Institute of Automotive Technology, Department of Mechanical Engineering, TUM School of Engineering and Design, Technical University of Munich, Boltzmannstr. 15, 85748, Garching b. München, Germany.
Department of Civil and Environmental Engineering, Stanford University, 473 Via Ortega, 94305, Stanford, USA.
Sci Data. 2023 Sep 20;10(1):639. doi: 10.1038/s41597-023-02544-x.
Rapid renovation of Europe's inefficient buildings is required to reduce climate change. However, evaluating buildings at scale is challenging because every building is unique. In current practice, the energy performance of buildings is assessed during on-site visits, which are slow, costly, and local. This paper presents a building point cloud dataset that promotes a data-driven, large-scale understanding of the 3D representation of buildings and their energy characteristics. We generate building point clouds by intersecting building footprints with geo-referenced LiDAR data and link them with attributes from UK's energy performance database via the Unique Property Reference Number (UPRN). To mimic England's building stock's features well, we select one million buildings from a range of rural and urban regions, of which half a million are linked to energy characteristics. Building point clouds in new regions can be generated with our published open-source code. The dataset enables novel research in building energy modeling and can be easily expanded to other research fields by adding building features via the UPRN or geo-location.
为了减少气候变化，欧洲急需对低效建筑进行快速改造。然而，由于每栋建筑都是独一无二的，因此对其进行大规模评估具有挑战性。在当前的实践中，建筑的能源性能是通过现场访问来评估的，这种方法既缓慢又昂贵，而且具有地域性。本文提出了一个建筑点云数据集，旨在通过数据驱动的方式，从大规模角度理解建筑物的 3D 表示及其能源特征。我们通过将建筑物的轮廓与地理参考的 LiDAR 数据进行交叉，生成建筑物点云，并通过独特属性参考号（Unique Property Reference Number，UPRN）将其与英国能源绩效数据库的属性联系起来。为了很好地模拟英格兰的建筑存量特征，我们从各种农村和城市地区中选择了一百万栋建筑物，其中一半与能源特征相关联。新地区的建筑物点云可以使用我们发布的开源代码生成。该数据集为建筑物能源建模的研究提供了新的思路，并且可以通过 UPRN 或地理位置添加建筑物特征轻松扩展到其他研究领域。
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