstract Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using neural general circulation model (NeuralGCM), a hybrid ML-physics atmospheric model developed by Google, for seasonal predictions of large-scale atmospheric variability and Northern Hemisphere tropical cyclone (TC) activity. Inspired by physical model studies, we simplify boundary conditions, assuming sea surface temperature and sea ice follow their climatological cycle but persist anomalies present at the initialization time. With such forcings, NeuralGCM can generate 100 simulation days in ∼8 min with a single graphics processing unit while simulating realistic atmospheric circulation and TC climatology patterns. This configuration yields useful seasonal predictions (July–November) for the tropical atmosphere and various TC activity metrics. Notably, the predicted and observed TC frequency in the North Atlantic and East Pacific basins are significantly correlated during 1990–2023 (r = ∼0.7), suggesting prediction skill comparable to existing physical GCMs. Despite challenges associated with model resolution and simplified boundary forcings, the model-predicted interannual variations demonstrate significant correlations with the observed sub-basin TC tracks (p View details Crisis resilience at Google We’re making critical information accessible via Google products and partnerships to help communities stay safe and informed. Go to website The big ambition Explore our Google-wide mission to ensure that no one, anywhere, is surprised by a natural disaster. Go to website Follow us Explore our other initiatives Google AI Discover how Google AI is committed to enriching knowledge and solving complex challenges Products Build Research Responsibility Societal Impact About Google Cloud High-performance infrastructure for cloud computing, data analytics & machine learning Overview Solutions Products Pricing Resources Google DeepMind Our mission is to build AI responsibly to benefit humanity Models Research Science About Google Labs Explore the future of AI responsibly with Google Labs About Experiments Stay connected About Google Google Products Privacy Terms Cookies management controls ×