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.

该研究提出了一种建筑点云数据集，利用地理引用 LiDAR 数据与英国能源性能数据库相结合，实现建筑能源信息的数据驱动、大规模理解，为建筑能源建模提供了新的研究思路。