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 ...
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Dokumenttyp: | status-type:publishedVersion |
Erscheinungsdatum: | 2020 |
Verlag/Hrsg.: |
San Francisco
California US : PLOS |
Schlagwörter: | adult / Germany / France / government / Italy / machine learning / Netherlands / prediction / ddc:500 / ddc:610 |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-27205088 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://oa.tib.eu/renate/handle/123456789/7687 |