Rapid renovation of Europe's inefficient buildings is required to reduce climate change. However, analyzing and 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 achieve a representative sample, we select one million buildings from a range of rural and urban regions across England, of which half a million are linked to energy characteristics. Building point clouds in new regions can be generated with the open-source code published alongside the paper. 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.

翻译：为应对气候变化，亟需对欧洲低能效建筑进行快速改造。然而，建筑单体具有独特性，使得大规模建筑分析与评估面临挑战。现行实践中，建筑能效评估依赖现场勘查，存在效率低、成本高且受限于局地的问题。本文提出一种建筑点云数据集，旨在推动基于数据驱动的建筑三维表征及其能耗特征的大规模理解。通过将建筑轮廓与地理参考LiDAR数据相交生成建筑点云，并借助唯一物业参考编号（UPRN）关联英国能效数据库中的属性信息。为获取代表性样本，我们从英格兰多个城乡区域选取了100万栋建筑，其中50万栋已关联能耗特征数据。配合本文发布的开源代码，可生成新区域的建筑点云。该数据集支持建筑能耗建模领域的创新研究，并可通过UPRN或地理坐标附加建筑特征，便捷扩展至其他研究领域。