Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China
Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China
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DOI：
10.3390/rs12172805
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发表时间：
2020-09-01
期刊：
影响因子：
5
通讯作者：
Yu, Bailang
中科院分区：
文献类型：
--
作者：
Chen, Wei;Zhou, Yuyu;Yu, Bailang
在以建筑物为基本空间单元的高分辨率建筑物建模中，建筑物类型信息是城市规划和管理的迫切需要。然而，在世界许多地方，这一信息仍然缺失。本文提出了一个从高德和百度地图获取建筑物类型信息的框架，这些数据包括兴趣点(POI)数据、建筑物覆盖区、土地利用面和道路。首先，我们使用基于自然语言处理(NLP)的方法(即文本相似性度量和主题建模)自动重新分类POI类别，从而可以直接推断建筑类型。其次，基于建筑足迹与POI之间的关系，使用类型率和面积比两个指标来识别建筑类型。中国说，该框架在北京使用了超过440,000个建筑足迹进行了测试。基于NLP的方法和建筑物类型识别方法的总体准确率分别为89.0%和78.2%，kappa系数分别为0.83和0.71。该框架可移植到其他中国城市，用于从网络地图平台获取建筑类型信息。这项研究产生的数据产品对建筑层面的定量城市研究非常有用。
The information of building types is highly needed for urban planning and management, especially in high resolution building modeling in which buildings are the basic spatial unit. However, in many parts of the world, this information is still missing. In this paper, we proposed a framework to derive the information of building type using geospatial data, including point-of-interest (POI) data, building footprints, land use polygons, and roads, from Gaode and Baidu Maps. First, we used natural language processing (NLP)-based approaches (i.e., text similarity measurement and topic modeling) to automatically reclassify POI categories into which can be used to directly infer building types. Second, based on the relationship between building footprints and POIs, we identified building types using two indicators of type ratio and area ratio. The proposed framework was tested using over 440,000 building footprints in Beijing, China. Our NLP-based approaches and building type identification methods show overall accuracies of 89.0% and 78.2%, and kappa coefficient of 0.83 and 0.71, respectively. The proposed framework is transferrable to other China cities for deriving the information of building types from web mapping platforms. The data products generated from this study are of great use for quantitative urban studies at the building level.