4D Building Reconstruction with Machine Learning and Historical Maps

Abstract

The increasing importance of three-dimensional (3D) city modelling is linked to these data’s different applications and advantages in many domains. Images and Light Detection and Ranging (LiDAR) data availability are now an evident and unavoidable prerequisite, not always verified for past scenarios. Indeed, historical maps are often the only source of information when dealing with historical scenarios or multi-temporal (4D) digital representations. The paper presents a methodology to derive 4D building models in the level of detail 1 (LoD1), inferring missing height information through machine learning techniques. The aim is to realise 4D LoD1 buildings for geospatial analyses and visualisation, valorising historical data, and urban studies. Several machine learning regression techniques are analysed and employed for deriving missing height data from digitised multi-temporal maps. The implemented method relies on geometric, neighbours, and categorical attributes for height prediction. Derived elevation data are then used for 4D building reconstructions, offering multi-temporal versions of the considered urban scenarios. Various evaluation metrics are also presented for tackling the common issue of lack of ground-truth information within historical data.