Accurate Suitability Evaluation of Large-Scale Roof Greening Based on RS and GIS Methods
Under increasingly low urban land resources, carrying out roof greening to exploit new green space is a good strategy for sustainable development. Therefore, it is necessary to evaluate the suitability of roof greening for buildings in cities. However, most current evaluation methods are based on qu...
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| Veröffentlicht in: | Sustainability 2020-06, Vol.12 (11), p.4375 |
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| creator | Xu, Nan Luo, Jiancheng Zuo, Jin Hu, Xiaodong Dong, Jing Wu, Tianjun Wu, Songliang Liu, Hao |
| description | Under increasingly low urban land resources, carrying out roof greening to exploit new green space is a good strategy for sustainable development. Therefore, it is necessary to evaluate the suitability of roof greening for buildings in cities. However, most current evaluation methods are based on qualitative and conceptual research. In this paper, a methodological framework for roof greening suitability evaluation is proposed based on the basic units of building roofs extracted via deep learning technologies. The building, environmental and social criteria related to roof greening are extracted using technologies such as deep learning, machine learning, remote sensing (RS) methods and geographic information system (GIS) methods. The technique for order preference by similarity to an ideal solution (TOPSIS) method is applied to quantify the suitability of each roof, and Sobol sensitivity analysis of the score results is conducted. The experiment on Xiamen Island shows that the final evaluation results are highly sensitive to the changes in weight of the green space distance, population density and the air pollution level. This framework is helpful for the quantitative and objective development of roof greening suitability evaluation. |
| doi_str_mv | 10.3390/su12114375 |
| format | Article |
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Therefore, it is necessary to evaluate the suitability of roof greening for buildings in cities. However, most current evaluation methods are based on qualitative and conceptual research. In this paper, a methodological framework for roof greening suitability evaluation is proposed based on the basic units of building roofs extracted via deep learning technologies. The building, environmental and social criteria related to roof greening are extracted using technologies such as deep learning, machine learning, remote sensing (RS) methods and geographic information system (GIS) methods. The technique for order preference by similarity to an ideal solution (TOPSIS) method is applied to quantify the suitability of each roof, and Sobol sensitivity analysis of the score results is conducted. The experiment on Xiamen Island shows that the final evaluation results are highly sensitive to the changes in weight of the green space distance, population density and the air pollution level. This framework is helpful for the quantitative and objective development of roof greening suitability evaluation.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su12114375</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Air pollution ; Buildings ; Cooling ; Deep learning ; Evaluation ; Geographic information systems ; Greening ; Land resources ; Learning algorithms ; Monte Carlo simulation ; Neural networks ; Outdoor air quality ; Pollution levels ; Population density ; Rain ; Remote sensing ; Roofing ; Roofs ; Semantics ; Sensitivity analysis ; Sustainability ; Sustainable development ; Teaching methods ; Urban areas ; Urban planning</subject><ispartof>Sustainability, 2020-06, Vol.12 (11), p.4375</ispartof><rights>2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c295t-f2a1de35081d0c32f6f2826ef6dff587ead8b7da29e8c709495015ed43db02413</citedby><cites>FETCH-LOGICAL-c295t-f2a1de35081d0c32f6f2826ef6dff587ead8b7da29e8c709495015ed43db02413</cites><orcidid>0000-0002-0411-5396 ; 0000-0001-8323-2728 ; 0000-0003-0178-2342</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Xu, Nan</creatorcontrib><creatorcontrib>Luo, Jiancheng</creatorcontrib><creatorcontrib>Zuo, Jin</creatorcontrib><creatorcontrib>Hu, Xiaodong</creatorcontrib><creatorcontrib>Dong, Jing</creatorcontrib><creatorcontrib>Wu, Tianjun</creatorcontrib><creatorcontrib>Wu, Songliang</creatorcontrib><creatorcontrib>Liu, Hao</creatorcontrib><title>Accurate Suitability Evaluation of Large-Scale Roof Greening Based on RS and GIS Methods</title><title>Sustainability</title><description>Under increasingly low urban land resources, carrying out roof greening to exploit new green space is a good strategy for sustainable development. Therefore, it is necessary to evaluate the suitability of roof greening for buildings in cities. However, most current evaluation methods are based on qualitative and conceptual research. In this paper, a methodological framework for roof greening suitability evaluation is proposed based on the basic units of building roofs extracted via deep learning technologies. The building, environmental and social criteria related to roof greening are extracted using technologies such as deep learning, machine learning, remote sensing (RS) methods and geographic information system (GIS) methods. The technique for order preference by similarity to an ideal solution (TOPSIS) method is applied to quantify the suitability of each roof, and Sobol sensitivity analysis of the score results is conducted. The experiment on Xiamen Island shows that the final evaluation results are highly sensitive to the changes in weight of the green space distance, population density and the air pollution level. 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| subjects | Air pollution Buildings Cooling Deep learning Evaluation Geographic information systems Greening Land resources Learning algorithms Monte Carlo simulation Neural networks Outdoor air quality Pollution levels Population density Rain Remote sensing Roofing Roofs Semantics Sensitivity analysis Sustainability Sustainable development Teaching methods Urban areas Urban planning |
| title | Accurate Suitability Evaluation of Large-Scale Roof Greening Based on RS and GIS Methods |
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