Kamath Harsh G, Singh Manmeet, Malviya Neetiraj, Martilli Alberto, He Liu, Aliaga Daniel, He Cenlin, Chen Fei, Magruder Lori A, Yang Zong-Liang, Niyogi Dev
Department of Earth and Planetary Sciences, Jackson School of Geosciences, University of Texas at Austin, Austin, Texas, USA.
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
Sci Data. 2024 Aug 15;11(1):886. doi: 10.1038/s41597-024-03719-w.
We introduce University of Texas - GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for more than 1200 city or locales worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse-resolution urban canopy elevation data with a machine-learning model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had a root mean squared error (RMSE) of 9.1 meters. Validation of mean building heights within 1-km grid cells, including data from Hamburg and Sydney, resulted in an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset's effectiveness in modeling human thermal comfort in Baltimore, MD (daytime RMSE = 2.85°C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and biometeorological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.
我们引入了德克萨斯大学全球城市研究建筑高度数据集（UT - GLOBUS），该数据集提供了全球1200多个城市或地区的建筑高度和城市冠层参数（UCP）。UT - GLOBUS将开源星载测高数据（ICESat - 2和GEDI）以及粗分辨率城市冠层高程数据与机器学习模型相结合，以估算建筑层面的信息。使用来自美国六个城市的激光雷达数据进行验证表明，UT - GLOBUS得出的建筑高度的均方根误差（RMSE）为9.1米。对1公里网格单元内的平均建筑高度进行验证（包括来自汉堡和悉尼的数据），得出的RMSE为7.8米。在城市气象研究与预报（WRF - Urban）模型中对UCP进行测试，结果表明与德克萨斯州休斯顿现有的基于表格的局部气候区方法相比，城市内部气温表示有显著改善（RMSE降低55%）。此外，我们展示了该数据集在使用WRF - Urban模拟热缓解策略和建筑能耗方面的效用，以伊利诺伊州芝加哥市和德克萨斯州奥斯汀市为例进行了测试。使用太阳和长波环境辐照度几何（SOLWEIG）模型进行街道尺度平均辐射温度模拟，结合UT - GLOBUS和激光雷达得出的建筑高度，证实了该数据集在模拟马里兰州巴尔的摩市人类热舒适度方面的有效性（白天RMSE = 2.85°C）。因此，UT - GLOBUS可用于对具有重大社会经济和生物气象风险的城市灾害进行建模，实现更精细尺度的城市气候模拟，并克服以往因缺乏建筑信息而存在的局限性。
