地理
功能(生物学)
鉴定(生物学)
数据挖掘
缺少数据
计算机科学
运输工程
点(几何)
工程类
机器学习
数学
植物
几何学
进化生物学
生物
作者
Yingbin Deng,Renrong Chen,Ji Yang,Yong Li,Hao Jiang,Wenyue Liao,Meiwei Sun
标识
DOI:10.1080/13658816.2022.2046756
摘要
Building function type is an important parameter for urban planning and disaster management. However, existing identification methods do not always correctly recognize all building functions because of missing point of interest (POI) data in private areas. In this study, we proposed a hierarchical data-mining model to identify building function types using accessible auxiliary data, which was then applied to a case study. Residential building property was assessed to address missing residential POIs. The building functions were assigned to one of five different types, or a mixed-function type. Standard deviation and mean values extracted from remotely sensed images, distances to major roads, and building shape parameters were used to infer the function types of buildings without assigned function types. The proposed model was able to identify 65% of buildings not previously assigned as residential through the POI, with an overall accuracy of 87%. In addition, all buildings were successfully assigned a function type of residential, commercial, office, warehouse, public service, or mixed-function, with an overall accuracy of 85% for unclassified buildings. Our results demonstrated that missing POI data in private areas could be addressed by integration with multisource data using a simple method.
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