arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2002.08355physics.soc-phcond-mat.stat-mech
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Machine learning for buildings characterization and power-law recovery of urban metrics

Alaa Krayem, Aram Yeretzian, Ghaleb Faour, Sara Najem

英文摘要:

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 knowledge to the near-city-scale. Neural networks and random forest are applied to identify the buildings' number of floors and construction periods' dependencies on a set of shape features: area, perimeter, and height along with the annual electricity consumption, relying on a surveyed data in the city of Beirut. The predicted results are then compared with established scaling laws of urban forms, which constitutes a further consistency check and validation of our work ow.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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