scholarly journals A Rapid and Automated Urban Boundary Extraction Method Based on Nighttime Light Data in China

2019 ◽  
Vol 11 (9) ◽  
pp. 1126 ◽  
Author(s):  
Xiaojiang Liu ◽  
Xiaogang Ning ◽  
Hao Wang ◽  
Chenggang Wang ◽  
Hanchao Zhang ◽  
...  
Keyword(s):  
High Precision ◽  
Mutation Detection ◽  
Detection Method ◽  
Rapid Expansion ◽  
Optimal Threshold ◽  
Threshold Function ◽  
Boundary Extraction ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Relative Errors

As urbanization has progressed over the past 40 years, continuous population growth and the rapid expansion of urban land use have caused some regions to experience various problems, such as insufficient resources and issues related to the environmental carrying capacity. The urbanization process can be understood using nighttime light data to quickly and accurately extract urban boundaries at large scales. A new method is proposed here to quickly and accurately extract urban boundaries using nighttime light imagery. Three types of nighttime light data from the DMSP/OLS (US military’s defense meteorological satellite), NPP-VIIRS (National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite), and Luojia1-01 data sets are selected, and the high-precision urban boundaries obtained from a high-resolution image are selected as the true value. Next, 15 cities are selected as the training samples, and the Jaccard coefficient is introduced. The spatial data comparison method is then used to determine the optimal threshold function for the urban boundary extraction. Alternative high-precision urban boundary truth-values for the 13 cities are then selected, and the accuracy of the urban boundary extraction results obtained using the optimal threshold function and the mutation detection method are evaluated. The following observations are made from the results: (i) The average relative errors for the urban boundary extraction results based on the three nighttime light data sources (DMSP/OLS, NPP-VIIRS, and Luojia1-01) using the optimal threshold functions are 29%, 20%, and 39%, respectively. Compared with the mutation detection method, these relative errors are reduced by 83%, 18%, and 77%, respectively; (ii) The average overall classification accuracies of the extracted urban boundaries are 95%, 96%, and 93%, respectively, which are 5%, 1%, and 7% higher than those for the mutation detection method; (iii) The average Kappa coefficients of the extracted urban boundaries are 61%, 71%, and 61%, respectively, which are 5%, 4%, and 12% higher than for the mutation detection method.

scholarly journals An Improved Method for Urban Built-Up Area Extraction Supported by Multi-Source Data

2021 ◽  
Vol 13 (9) ◽  
pp. 5042
Author(s):  
Chengming Li ◽  
Xiaoyan Wang ◽  
Zheng Wu ◽  
Zhaoxin Dai ◽  
Jie Yin ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Urban Expansion ◽  
Social Factor ◽  
Road Networks ◽  
Extraction Methods ◽  
Boundary Extraction ◽  
Points Of Interest ◽  
Nighttime Light Data ◽  
The Social ◽  
Nighttime Light

Urban built-up areas, where urbanization process takes place, represent well-developed areas in a city. The accurate and timely extraction of urban built-up areas has a fundamental role in the comprehension and management of urbanization dynamics. Urban built-up areas are not only a reflection of urban expansion but also the main space carrier of social activities. Recent research has attempted to integrate the social factor to improve the extraction accuracy. However, the existing extraction methods based on nighttime light data only focus on the integration of a single factor, such as points of interest or road networks, which leads to weak constraint and low accuracy. To address this issue, a new index-based methodology for urban built-up area extraction that fuses nighttime light data with multisource big data is proposed in this paper. The proposed index, while being conceptually simple and computationally inexpensive, can extract the built-up areas efficiently. First, a new index-based methodology, which integrates nighttime light data with points-of-interest, road networks, and the enhanced vegetation index, was constructed. Then, based on the proposed new index and the reference urban built-up data area, urban built-up area extraction was performed based on the dynamic threshold dichotomy method. Finally, the proposed method was validated based on actual data in a city. The experimental results indicate that the proposed index has high accuracy (recall, precision and F1 score) and applicability for urban built-up area boundary extraction. Moreover, this paper discussed different existing urban area extraction methods, and provides an insight into the appropriate approaches selection for further urban built-up area extraction in cities with different conditions.

scholarly journals Delineating the Urban Areas of a Cross-Boundary City with Open-Access Data: Guangzhou–Foshan, South China

2021 ◽  
Vol 13 (5) ◽  
pp. 2930
Author(s):  
Pengfei Ban ◽  
Wei Zhan ◽  
Qifeng Yuan ◽  
Xiaojian Li
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
City Size ◽  
Border City ◽  
Point Of Interest ◽  
Nighttime Light Data ◽  
Profile Line ◽  
Point Analysis ◽  
Nighttime Light ◽  
Single Data

Cities defined mainly from the administrative aspect can create impact and problems especially in the case of China. However, only a few researchers from China have attempted to identify urban areas from the morphology dimension. In addition, previous studies have been mostly based on the national and regional scales or a single prefecture city and have completely ignored cross-boundary cities. Defining urban areas on the basis of a single data type also has limitations. To address these problems, this study integrates point of interest and nighttime light data, applies the breaking point analysis method to determine the physical geographic scope of the Guangzhou–Foshan cross-border city, and then compares this city with Beijing and Shanghai. Results show that Guangzhou–Foshan comprises one core urban area and six suburban counties, among which the core urban area extends across the administrative boundaries of Guangzhou and Foshan. The urban area and average urban radius of Guangzhou–Foshan are larger than those of Beijing and Shanghai, and this finding contradicts the city size measurements based on the administrative division system of China and those published on traditional official statistical yearbooks. In terms of urban density value, Shanghai has the steepest profile followed by Guangzhou–Foshan and Beijing, and the profile line of Guangzhou–Foshan has a bimodal shape.

Identification of urban shrinkage using NPP-VIIRS nighttime light data at the county level in China

Cities ◽  
2021 ◽  
Vol 118 ◽  
pp. 103373
Author(s):  
Ying Zhou ◽  
Chenggu Li ◽  
Wensheng Zheng ◽  
Yuefang Rong ◽  
Wei Liu
Keyword(s):  
County Level ◽  
Nighttime Light Data ◽  
Nighttime Light

DEVELOPMENT OF A METHOD OF ESTIMATION OF TOTAL FLOOR AREA USING DEEP LEARNING BASED ON NIGHTTIME-LIGHT DATA

2020 ◽  
Vol 76 (6) ◽  
pp. II_1-II_7
Author(s):  
Ryusei SAITO ◽  
Chizuko HIRAI ◽  
Chihiro HAGA ◽  
Takanori MATSUI ◽  
Hiroaki SHIRAKAWA ◽  
...  
Keyword(s):  
Deep Learning ◽  
Floor Area ◽  
Nighttime Light Data ◽  
Nighttime Light

High-Precision Atmospheric Disturbance Detection Method for Moving Objects

2021 ◽  
Vol 41 (2) ◽  
pp. 0228002
Author(s):  
张月 Zhang Yue ◽  
王旭 Wang Xu ◽  
苏云 Su Yun ◽  
张学敏 Zhang Xuemin ◽  
邬志强 Wu Zhiqiang ◽  
...  
Keyword(s):  
High Precision ◽  
Moving Objects ◽  
Detection Method ◽  
Atmospheric Disturbance ◽  
Disturbance Detection

High precision detection method of safety helmet based on convolution neural network

2021 ◽  
Vol 36 (7) ◽  
pp. 1018-1026
Author(s):  
Tian-yu LI ◽  
◽  
Dong LI ◽  
Ming-ju CHEN ◽  
Hao WU ◽  
...  
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Detection Method ◽  
Convolution Neural Network
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