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-DupontI, Nikola
Hans, Nicolai
Kaack, Lynn H.
Zumwald, Marius
Andrieux, François
De Barros Soares, Daniel
Lohrey, Steffen
PichlerI, Peter-Paul
Creutzig, Felix
Dokumenttyp: Artikel
Erscheinungsdatum: 2020
Verlag/Hrsg.: San Francisco
California
US : PLOS
Schlagwörter: adult / Germany / France / government / Italy / machine learning / Netherlands / prediction / 500 / 610
Sprache: unknown
Permalink: https://search.fid-benelux.de/Record/base-29160998
Datenquelle: BASE; Originalkatalog
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Link(s) : https://dx.doi.org/10.34657/6734