Large-scale urban building function mapping by integrating multi-source web-based geospatial data
Large-scale urban building function mapping by integrating multi-source web-based geospatial data
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DOI：
10.1080/10095020.2023.2264342
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发表时间：
2023-10
影响因子：
6
通讯作者：
Wei Chen;Yuyu Zhou;Eleanor C. Stokes;Xuesong Zhang
中科院分区：
文献类型：
--
作者：
Wei Chen;Yuyu Zhou;Eleanor C. Stokes;Xuesong Zhang
建筑物的形态(如形状、大小和高度)和功能(如工作、居住和购物)信息对于城市规划和管理以及其他应用如城市尺度的建筑能耗建模是非常必要的。由于社会经济地理空间数据的可获得性有限，绘制建筑物功能图比绘制建筑物形态信息更具挑战性，尤其是在大范围内。在这项研究中，我们提出了一个集成框架，通过集成多源基于网络的地理空间数据来绘制美国50个 城市的建筑功能地图。首先，开发了一个网络爬虫来从TripAdvisor.com中提取兴趣点(POI)，并开发了一个地图爬虫来从Google Maps中提取POI和土地利用地块。其次，基于微软建筑足迹提取的景观特征，采用无监督机器学习算法OneClassSVM对住宅建筑进行识别。第三，利用POI类型比和土地利用地块面积比识别医院、酒店、学校、商店、餐馆和办公室等六种非居住功能。准确度评估表明，该框架表现良好，平均总体准确率为94%，kappa系数为0.63。随着谷歌地图和TripAdvisor.com的全球覆盖，拟议的框架可以移植到世界其他城市。这项研究产生的数据产品对城市规模的定量城市研究非常有用，例如大面积上单个建筑级别的建筑能耗建模。
Morphological (e.g. shape, size, and height) and function (e.g. working, living, and shopping) information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling. Due to the limited availability of socio-economic geospatial data, it is more challenging to map building functions than building morphological information, especially over large areas. In this study, we proposed an integrated framework to map building functions in 50 U.S. cities by integrating multi-source web-based geospatial data. First, a web crawler was developed to extract Points of Interest (POIs) from Tripadvisor.com, and a map crawler was developed to extract POIs and land use parcels from Google Maps. Second, an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints. Third, the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions (i.e. hospital, hotel, school, shop, restaurant, and office). The accuracy assessment indicates that the proposed framework performed well, with an average overall accuracy of 94% and a kappa coefficient of 0.63. With the worldwide coverage of Google Maps and Tripadvisor.com, the proposed framework is transferable to other cities over the world. The data products generated from this study are of great use for quantitative city-scale urban studies, such as building energy use modeling at the single building level over large areas.