Learning from urban form to predict building heights

Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a ma... Mehr ...

Verfasser: Milojevic-Dupont, Nikola
Hans, Nicolai
Kaack, Lynn H.
Zumwald, Marius
Andrieux, François
de Barros Soares, Daniel
Lohrey, Steffen
Pichler, Peter-Paul
Creutzig, Felix
Dokumenttyp: Artikel
Erscheinungsdatum: 2020
Schlagwörter: 710 Städtebau / Raumplanung / Landschaftsgestaltung / cities / machine learning / roads / Netherlands / Europe / Italy / machine learning algorithms / neighborhoods
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-26799174
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://depositonce.tu-berlin.de/handle/11303/12242