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Rapid renovation of Europe's inefficient buildings is required to reduceclimate change. However, analyzing and evaluating buildings at scale ischallenging because every building is unique. In current practice, the energyperformance of buildings is assessed during on-site visits, which are slow,costly, and local. This paper presents a building point cloud dataset thatpromotes a data-driven, large-scale understanding of the 3D representation ofbuildings and their energy characteristics. We generate building point cloudsby intersecting building footprints with geo-referenced LiDAR data and linkthem with attributes from UK's energy performance database via the UniqueProperty Reference Number (UPRN). To achieve a representative sample, we selectone million buildings from a range of rural and urban regions across England,of which half a million are linked to energy characteristics. Building pointclouds in new regions can be generated with the open-source code publishedalongside the paper. The dataset enables novel research in building energymodeling and can be easily expanded to other research fields by adding buildingfeatures via the UPRN or geo-location.

Points for Energy Renovation (PointER): A LiDAR-Derived Point Cloud Dataset of One Million English Buildings Linked to Energy Characteristics