The Relationship Between Artificial Nighttime Light (ANTL) and Built-Up Area: a Remote Sensing Perspective

Nurbandi, Wahyu; Prasetya, Ruwanda; Kamal, Muhammad

doi:10.1109/icstc.2018.8528633

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…In addition, the National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) night-time light data (spatial resolution of 500 m) was from the National Oceanic and Atmospheric Administration National Geophysical Data Center (NOAA/NGDC). This night-time light data is a representation of the intensity of human activities, which has been used to represent socio-economic parameters, such as GDP, population size, and electricity consumption (Agnihotri and Mishra, 2021;Sun et al, 2020;Doll et al, 2006;Sutton et al, 2007;Sun et al, 2021;Jasinski, 2019;Nurbandi et al, 2018;Henderson et al, 2012;Li et al, 2013). In this study, the NPP/VIIRS night-time light data in 2015 was used to represent the land value per unit area, with which the damage of urban flooding disaster was calculated.…”

Section: Datamentioning

confidence: 99%

“…where i is the serial number of grid cells, n is the total number of grid cells in the study area, d i is disaster damage rate of grid i, V i is value of grid i, and v i is the ratio of the value of grid i to the total value of the study area. In this study, V i was translated from the light intensity of grid i, considering that night-time light data is a good proxy of socioeconomic parameters (Doll et al, 2006;Sutton et al, 2007;Nurbandi et al, 2018;Henderson et al, 2012;Li et al, 2013). It is believed that areas with high brightness of lights is usually a gathering area of human social and economic activities, with high efficiency and economic output of land per unit area, indicating a relatively high level of land value.…”

Section: Resilience Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

A disaster-damage-based framework for assessing urban resilience to intense rainfall-induced flooding

Zhang¹,

Mao²,

Gong³

et al. 2023

Urban Climate

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Section: Datamentioning

confidence: 99%

Section: Resilience Assessmentmentioning

confidence: 99%

A disaster-damage-based framework for assessing urban resilience to intense rainfall-induced flooding

Zhang¹,

Mao²,

Gong³

et al. 2023

Urban Climate

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Extraction of Urban Built-Up Area Based on Deep Learning and Multi-Sources Data Fusion—The Application of an Emerging Technology in Urban Planning

Zhang

Tan

et al. 2022

Land

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With the rapid expansion of urban built-up areas in recent years, it has become particularly urgent to develop a fast, accurate and popularized urban built-up area extraction method system. As the direct carrier of urban regional relationship, urban built-up area is an important reference to judge the level of urban development. The accurate extraction of urban built-up area plays an important role in formulating scientific planning thus to promote the healthy development of both urban area and rural area. Although nighttime light (NTL) data are used to extract urban built-up areas in previous studies, there are certain shortcomings in using NTL data to extract urban built-up areas. On the other hand, point of interest (POI) data and population migration data represent different attributes in urban space, which can both assist in modifying the deficiencies of NTL data from both static and dynamic spatial elements, respectively, so as to improve the extraction accuracy of urban built-up areas. Therefore, this study attempts to propose a feasible method to modify NTL data by fusing Baidu migration (BM) data and POI data thus accurately extracting urban built-up areas in Guangzhou. More accurate urban built-up areas are extracted using the method of U-net deep learning network. The maximum built-up area extracted from the study is 1103.45 km2, accounting for 95.21% of the total built-up area, and the recall rate is 0.8905, the precision rate is 0.8121, and the F1 score is 0.8321. The results of using POI data and BM data to modify NTL data to extract built-up areas have not been significantly improved due to the fact that the more data get fused, the more noise there would be, which would ultimately affect the results. This study analyzes the feasibility and insufficiency of using big data to modify NTL data through data fusion and feature extraction system, which has important theoretical and practical significance for future studies on urban built-up areas and urban development.

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The Relationship Between Artificial Nighttime Light (ANTL) and Built-Up Area: a Remote Sensing Perspective

Cited by 2 publications

References 16 publications

A disaster-damage-based framework for assessing urban resilience to intense rainfall-induced flooding

A disaster-damage-based framework for assessing urban resilience to intense rainfall-induced flooding

Extraction of Urban Built-Up Area Based on Deep Learning and Multi-Sources Data Fusion—The Application of an Emerging Technology in Urban Planning

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