A research team from Skoltech and FBK (Italy) presented a methodology to derive 4D building models using historical maps and machine learning. The implemented method relies on the geometric, neighbourhood, and categorical attributes to predict building heights. The method is useful for understanding urban phenomena and changes contributing to defining our cities' actual shapes. The results were published in the MDPI Applied Sciences journal.
Historical maps are the most powerful source used to analyze changes in urban development. Nevertheless, maps represent the 3D world in the 2D space, which describes the main features of the urban environment but fails to incorporate other spatial information, such as building heights. In 3D/4D city modeling applications based on historical data, the lack of building heights is a major obstacle for accurate space representation, analysis, visualization, or simulations.
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