A scalable data collection, characterization, and accounting framework for urban material stocks
A scalable data collection, characterization, and accounting framework for urban material stocks
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DOI：
10.1111/jiec.13198
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发表时间：
2021-09-25
影响因子：
5.9
通讯作者：
Tingley, Danielle Densley
中科院分区：
文献类型：
--
作者：
Arbabi, Hadi;Lanau, Maud;Tingley, Danielle Densley
建筑存量代表着广泛的二次资源储存库。然而，通常基于建筑物的原型分类及其相应的材料强度的自下而上的特征仍然不适合为循环经济战略提供充分的信息。事实上，这些方法通常会导致建筑物特定细节的丢失，以及根据材料质量（例如，玻璃）而不是组件（例如，窗户）来描述建筑物的库存特征。为了提供更高分辨率的细节，需要一种可扩展的方法来描述城市库存，从而能够在建筑组件级别上对建筑库存进行自下而上的估计。在本文中，我们提出了一个自动化表征城市存量的框架。通过使用移动传感方法并将其与计算机视觉相结合，可以将城市库存捕获为3D表面地图，从而可以对库存对象、组件和材料进行识别和语义分类。通过对英国谢菲尔德社区的案例研究，我们展示了这一框架的潜力，通过使用一个原型工作流，包括一个定制的移动传感平台和一套现有的神经网络模型来计算建筑物外部门窗的估计计数。框架的原型实现实现了与通过人工计数实现的总数和构建级组件计数相当的数量。这种对组件的自动化估计使我们能够了解整个循环经济层次的机会，并告知整个供应链的利益相关者，以便更好地为包括建筑翻新在内的循环战略的实施做好准备。
Building stocks represent an extensive reservoir of secondary resources. However, common bottom-up characterization of these, often based on archetypal classification of buildings and their corresponding material intensity, are still not suitable to adequately inform circular economic strategies. Indeed, these approaches typically result in a loss of building-specific details, and a building stock characterization in terms of material mass, for example, glass, rather than component, for example, window. To deliver this higher resolution of details, a scalable approach to urban stock characterization, that enables a bottom-up estimation of building stocks at the building component level, is needed. In this paper, we present a framework to automate the characterization of urban stock. By using and combining a mobile-sensing approach with computer vision, urban stocks can be captured as 3D surface maps allowing the identification and semantic classification of stock objects, components, and materials. We demonstrate the potential of this framework through a case study of a neighborhood in Sheffield, UK, by using a prototype workflow comprising a custom-made mobile-sensing platform and an existing suite of neural network models to calculate an estimate count of buildings external doors and windows. The prototype implementation of the framework achieves comparable total and building-level component counts with those achieved through manual human counts. Such automated estimation of components enables an understanding of opportunities across the circular economic hierarchies and informs stakeholders across the supply chain to better prepare for the implementation of circular strategies including building refurbishments.