Systematic modelling of a large-scale, historicised, well-detailed building land use, with the fusion of massive and heterogeneous datasets.

Buildings are the primary venues for human activity. Generating precise numeric building models is of great interest for many applications. At regional and national scales, many different formalisms are employed to represent the various components of buildings, yet the building land use is frequently oversimplified - even when mixed-use buildings are considered. Inferring a realistic building land use, defined as an inventory of the distinct functional spaces hosted by a building, brings valuable knowledge for building models. Manual measurement can produce highly realistic interior models, but such approaches are labour-intensive and must be conducted on a case-by-case basis. We introduce a fully automated workflow that systematically simulates a well-described building land use by integrating three complementary tasks: (1) use-oriented building object modelling - defining building objects that capture building land use, (2) activity census and conflation - aggregating and reconciling activity records from heterogeneous datasets, and (3) logical attribute generation - deriving detailed attributes for housing units and activities in relation to the characteristics of their host building and other environmental factors. The workflow adapts to varying levels of data availability and attribute richness, and it supports "recent-period historicisation" (i.e., reconstructing past states of the built environment). Because the entire pipeline is automated, it can be deployed at a large scale; we demonstrate its application to an entire country. We evaluate the method through multiple validation procedures, including comparisons with ground truth measurements, showing that the approach reliably reproduces building land use.