Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

Gong, Peng; Chen, Bin; Xuecao, Li; Liu, Han; Wang, Jie; Bai, Yuqi; Chen, Jingming; Chen, Xi; Fang, Lei; Feng, Shuailong; Feng, Yang; Gong, Yukang; Gu, Hao; Huang, Huabing; Huang, Xiaochun; Jiao, Hongzan; Kang, Yingdong; Lei, Guangbin; Li, Ainong; Li, Xiaoting; Li, Xun; Li, Yuechen; Li, Zhilin; Li, Zhongde; Liu, Chong; Liu, Chunxia; Liu, Maochou; Liu, Shuguang; Mao, Wanliu; Chen, Miao; Ni, Hao; Pan, Qingqing; Qi, Shanshan; Ren, Zhehao; Shan, Zhuoran; Shen, Shaoqing; Shi, Minjun; Song, Yimeng; Su, Mo; Suen, Hoi Ping; Sun, Bo; Sun, Fei; Sun, Jian; Sun, Lin; Sun, Wenyao; Tian, Tian; Tong, Xiaohua; Tseng, Yi Hsing; Tu, Ying; Hong, Wang; Wang, Lan; Wang, Xi; Wang, Zongming; Wu, Tinghai; Xie, Yaowen; Yang, Jian; Yang, Jun; Yuan, Mei; Yue, Wenze; Zeng, Heng; Zhang, Kuo; Zhang, Neng; Zhang, Tao; Yu, Zhang; Zhao, Feng; Zheng, Yonglai; Zhou, Qiming; Clinton, Nicholas; Zhu, Zhiliang; Xu, Bing

doi:10.1016/j.scib.2019.12.007

Cited by 366 publications

(188 citation statements)

References 13 publications

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“…These values produced the most homogeneous and meaningful objects within the image. After that, we converted those segmentation objects into polygons and further overlapped them with the original parcels of EULUC [1]. In the end, a total number of 58,311 parcels was regenerated, which was approximately ten times that of the original parcels.…”

Section: Regeneration Of Parcelsmentioning

confidence: 99%

“…Additionally, the area of each parcel was also computed. Moreover, following our previous study [1], features of POIs and Luojia-1 NTLs were recalculated within each parcel. In this study, the two-level scheme of EULUC was adopted as the basic classification system [1].…”

Section: Regeneration Of Parcelsmentioning

confidence: 99%

“…Recently, the concept of a two-level classification system entitled essential urban land use categories (EULUC) has been proposed by Gong et al [1]. The EULUC criterion divides the urban land into five essential Level I classes and twelve Level II classes.…”

Section: Introductionmentioning

confidence: 99%

“…The correct division of parcels contributes to the improvement of classification accuracy. In our previous work with EULUC, parcels were delineated by road vectors derived from the OpenStreetMap (OSM) data [1]; however, roads of the OSM data are usually sparse or even absent in some suburbs and rural areas. As a result, parcels based on road vectors are too big for urban land use mapping in those areas and normally have mixed functions (i.e., one road-based parcel could have two or more urban land use types simultaneously).…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have shown that object-based image segmentation approaches can effectively reduce the degree of parcel mixture [4][5][6][7][8]. On the other hand, as shown in the study of EULUC-China, the 1 km Tencent mobile phone locating-request (MPL) data seemed to be too coarse for parcel-based mapping, and the contribution of features obtained from this kind of data was reported to be the lowest [1]. Therefore, higher spatial resolution data is required to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

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Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach

Чэн

Zhang

et al. 2020

Remote Sensing

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Understanding distributions of urban land use is of great importance for urban planning, decision support, and resource allocation. The first mapping results of essential urban land use categories (EULUC) in China for 2018 have been recently released. However, such kind of national maps may not sufficiently meet the growing demand for regional analysis. To address this shortcoming, here we proposed a segmentation-based framework named EULUC-seg to improve the mapping results of EULUC at the city scale. An object-based segmentation approach was first applied to generate the basic mapping units within urban parcels. Multiple features derived from high-resolution remotely sensed and social sensing data were updated and then recalculated within each unit. Random forest was adopted as the machine learning algorithm for classifying urban land use into five Level I classes and twelve Level II classes. Finally, an accuracy assessment was carried out based on a collection of manually interpreted samples. Results showed that our derived map achieved an overall accuracy of 87.58% for Level I, and 73.53% for Level II. The accurate and refined map of EULUC-seg is expected to better support various applications in the future.

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Section: Regeneration Of Parcelsmentioning

confidence: 99%

Section: Regeneration Of Parcelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach

Чэн

Zhang

et al. 2020

Remote Sensing

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Urban heat islands in Hong Kong: Bonding with atmospheric stability

Wang

Chen

et al. 2021

Atmospheric Science Letters

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A barrier to urban heat island (UHI) mitigation is the lack of quantitative attribution of the various contributions to UHI intensity. This study demonstrates the daily and seasonal dynamics of UHIs in Hong Kong, a subtropical highdensity city. The nocturnal UHIs of the city are grouped according to various dynamic stability conditions (neutral, weak stable, and strong stable) of the boundary layer. Results indicate that the stronger the atmospheric stability, the more intense the UHI. The atmospheric anomalies linked to these stability classifications are hence revealed. In summer, nights of neutral (strong stable) stratification are controlled by low (high) pressure with rising (sinking) motion, less (more) precipitation, and lower (higher) air temperature at the surface. In winter, the influence of the large-scale circulation system of the East Asian winter monsoon is significant. In the upper layer, the East Asian jet stream retreats westward (is displaced northward) on nights with neutral (strong stable) atmospheric stratification. At the surface, southeast China is hot and humid on neutral nights, while on strong stable nights, the coastal regions of southeast China are dry, and East Asia is dominated by positive surface air temperature anomalies. Atmospheric anomalies are generally nonsignificant on nights with weak stable stratification in both summer and winter. These findings provide potential predictors for UHI intensity.

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Application of LCZ to Time-Series Urban Morphology Detection

Zhao,

2024

Local Climate Zone Application in Sustainable Urban Development

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Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

Cited by 366 publications

References 13 publications

Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach

Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach

Urban heat islands in Hong Kong: Bonding with atmospheric stability

Application of LCZ to Time-Series Urban Morphology Detection

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