内容来源：https://research.google/blog/expanding-our-heat-resilience-data-to-50-global-cities/

内容总结：

谷歌发布全球50余城市屋顶反照率数据集，助力城市热浪缓解

谷歌研究院于2026年6月30日宣布，将面向全球50多个主要城市发布一份高分辨率建筑屋顶反照率（反射率）数据集，旨在帮助城市规划者实施“清凉屋顶”解决方案，以应对极端高温天气。

据谷歌介绍，每年约有50万例死亡与极端高温直接相关，而城市热岛效应正使大都市区的升温速度达到全球平均水平的两倍。今年6月初，西欧多国遭遇创纪录热浪，气温突破40摄氏度。吸热材料（如深色路面和屋顶）的广泛使用以及植被的缺乏，是导致局部升温加剧的主要推手。研究表明，提升屋顶反照率是极具成本效益的降温手段，能有效减少建筑吸收的太阳能，降低地表温度并保护脆弱群体。

为破解这一难题，谷歌研究院利用AI技术，结合高分辨率卫星与航空影像，开发了“热韧性”工具。2024年，该工具已率先在14个试点城市落地，通过提供屋顶反照率数据，帮助当地识别高风险社区并规划降温干预措施，部分城市据此出台了“清凉屋顶”法规及适应性计划。

此次发布的研究成果发表于《自然·通讯》期刊。谷歌团队创新性地融合了哨兵2号（Sentinel-2）卫星数据与30厘米分辨率的航空影像（空客Pléiades Neo），并借助机器学习与辐射定标技术，构建出全球首个建筑级别的反照率映射模型。该模型通过美国科罗拉多州博尔德市的机载高光谱数据验证，均方根误差仅为0.04，精度显著优于传统10米空间分辨率的数据。模拟显示，基于该数据的精准屋顶降温规划，有望使全球极端城市热浪温度降低0.5摄氏度。

目前，涵盖伦敦、雅典、巴塞罗那、里约热内卢、圣保罗、洛杉矶、奥斯汀、纽约等欧洲、巴西及美国主要城市的数据集，已通过全新上线的“热韧性地球引擎应用”向公众免费开放。用户可交互式浏览50余座城市的屋顶反照率分布，并下载高清数据集用于本地分析。

这项研究由谷歌研究院与世界资源研究所（WRI）共同完成，其合作团队包括多位来自两家机构的科学家。谷歌表示，希望借助开放数据，加速全球城市推广反光表面技术，从根源上降低城市地表温度。

中文翻译：

2026年6月30日，Google研究团队研究员David Fork与产品经理Jules Kuperminc联合发布：我们现推出覆盖全球50多个城市的建筑屋顶反射率扩展数据集，旨在帮助城市规划者实施"清凉屋顶"方案，缓解极端高温问题。该数据集可通过全新高分辨率"热韧性地球引擎应用"获取。

每年约有50万人死于极端高温，而城市热岛效应使大都市地区的升温速度达到全球平均水平的两倍，进一步加剧了这一危机。本月早些时候，破纪录的热浪席卷西欧，气温突破40摄氏度。深色路面与屋顶等吸热材料的广泛使用，加之植被匮乏，是导致局部升温的主因。实施热缓解措施对减少伤亡至关重要，而清凉屋顶正是一种高性价比解决方案。通过提升屋顶反射率（反照率），可显著降低建筑吸收的太阳能量，最终降低地表温度，保护脆弱社区。

为此，Google研究团队正开发AI驱动工具，以降低城市温度、保障社区安全。通过将人工智能应用于高分辨率卫星与航拍影像，我们的热韧性工具能帮助城市量化针对性降温干预措施的效果。2024年，我们与14个城市试点合作，为其提供屋顶反射率数据，识别高脆弱性社区并确定清凉屋顶能实现最大降温效果的区域。这些数据引导多座城市做出关键决策，催生了清凉屋顶条例与适应计划等举措。

如今，我们正扩大这一影响力。在《自然·通讯》发表的《面向城市应用的高分辨率反照率估算》论文中，我们详细阐述了绘制多样化城市环境中建筑级反射率的方法。这项研究弥合了宏观气候观测与可操作的建筑级数据之间的鸿沟。同时，我们发布覆盖全球50余城市的扩展反照率数据集，助力世界各地的城市规划者优先部署清凉屋顶项目。该数据集通过全新高分辨率"热韧性地球引擎应用"实现开放获取。

作为Google Earth AI地理空间模型与数据集系列（致力于将行星信息转化为可执行洞察）的一部分，我们开发了融合哨兵二号卫星数据与高分辨率（30厘米）卫星影像（空客Pléiades Neo卫星）的新方法。这一高粒度数据集超越社区平均值，提供可操作的建筑级洞察。重要的是，我们的模型表明，利用该数据进行针对性清凉屋顶规划，可在全球范围内将极端城市高温降低最多0.5摄氏度，为城市规划者提供了高效的前进路径。

尽管基于哨兵二号卫星的反照率估算在全球免费开放，但其10米空间分辨率不足以解析单个屋顶。为突破这一限制，我们的方法采用机器学习模型与辐射定标技术，融合哨兵二号卫星的辐射精度与全球覆盖能力，以及商业影像的精确空间细节。通过混合不同波长采集的数据，我们能为每个城市像素重建完整的光谱反射率剖面。

为确保精度，我们利用美国科罗拉多州博尔德市采集的高分辨率机载高光谱测量数据验证方法。融合后的30厘米反照率地图展现出高精度，与真实数据的均方根误差仅为0.04。这一粒度的突破使城市规划者能超越社区平均水平，准确优先对大型单体建筑实施针对性清凉屋顶改造。

为让决策者便捷获取数据，我们推出热韧性地球引擎应用。该平台提供高分辨率屋顶反照率数据，助力前瞻性市政规划。

应用特色包括：
本次发布将数据扩展至9个国家50余座城市。新增覆盖区域包括欧洲主要城市（伦敦、雅典、巴塞罗那）、巴西（里约热内卢、圣保罗）及美国（洛杉矶、奥斯汀、纽约）。通过开放获取这些建筑级反照率数据，我们希望帮助城市加速采用反射表面，降低地表温度。

热韧性地球引擎应用现已上线并向公众开放。您可探索交互式数据，可视化本次发布的50余座城市的屋顶反照率。

如需获取详细技术文档及下载高分辨率数据集用于自主分析，请访问热韧性网站。

本研究由Google研究团队与世界资源研究所合作完成。

我们感谢Google与WRI的合作伙伴：Elizabeth J. Wesley（WRI）、Salil Banerjee、Vishal Batchu、Aniruddh Chennapragada、Kevin Crossan、Bryce Cronkite-Ratcliff、Ellie Delich、Tristan Goulden（国家生态观测网络）、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及Lucy R. Hutyra。

英文来源：

June 30, 2026
David Fork, Staff Research Scientist, and Jules Kuperminc, Product Manager, Google Research
We’re releasing an expanded dataset of building-level rooftop reflectivity covering 50+ global cities to help urban planners implement cool-roof solutions and mitigate extreme heat. This dataset is being made accessible through a new high-resolution Heat Resilience Earth Engine App.
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:
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.