论文标题
建筑物的机器学习表征和城市指标的幂律恢复
Machine learning for buildings characterization and power-law recovery of urban metrics
论文作者
论文摘要
在本文中，我们专注于城市的关键组成部分：其建筑库存，其中持有其许多社会经济活动。就我们而言，缺乏有关其特征的全面数据库以及对被调查子集的局限性的，这会导致我们采用数据驱动的技术来将我们的知识扩展到近乎城市的规模。使用神经网络和随机森林来确定建筑物对一组形状特征的地板和建筑期间的依赖性：面积，周边和高度以及年度消耗量，依靠贝鲁特市的调查数据。然后，将预测的结果与既定的城市形式的规模定律进行了比较，这构成了我们工作的进一步一致性检查和验证。
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.
