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.
Highlights
Sci. 2021, 11, 1445. https://doi.org/Historical maps are the most powerful source of information for understanding urban phenomena and changes that contributed to defining our cities’ actual shape
Several geomatic and modelling techniques have been developed in the last years to generate 3D/4D city models, derived with different levels of automation and input data [1,2,3,4]. 3D/4D models can be digital copies of our cities when enriched with textural information or semantically enhanced when the geometry is linked to other attributes
Data were randomly split into training and test sets and metrics computed
Summary
Sci. 2021, 11, 1445. https://doi.org/Historical maps are the most powerful source of information for understanding urban phenomena and changes that contributed to defining our cities’ actual shape. The growth and transformation of the urban patterns and landscapes can be analysed through these differently accurate, symbolised, and generalised representations of reality. 3D/4D models can be digital copies of our cities when enriched with textural information or semantically enhanced when the geometry is linked to other attributes. Depending on their nature, they can be used for visualisation, simulations, geospatial analyses, planning activities, and many other applications [5]. The undisputable advantage offered by the three dimensions is the broader comprehension of the built spaces and relations in the urban pattern, as well as their interaction with the natural elements. Modelling in 3D multi-temporal versions of the same city (4D) can broaden how these relations and interactions are changed over time
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