All Publications

  • Points for energy renovation (PointER): A point cloud dataset of a million buildings linked to energy features. Scientific data Krapf, S., Mayer, K., Fischer, M. 2023; 10 (1): 639


    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.

    View details for DOI 10.1038/s41597-023-02544-x

    View details for PubMedID 37730863

  • Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature data APPLIED ENERGY Mayer, K., Haas, L., Huang, T., Bernabe-Moreno, J., Rajagopal, R., Fischer, M. 2023; 333
  • Classifying building energy efficiency from street view and aerial images in Denmark Mayer, K., Heilborn, G., Fischer, M., ACM ASSOC COMPUTING MACHINERY. 2023: 220-223
  • <p>3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D</p> APPLIED ENERGY Mayer, K., Rausch, B., Arlt, M., Gust, G., Wang, Z., Neumann, D., Rajagopal, R. 2022; 310
  • DeepSolar for Germany: A deep learning framework for PV system mapping from aerial imagery Mayer, K., Wang, Z., Arlt, M., Neumann, D., Rajagopal, R., IEEE IEEE. 2020