google·Jun 30, 2026·3 min readCurated summary

Expanding our Heat Resilience data to 50+ global cities

Google Research has expanded its building-level rooftop reflectivity dataset from 14 pilot cities to more than 50 cities across nine countries. By combining Sentinel-2 satellite data with 30-centimeter commercial imagery and machine learning, the project identifies roofs where reflective “cool roof” interventions could reduce urban heat. The data is publicly available through a new Google Earth Engine app for planners and researchers. ## Why Rooftop Reflectivity Matters - Extreme heat contributes to approximately 500,000 deaths annually. - Urban heat islands make cities warm faster than the global average. - Dark roofs, pavements, and limited vegetation increase heat absorption. - Reflective roofs reduce the solar energy absorbed by buildings and can lower local surface temperatures. - Earlier pilot data from 14 cities supported cool-roof ordinances and broader climate adaptation plans. ## Building-Level Albedo Mapping - Sentinel-2 provides global albedo data but at 10-meter resolution, which cannot reliably distinguish individual rooftops. - Google Research fused Sentinel-2’s spectral and global coverage with 30-centimeter Airbus Pléiades Neo imagery. - Machine learning and radiometric calibration reconstruct detailed reflectance profiles at the urban-pixel level. - Validation against airborne hyperspectral measurements in Boulder, Colorado, produced an RMSE of 0.04. - The resulting maps allow planners to prioritize large, low-reflectivity buildings for cool-roof retrofits. - The modeling suggests targeted interventions could reduce extreme urban heat by up to 0.5°C globally. ## Heat Resilience Earth Engine App - Displays building-level albedo using rooftop centroids to highlight low-reflectivity surfaces. - Provides baseline analyses and supports monitoring changes over time. - Allows users to download high-resolution data for local studies and policy development. - Offers a nested view that moves from census-tract summaries to individual buildings. ## Expanded Global Coverage - The dataset now covers more than 50 cities in nine countries. - Newly included urban areas span Europe, Brazil, and the United States. - Examples include London, Athens, Barcelona, Rio de Janeiro, São Paulo, Los Angeles, Austin, and New York City. - The open dataset is intended to help municipalities accelerate reflective-surface programs. ## Access and Collaboration - The interactive app and datasets are publicly available through Google’s Heat Resilience site. - The methodology is described in the Nature Communications paper “Estimating high-resolution albedo for urban applications.” - The work was developed by Google Research in collaboration with the World Resources Institute. Cities can use the app to identify the buildings and neighborhoods where cool-roof investments are likely to have the greatest heat-reduction benefits.

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Satellite ImageryMachine LearningGeospatial DataEarth Engine+3

google·Nov 4, 2025Original article

Forecasting the future of forests with AI: From counting losses to predicting risk (opens in new tab)

Research from Google DeepMind and Google Research introduces ForestCast, a deep learning-based framework designed to transition forest management from retrospective loss monitoring to proactive risk forecasting. By utilizing vision transformers and pure satellite data, the team has developed a scalable method to predict future deforestation that matches or exceeds the accuracy of traditional models dependent on inconsistent manual inputs. This approach provides a repeatable, future-proof benchmark for protecting biodiversity and mitigating climate change on a global scale. ### Limitations of Traditional Forecasting * Existing state-of-the-art models rely on specialized geospatial maps, such as infrastructure development, road networks, and regional economic indicators. * These traditional inputs are often "patchy" and inconsistent across different countries, requiring manual assembly that is difficult to replicate globally. * Manual data sources are not future-proof; they tend to go out of date quickly with no guarantee of regular updates, unlike continuous satellite streams. ### A Scalable Pure-Satellite Architecture * The ForestCast model adopts a "pure satellite" approach, using only raw inputs from Landsat and Sentinel-2 satellites. * The architecture is built on vision transformers (ViTs) that process an entire tile of pixels in a single pass to capture critical spatial context and landscape-level trends. * The model incorporates a satellite-derived "change history" layer, which identifies previously deforested pixels and the specific year the loss occurred. * By avoiding socio-political or infrastructure maps, the method can be applied consistently to any region on Earth, allowing for meaningful cross-regional comparisons. ### Key Findings and Benchmark Release * Research indicates that "change history" is the most information-dense input; a model trained on this data alone performs almost as well as those using raw multi-spectral data. * The model successfully predicts tile-to-tile variation in deforestation amounts and identifies the specific pixels most likely to be cleared next. * Google has released the training and evaluation data as a public benchmark dataset, focusing initially on Southeast Asia to allow the machine learning community to verify and improve upon the results. The release of ForestCast provides a template for scaling predictive modeling to Latin America, Africa, and boreal latitudes. Conservationists and policymakers should utilize these forecasting tools to move beyond counting historical losses and instead direct resources toward "frontline" areas where the model identifies imminent risk of habitat conversion.

Satellite ImageryAIMachine LearningDeep Learning+4