气候变化领域集成服务门户(CCPortal): Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: A case study of Chinese cities

CCPortal

DOI	10.1016/j.rse.2020.111838
	Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: A case study of Chinese cities
	Zhong Y.; Su Y.; Wu S.; Zheng Z.; Zhao J.; Ma A.; Zhu Q.; Ye R.; Li X.; Pellikka P.; Zhang L.
发表日期	2020
ISSN	00344257
卷号	247
英文摘要	Reliable urban land-use maps are essential for urban analysis because the spatial distribution of land use reflects the complex environment of cities under the combined effects of nature and socio-economics. In recent years, very high resolution (VHR) remote sensing imagery interpretation has resolved the “semantic gap” between the low-level data and the high-level semantic scenes, and has been used to map urban land use. Nevertheless, the existing frameworks cannot easily be applied to practical urban analysis, which can be attributed to three main reasons: 1) the indistinguishable socio-economic attributes of the same ground object layouts; 2) the weak transferability of the supervised frameworks and the time-consuming training sample annotation; and 3) the category system inconsistency between the data source and the urban land-use application. In this paper, to achieve an “application gap” breakthrough for urban land-use mapping, a data-driven point, line, and polygon semantic object mapping (PLPSOM) framework is proposed, which makes full use of open-source VHR images and multi-source geospatial data. In the PLPSOM framework, point, line, and polygon semantic objects are represented by the points of interest (POIs), OpenStreetMap (OSM) data, and VHR images corresponding to the scenes in the land-use mapping units, respectively. OSM line semantic objects are utilized to supply the boundaries of the land-use mapping units for the POIs and VHR images, forming urban land parcels (street blocks). To reduce the cost of the data annotation, the training dataset is constructed using multiple open-source data sources. An enhanced deep adaptation network (EDAN) is then proposed to acquire the categories of the VHR scene images in the case of partial transfer learning. Finally, in order to meet the actual needs, a rule-based category mapping (RCM) model is applied to integrate the categories of the POIs and VHR images into the urban land-use category system, allowing us to acquire the land-use maps of the cities. The effectiveness of the proposed method was tested in four cities of China, including six specific areas: Beijing and Wuhan city centers; the Hanyang District of Wuhan; the Hannan District of Wuhan; Macao; and the Wan Chai area of Hong Kong, achieving a high classification accuracy. The “urban image” analysis confirmed the practicality of the obtained urban land-use maps. © 2020 Elsevier Inc.
英文关键词	Enhanced deep adaptation network; Multi-source geospatial data; Rule-based category mapping; Scene classification; Semantic objects; Urban land-use mapping; Very high resolution remote sensing imagery
语种	英语
scopus关键词	Computer software maintenance; Economic analysis; Geometry; Image enhancement; Land use; Open Data; Remote sensing; Semantics; Transfer learning; Classification accuracy; Complex environments; High level semantics; Land-use mappings; Open source datum; Points of interest; Remote sensing imagery; Very high resolution; Mapping; data assimilation; land use change; remote sensing; satellite imagery; spatial distribution; urban area; Beijing [China]; China; Hanyang; Hong Kong; Hubei; Macau; Wuhan
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179264
作者单位	State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China; Hubei Provincial Engineering Research Center of Natural Resources Remote Sensing Monitoring, Wuhan University, China; School of Computer Science, China University of Geosciences, China; School of Geography and Information Engineering, China University of Geosciences, China; Earth Change Observation Laboratory, Department of Geosciences and Geography, University of Helsinki, Finland
推荐引用方式 GB/T 7714	Zhong Y.,Su Y.,Wu S.,et al. Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: A case study of Chinese cities[J],2020,247.
APA	Zhong Y..,Su Y..,Wu S..,Zheng Z..,Zhao J..,...&Zhang L..(2020).Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: A case study of Chinese cities.Remote Sensing of Environment,247.
MLA	Zhong Y.,et al."Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: A case study of Chinese cities".Remote Sensing of Environment 247(2020).