Machine learning for buildings' characterization and power-law recovery of urban metrics.
In this paper we focus on a critical component of the city: its building stock, which holds much of its socio-economic activities. In our case, the lack of a comprehensive database about their features and its limitation to a surveyed subset lead us to adopt data-driven techniques to extend our know...
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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Public Library of Science (PLoS)
2021-01-01
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| Colecção: | PLoS ONE |
| Acesso em linha: | https://doi.org/10.1371/journal.pone.0246096 |
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