Expanding our Heat Resilience data to 50+ global cities

Approximately 500,000 deaths every year are attributed to extreme heat, a crisis intensified by the urban heat island effect, which causes metropolitan areas to warm at double the worldwide average. Earlier this month, record-breaking heat waves across Western Europe pushed temperatures past 40°C (104°F). The prevalence of heat-trapping materials, like dark pavements and roofs, combined with a lack of vegetation, largely drives this localized warming. Heat mitigation measures are critical to reducing this toll, and cool roofs offer a highly cost-effective solution. By increasing rooftop reflectivity (albedo), we can significantly reduce the amount of solar energy absorbed by buildings, ultimately lowering local surface temperatures and protecting vulnerable communities.

To address this, Google Research is building AI-driven tools to help lower city temperatures and keep communities safe. By applying AI to high-resolution satellite and aerial imagery, our Heat Resilience tools help cities quantify the impact of targeted cooling interventions. In 2024, we piloted this approach with 14 cities, providing them with rooftop reflectivity data to identify highly vulnerable neighborhoods and determine where cool roofs would yield the greatest temperature reductions. This data guided critical decisions across several cities, resulting in initiatives such as cool roof ordinances and adaptation plans.

Now, we are scaling this impact. In "Estimating high-resolution albedo for urban applications", published in Nature Communications, we detail our methodology for mapping building-level reflectivity across diverse urban environments. This research bridges the gap between general climate observations and actionable, building-level data. We are also releasing an expanded albedo dataset covering over 50 global cities to empower urban planners worldwide to prioritize cool-roof interventions. This dataset is open and accessible through our new, high-resolution Heat Resilience Earth Engine App.

As part of our Google Earth AI collection of geospatial models and datasets to transform planetary information into actionable intelligence, we developed a novel method that fuses Sentinel-2 satellite data with high-resolution (30-cm) satellite imagery (Airbus Pléiades Neo). This highly granular dataset moves beyond neighborhood averages to provide actionable, building-level insights. Importantly, our modeling demonstrates that targeted cool-roof planning using this data could mitigate extreme urban heat by up to 0.5°C (1.8°F) globally, offering a highly effective path forward for city planners.

While satellite-based albedo estimates derived from Sentinel-2 are freely available globally, their 10-meter spatial resolution is insufficient to resolve individual rooftops. To overcome this limitation, our approach uses machine learning models and radiometric calibration techniques to blend the radiometric accuracy and global coverage of Sentinel-2 with the precise spatial detail of commercial imagery. By blending data captured across different wavelengths, we can reconstruct a comprehensive spectral reflectance profile for each urban pixel.

To ensure accuracy, we validated our method against high-resolution airborne hyperspectral measurements collected over Boulder, Colorado. The fused 30-cm albedo maps demonstrated high precision, achieving a root mean square error (RMSE) of just 0.04 relative to the ground-truth data. This breakthrough in granularity enables city planners to move beyond neighborhood-level averages and accurately prioritize individual, large-footprint buildings for targeted cool roof retrofits.

To make this data accessible to decision-makers, we have launched a Heat Resilience Earth Engine App. This platform provides high-resolution rooftop albedo (reflectivity) data to empower proactive municipal planning.

The app features:

• Building-level visualization: Albedo data displayed as centroids to identify low-reflectivity surfaces.
• Baseline analysis: Tools to help cities understand current reflectivity and monitor changes over time.
• Data portability: Functionality to download high-resolution data for local analysis and policy development.
• Dynamic zoom: A nested interface that transitions from census-tract aggregates to individual building insights.

This release expands our data to 50+ cities across 9 countries. New coverage includes major urban centers in Europe (including London, Athens, Barcelona), Brazil (including Rio de Janeiro and São Paulo), and the United States (including Los Angeles, Austin, and New York City). By providing open access to this building-level albedo data, we aim to help cities accelerate the adoption of reflective surfaces to lower urban surface temperatures.

The Heat Resilience Earth Engine App is now live and available for public use. You can explore the interactive data to visualize rooftop albedo across all 50+ cities included in this release.

For detailed technical documentation and to download the high-resolution datasets for your own analysis, please visit the Heat Resilience site.

This research was developed by Google Research in collaboration with the World Resource Institute (WRI).

We thank our collaborators at Google and WRI: Elizabeth J. Wesley (WRI), Salil Banerjee, Vishal Batchu, Aniruddh Chennapragada, Kevin Crossan, Bryce Cronkite-Ratcliff, Ellie Delich, Tristan Goulden (National Ecological Observatory Network), Mansi Kansal, Jonas Kemp, Eric Mackres (WRI), Yael Mayer, Rebecca Milman, John C. Platt, Shruthi Prabhakara, Gautam Prasad, Aaron Bell, Shravya Shetty, Charlotte Stanton, Wayne Sun, and Lucy R. Hutyra.