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

TL;DR

This paper introduces PointER, a large-scale LiDAR-derived point cloud dataset of one million English buildings linked to energy data, facilitating data-driven energy renovation research.

Contribution

It provides a novel, extensive dataset linking 3D building representations with energy characteristics, enabling scalable analysis and research in building energy performance.

Findings

Dataset includes one million buildings with linked energy data.

Open-source code allows generation of building point clouds in new regions.

Dataset supports large-scale, data-driven building energy research.

Abstract

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.