Alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery

Light pollution caused by unreasonable and excessive use of outdoor artificial light at night (ALAN) has become one of the fastest growing and most urgent global environmental problems. Therefore, the International Dark-Sky Association advocated the more controlled use of ALAN in urban area to prote...
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Autor*in:	Yang Ye [verfasserIn] Chen Tong [verfasserIn] Baiyu Dong [verfasserIn] Chenhao Huang [verfasserIn] Haijun Bao [verfasserIn] Jinsong Deng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Artificial light at night (ALAN) Light pollution Night-time light control area Urban big data Lighting regulation Urban night-time environment

Übergeordnetes Werk:	In: Ecological Indicators - Elsevier, 2021, 158(2024), Seite 111591-
Übergeordnetes Werk:	volume:158 ; year:2024 ; pages:111591-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.ecolind.2024.111591

Katalog-ID:	DOAJ097613320

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520			\|a Light pollution caused by unreasonable and excessive use of outdoor artificial light at night (ALAN) has become one of the fastest growing and most urgent global environmental problems. Therefore, the International Dark-Sky Association advocated the more controlled use of ALAN in urban area to protect night-time environment and enable sustainable development. However, relevant research and lighting planning are still in the enlightenment stage. This study focused on a typical city experiencing significant ALAN growth, combining night-time remote sensing imagery, urban big data and other muti-source heterogeneous data to systematically research urban night-time light status. We first analyzed the spatio-temporal evolution pattern of ALAN. Subsequently, advanced computer vision algorithms were proposed to recognize the night-time light control area (NLCA) based on the perspective of supply and demand balance. Results suggest both the ALAN intensity and area have grown rapidly over the past decade in the study area, with the intensity increasing by 82 % and the area expanding by 42 %. And a 108.5-square-kilometre area of the NLCA was intelligently delineated, for where the ALAN intensity was more than the demand of population, indicating that the management of the night-time light environment an urgent matter. Therefore, this study proposed practical management solutions according to the different functional zones in city within the NLCA, for further promoting the green transformation and smart upgrading of urban outdoor lighting. To sum up, our research offers a novel approach to resolving the issue of urban light pollution by recognizing the NLCA, as well as facilitating energy conservation and emission reduction.
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spelling	10.1016/j.ecolind.2024.111591 doi (DE-627)DOAJ097613320 (DE-599)DOAJ72917438acd44c31b92b8efa1480bef8 DE-627 ger DE-627 rakwb eng QH540-549.5 Yang Ye verfasserin aut Alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Light pollution caused by unreasonable and excessive use of outdoor artificial light at night (ALAN) has become one of the fastest growing and most urgent global environmental problems. Therefore, the International Dark-Sky Association advocated the more controlled use of ALAN in urban area to protect night-time environment and enable sustainable development. However, relevant research and lighting planning are still in the enlightenment stage. This study focused on a typical city experiencing significant ALAN growth, combining night-time remote sensing imagery, urban big data and other muti-source heterogeneous data to systematically research urban night-time light status. We first analyzed the spatio-temporal evolution pattern of ALAN. Subsequently, advanced computer vision algorithms were proposed to recognize the night-time light control area (NLCA) based on the perspective of supply and demand balance. Results suggest both the ALAN intensity and area have grown rapidly over the past decade in the study area, with the intensity increasing by 82 % and the area expanding by 42 %. And a 108.5-square-kilometre area of the NLCA was intelligently delineated, for where the ALAN intensity was more than the demand of population, indicating that the management of the night-time light environment an urgent matter. Therefore, this study proposed practical management solutions according to the different functional zones in city within the NLCA, for further promoting the green transformation and smart upgrading of urban outdoor lighting. To sum up, our research offers a novel approach to resolving the issue of urban light pollution by recognizing the NLCA, as well as facilitating energy conservation and emission reduction. Artificial light at night (ALAN) Light pollution Night-time light control area Urban big data Lighting regulation Urban night-time environment Ecology Chen Tong verfasserin aut Baiyu Dong verfasserin aut Chenhao Huang verfasserin aut Haijun Bao verfasserin aut Jinsong Deng verfasserin aut In Ecological Indicators Elsevier, 2021 158(2024), Seite 111591- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:158 year:2024 pages:111591- https://doi.org/10.1016/j.ecolind.2024.111591 kostenfrei https://doaj.org/article/72917438acd44c31b92b8efa1480bef8 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X24000487 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 158 2024 111591-
allfields_unstemmed	10.1016/j.ecolind.2024.111591 doi (DE-627)DOAJ097613320 (DE-599)DOAJ72917438acd44c31b92b8efa1480bef8 DE-627 ger DE-627 rakwb eng QH540-549.5 Yang Ye verfasserin aut Alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Light pollution caused by unreasonable and excessive use of outdoor artificial light at night (ALAN) has become one of the fastest growing and most urgent global environmental problems. Therefore, the International Dark-Sky Association advocated the more controlled use of ALAN in urban area to protect night-time environment and enable sustainable development. However, relevant research and lighting planning are still in the enlightenment stage. This study focused on a typical city experiencing significant ALAN growth, combining night-time remote sensing imagery, urban big data and other muti-source heterogeneous data to systematically research urban night-time light status. We first analyzed the spatio-temporal evolution pattern of ALAN. Subsequently, advanced computer vision algorithms were proposed to recognize the night-time light control area (NLCA) based on the perspective of supply and demand balance. Results suggest both the ALAN intensity and area have grown rapidly over the past decade in the study area, with the intensity increasing by 82 % and the area expanding by 42 %. And a 108.5-square-kilometre area of the NLCA was intelligently delineated, for where the ALAN intensity was more than the demand of population, indicating that the management of the night-time light environment an urgent matter. Therefore, this study proposed practical management solutions according to the different functional zones in city within the NLCA, for further promoting the green transformation and smart upgrading of urban outdoor lighting. To sum up, our research offers a novel approach to resolving the issue of urban light pollution by recognizing the NLCA, as well as facilitating energy conservation and emission reduction. Artificial light at night (ALAN) Light pollution Night-time light control area Urban big data Lighting regulation Urban night-time environment Ecology Chen Tong verfasserin aut Baiyu Dong verfasserin aut Chenhao Huang verfasserin aut Haijun Bao verfasserin aut Jinsong Deng verfasserin aut In Ecological Indicators Elsevier, 2021 158(2024), Seite 111591- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:158 year:2024 pages:111591- https://doi.org/10.1016/j.ecolind.2024.111591 kostenfrei https://doaj.org/article/72917438acd44c31b92b8efa1480bef8 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X24000487 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 158 2024 111591-
allfieldsGer	10.1016/j.ecolind.2024.111591 doi (DE-627)DOAJ097613320 (DE-599)DOAJ72917438acd44c31b92b8efa1480bef8 DE-627 ger DE-627 rakwb eng QH540-549.5 Yang Ye verfasserin aut Alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Light pollution caused by unreasonable and excessive use of outdoor artificial light at night (ALAN) has become one of the fastest growing and most urgent global environmental problems. Therefore, the International Dark-Sky Association advocated the more controlled use of ALAN in urban area to protect night-time environment and enable sustainable development. However, relevant research and lighting planning are still in the enlightenment stage. This study focused on a typical city experiencing significant ALAN growth, combining night-time remote sensing imagery, urban big data and other muti-source heterogeneous data to systematically research urban night-time light status. We first analyzed the spatio-temporal evolution pattern of ALAN. Subsequently, advanced computer vision algorithms were proposed to recognize the night-time light control area (NLCA) based on the perspective of supply and demand balance. Results suggest both the ALAN intensity and area have grown rapidly over the past decade in the study area, with the intensity increasing by 82 % and the area expanding by 42 %. And a 108.5-square-kilometre area of the NLCA was intelligently delineated, for where the ALAN intensity was more than the demand of population, indicating that the management of the night-time light environment an urgent matter. Therefore, this study proposed practical management solutions according to the different functional zones in city within the NLCA, for further promoting the green transformation and smart upgrading of urban outdoor lighting. To sum up, our research offers a novel approach to resolving the issue of urban light pollution by recognizing the NLCA, as well as facilitating energy conservation and emission reduction. Artificial light at night (ALAN) Light pollution Night-time light control area Urban big data Lighting regulation Urban night-time environment Ecology Chen Tong verfasserin aut Baiyu Dong verfasserin aut Chenhao Huang verfasserin aut Haijun Bao verfasserin aut Jinsong Deng verfasserin aut In Ecological Indicators Elsevier, 2021 158(2024), Seite 111591- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:158 year:2024 pages:111591- https://doi.org/10.1016/j.ecolind.2024.111591 kostenfrei https://doaj.org/article/72917438acd44c31b92b8efa1480bef8 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X24000487 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 158 2024 111591-
allfieldsSound	10.1016/j.ecolind.2024.111591 doi (DE-627)DOAJ097613320 (DE-599)DOAJ72917438acd44c31b92b8efa1480bef8 DE-627 ger DE-627 rakwb eng QH540-549.5 Yang Ye verfasserin aut Alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Light pollution caused by unreasonable and excessive use of outdoor artificial light at night (ALAN) has become one of the fastest growing and most urgent global environmental problems. Therefore, the International Dark-Sky Association advocated the more controlled use of ALAN in urban area to protect night-time environment and enable sustainable development. However, relevant research and lighting planning are still in the enlightenment stage. This study focused on a typical city experiencing significant ALAN growth, combining night-time remote sensing imagery, urban big data and other muti-source heterogeneous data to systematically research urban night-time light status. We first analyzed the spatio-temporal evolution pattern of ALAN. Subsequently, advanced computer vision algorithms were proposed to recognize the night-time light control area (NLCA) based on the perspective of supply and demand balance. Results suggest both the ALAN intensity and area have grown rapidly over the past decade in the study area, with the intensity increasing by 82 % and the area expanding by 42 %. And a 108.5-square-kilometre area of the NLCA was intelligently delineated, for where the ALAN intensity was more than the demand of population, indicating that the management of the night-time light environment an urgent matter. Therefore, this study proposed practical management solutions according to the different functional zones in city within the NLCA, for further promoting the green transformation and smart upgrading of urban outdoor lighting. To sum up, our research offers a novel approach to resolving the issue of urban light pollution by recognizing the NLCA, as well as facilitating energy conservation and emission reduction. Artificial light at night (ALAN) Light pollution Night-time light control area Urban big data Lighting regulation Urban night-time environment Ecology Chen Tong verfasserin aut Baiyu Dong verfasserin aut Chenhao Huang verfasserin aut Haijun Bao verfasserin aut Jinsong Deng verfasserin aut In Ecological Indicators Elsevier, 2021 158(2024), Seite 111591- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:158 year:2024 pages:111591- https://doi.org/10.1016/j.ecolind.2024.111591 kostenfrei https://doaj.org/article/72917438acd44c31b92b8efa1480bef8 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X24000487 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 158 2024 111591-
language	English
source	In Ecological Indicators 158(2024), Seite 111591- volume:158 year:2024 pages:111591-
sourceStr	In Ecological Indicators 158(2024), Seite 111591- volume:158 year:2024 pages:111591-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Artificial light at night (ALAN) Light pollution Night-time light control area Urban big data Lighting regulation Urban night-time environment Ecology
isfreeaccess_bool	true
container_title	Ecological Indicators
authorswithroles_txt_mv	Yang Ye @@aut@@ Chen Tong @@aut@@ Baiyu Dong @@aut@@ Chenhao Huang @@aut@@ Haijun Bao @@aut@@ Jinsong Deng @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	338074163
id	DOAJ097613320
language_de	englisch
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And a 108.5-square-kilometre area of the NLCA was intelligently delineated, for where the ALAN intensity was more than the demand of population, indicating that the management of the night-time light environment an urgent matter. Therefore, this study proposed practical management solutions according to the different functional zones in city within the NLCA, for further promoting the green transformation and smart upgrading of urban outdoor lighting. 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callnumber-first	Q - Science
author	Yang Ye
spellingShingle	Yang Ye misc QH540-549.5 misc Artificial light at night (ALAN) misc Light pollution misc Night-time light control area misc Urban big data misc Lighting regulation misc Urban night-time environment misc Ecology Alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery
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topic_title	QH540-549.5 Alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery Artificial light at night (ALAN) Light pollution Night-time light control area Urban big data Lighting regulation Urban night-time environment
topic	misc QH540-549.5 misc Artificial light at night (ALAN) misc Light pollution misc Night-time light control area misc Urban big data misc Lighting regulation misc Urban night-time environment misc Ecology
topic_unstemmed	misc QH540-549.5 misc Artificial light at night (ALAN) misc Light pollution misc Night-time light control area misc Urban big data misc Lighting regulation misc Urban night-time environment misc Ecology
topic_browse	misc QH540-549.5 misc Artificial light at night (ALAN) misc Light pollution misc Night-time light control area misc Urban big data misc Lighting regulation misc Urban night-time environment misc Ecology
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title	Alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery
ctrlnum	(DE-627)DOAJ097613320 (DE-599)DOAJ72917438acd44c31b92b8efa1480bef8
title_full	Alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery
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author_browse	Yang Ye Chen Tong Baiyu Dong Chenhao Huang Haijun Bao Jinsong Deng
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title_sort	alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery
callnumber	QH540-549.5
title_auth	Alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery
abstract	Light pollution caused by unreasonable and excessive use of outdoor artificial light at night (ALAN) has become one of the fastest growing and most urgent global environmental problems. Therefore, the International Dark-Sky Association advocated the more controlled use of ALAN in urban area to protect night-time environment and enable sustainable development. However, relevant research and lighting planning are still in the enlightenment stage. This study focused on a typical city experiencing significant ALAN growth, combining night-time remote sensing imagery, urban big data and other muti-source heterogeneous data to systematically research urban night-time light status. We first analyzed the spatio-temporal evolution pattern of ALAN. Subsequently, advanced computer vision algorithms were proposed to recognize the night-time light control area (NLCA) based on the perspective of supply and demand balance. Results suggest both the ALAN intensity and area have grown rapidly over the past decade in the study area, with the intensity increasing by 82 % and the area expanding by 42 %. And a 108.5-square-kilometre area of the NLCA was intelligently delineated, for where the ALAN intensity was more than the demand of population, indicating that the management of the night-time light environment an urgent matter. Therefore, this study proposed practical management solutions according to the different functional zones in city within the NLCA, for further promoting the green transformation and smart upgrading of urban outdoor lighting. To sum up, our research offers a novel approach to resolving the issue of urban light pollution by recognizing the NLCA, as well as facilitating energy conservation and emission reduction.
abstractGer	Light pollution caused by unreasonable and excessive use of outdoor artificial light at night (ALAN) has become one of the fastest growing and most urgent global environmental problems. Therefore, the International Dark-Sky Association advocated the more controlled use of ALAN in urban area to protect night-time environment and enable sustainable development. However, relevant research and lighting planning are still in the enlightenment stage. This study focused on a typical city experiencing significant ALAN growth, combining night-time remote sensing imagery, urban big data and other muti-source heterogeneous data to systematically research urban night-time light status. We first analyzed the spatio-temporal evolution pattern of ALAN. Subsequently, advanced computer vision algorithms were proposed to recognize the night-time light control area (NLCA) based on the perspective of supply and demand balance. Results suggest both the ALAN intensity and area have grown rapidly over the past decade in the study area, with the intensity increasing by 82 % and the area expanding by 42 %. And a 108.5-square-kilometre area of the NLCA was intelligently delineated, for where the ALAN intensity was more than the demand of population, indicating that the management of the night-time light environment an urgent matter. Therefore, this study proposed practical management solutions according to the different functional zones in city within the NLCA, for further promoting the green transformation and smart upgrading of urban outdoor lighting. To sum up, our research offers a novel approach to resolving the issue of urban light pollution by recognizing the NLCA, as well as facilitating energy conservation and emission reduction.
abstract_unstemmed	Light pollution caused by unreasonable and excessive use of outdoor artificial light at night (ALAN) has become one of the fastest growing and most urgent global environmental problems. Therefore, the International Dark-Sky Association advocated the more controlled use of ALAN in urban area to protect night-time environment and enable sustainable development. However, relevant research and lighting planning are still in the enlightenment stage. This study focused on a typical city experiencing significant ALAN growth, combining night-time remote sensing imagery, urban big data and other muti-source heterogeneous data to systematically research urban night-time light status. We first analyzed the spatio-temporal evolution pattern of ALAN. Subsequently, advanced computer vision algorithms were proposed to recognize the night-time light control area (NLCA) based on the perspective of supply and demand balance. Results suggest both the ALAN intensity and area have grown rapidly over the past decade in the study area, with the intensity increasing by 82 % and the area expanding by 42 %. And a 108.5-square-kilometre area of the NLCA was intelligently delineated, for where the ALAN intensity was more than the demand of population, indicating that the management of the night-time light environment an urgent matter. Therefore, this study proposed practical management solutions according to the different functional zones in city within the NLCA, for further promoting the green transformation and smart upgrading of urban outdoor lighting. To sum up, our research offers a novel approach to resolving the issue of urban light pollution by recognizing the NLCA, as well as facilitating energy conservation and emission reduction.
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title_short	Alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery
url	https://doi.org/10.1016/j.ecolind.2024.111591 https://doaj.org/article/72917438acd44c31b92b8efa1480bef8 http://www.sciencedirect.com/science/article/pii/S1470160X24000487 https://doaj.org/toc/1470-160X
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