Information mining for urban building energy models (UBEMs) from two data sources: OpenStreetMap and baidu map

Abstract

Urban Building Energy Models (UBEM) are essential for urban energy related applications. The lack of publicly available data is widely acknowledged as one of the main barriers in the development of UBEM. Hence, open datasets such as OpenStreetMap (OSM) and Baidu Map (BDM) offer a significant potential to improve the quality and efficiency of data collection, integration, and processing in UBEM. This paper proposes methods to obtain three fundamental components, the information of building footprints, types and height, in a complete input dataset for UBEM, using OSM data and BDM data. The assessments of building footprints suggest that BDM data is superior to OSM data in completeness and shape accuracy. The accuracy of the determination of building types in OSM by taking advantages of the Tags or Values and the land use type is higher than that in BDM linked with Points of Interests (POIs). The information of building height is inadequate both in OSM and BDM. However, the transfer of altitude information from POIs to buildings could be realized in BDM, showing better quality in residential buildings than in office buildings. This study demonstrates that, despite of some defects, OSM and BDM still have great potential to be useful data sources for UBEM.