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

scholarly journals Calculating the water dissipation of buildings in urban areas based on global nighttime light data

2021 ◽  
Author(s):  
Han Gao ◽  
Jiahong Liu ◽  
Chao Mei ◽  
Wang Hao ◽  
Weiwei Shao ◽  
...  
Keyword(s):  
Urban Areas ◽  
Developing Economies ◽  
Calculation Model ◽  
Urban Water ◽  
Heat Island ◽  
Floor Area ◽  
Island Effect ◽  
Nighttime Light Data ◽  
Basic Understanding ◽  
Nighttime Light

Abstract Urban water dissipation is increasing gradually as urbanization progresses. Urban water dissipation mainly includes the dissipation of water in buildings and natural water evapotranspiration. Previous studies have mainly focused on calculating natural evapotranspiration in urban areas and have overlooked the dissipation of water in buildings under the influence of strong human-related water use activities. In this paper, the concept of building water dissipation (BWD) was proposed to describe the phenomenon that water dissipation occurs inside buildings. Moreover, a BWD calculation model was established and applied to calculate global building water dissipation. To reveal the specific water dissipation inside buildings, it is necessary to obtain the urban building floor area first. This paper proposed a new method to calculate the urban building floor area based on global nighttime light data obtained from NPP-VIIRS. Taking the floor area results into the BWD calculation model, the global building water dissipation in urban areas was found to be 127 billion m3 in 2015. The vast building water dissipation that occurs in urban areas mostly results from rapidly developing economies and intense human activities. The results provide a basic understanding of the nexus between water resources and the energy-heat island effect in urban areas.

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
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