Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images

Urban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-05, Vol.15 (10), p.2562
Main Authors: Shao, Zhenfeng, Cheng, Tao, Fu, Huyan, Li, Deren, Huang, Xiao
Format: Article
Language:English
Subjects:
Cities
Climate change
Climatic changes
Deep learning
Environmental changes
High resolution
Hydrology
Image resolution
impervious surface estimation
Mapping
Meteorological satellites
Occlusion
Permeability
Population growth
Remote sensing
Satellite imagery
Spatial discrimination
Spatial resolution
Sustainable development
Urban areas
Urban development
urban mapping issues
Urbanization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c400t-87dd6738cabaeaedc29c7f88fc4219fd3683cfb9bff4e195a9ba68cf5bbd3be63
cites cdi_FETCH-LOGICAL-c400t-87dd6738cabaeaedc29c7f88fc4219fd3683cfb9bff4e195a9ba68cf5bbd3be63
container_end_page
container_issue 10
container_start_page 2562
container_title Remote sensing (Basel, Switzerland)
container_volume 15
creator Shao, Zhenfeng
Cheng, Tao
Fu, Huyan
Li, Deren
Huang, Xiao
description Urban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface mapping from high-resolution remote sensing imagery. In this paper, we compare current extraction methods in terms of extraction units and extraction models and summarize their strengths and limitations. We discuss the challenges in impervious surface estimation from high spatial resolution remote sensing imagery in terms of selection of spatial resolution, spectral band, and extraction method. The uncertainties caused by clouds and snow, shadows, and vegetation occlusion are also analyzed. Automated sample labeling and remote sensing domain knowledge are the main directions in impervious surface extraction using deep learning methods. We should also focus on using continuous time series of high-resolution imagery and multi-source satellite imagery for dynamic monitoring of impervious surfaces.
doi_str_mv 10.3390/rs15102562
format article
fullrecord <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_e0f3999603bb478da0f10a743ac8eab8</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A752658019</galeid><doaj_id>oai_doaj_org_article_e0f3999603bb478da0f10a743ac8eab8</doaj_id><sourcerecordid>A752658019</sourcerecordid><originalsourceid>FETCH-LOGICAL-c400t-87dd6738cabaeaedc29c7f88fc4219fd3683cfb9bff4e195a9ba68cf5bbd3be63</originalsourceid><addsrcrecordid>eNpNUU1r3DAQNaWFLsle-gsMuRWc6MMf0nEJaWNICOx2z-pIHjla1pYr2YX--2p3S5qZwwxv3nsMM1n2hZJbziW5C5FWlLCqZh-yFSMNK0om2cd3_edsHeOBpOCcSlKusp8PA4bejX3exrhgzN2YP8M0nZB90DDm7TBh-O38EvPdEiyYRNrH0_zR9a_FFqM_LrPzY77Fwc-Y73A8j9sBeozX2ScLx4jrf_Uq2397-HH_WDy9fG_vN0-FKQmZC9F0Xd1wYUADAnaGSdNYIawpGZW247XgxmqprS2RygqkhloYW2ndcY01v8rai2_n4aCm4AYIf5QHp86AD72CMDtzRIXEcillTbjWZSM6IJYSaEoORiBokbxuLl5T8L_SUWZ18EsY0_qKCSpLwQg7sW4vrB6SqRutnwOYlB0OzvgRrUv4pqlYXQlCZRJ8vQhM8DEGtG9rUqJOH1T_P8j_AlGfjsk</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2819482028</pqid></control><display><type>article</type><title>Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images</title><source>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</source><creator>Shao, Zhenfeng ; Cheng, Tao ; Fu, Huyan ; Li, Deren ; Huang, Xiao</creator><creatorcontrib>Shao, Zhenfeng ; Cheng, Tao ; Fu, Huyan ; Li, Deren ; Huang, Xiao</creatorcontrib><description>Urban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface mapping from high-resolution remote sensing imagery. In this paper, we compare current extraction methods in terms of extraction units and extraction models and summarize their strengths and limitations. We discuss the challenges in impervious surface estimation from high spatial resolution remote sensing imagery in terms of selection of spatial resolution, spectral band, and extraction method. The uncertainties caused by clouds and snow, shadows, and vegetation occlusion are also analyzed. Automated sample labeling and remote sensing domain knowledge are the main directions in impervious surface extraction using deep learning methods. We should also focus on using continuous time series of high-resolution imagery and multi-source satellite imagery for dynamic monitoring of impervious surfaces.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs15102562</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Cities ; Climate change ; Climatic changes ; Deep learning ; Environmental changes ; High resolution ; Hydrology ; Image resolution ; impervious surface estimation ; Mapping ; Meteorological satellites ; Occlusion ; Permeability ; Population growth ; Remote sensing ; Satellite imagery ; Spatial discrimination ; Spatial resolution ; Sustainable development ; Urban areas ; Urban development ; urban mapping issues ; Urbanization</subject><ispartof>Remote sensing (Basel, Switzerland), 2023-05, Vol.15 (10), p.2562</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 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 (https://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-c400t-87dd6738cabaeaedc29c7f88fc4219fd3683cfb9bff4e195a9ba68cf5bbd3be63</citedby><cites>FETCH-LOGICAL-c400t-87dd6738cabaeaedc29c7f88fc4219fd3683cfb9bff4e195a9ba68cf5bbd3be63</cites><orcidid>0000-0002-2113-0280 ; 0000-0002-4323-382X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2819482028/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2819482028?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Shao, Zhenfeng</creatorcontrib><creatorcontrib>Cheng, Tao</creatorcontrib><creatorcontrib>Fu, Huyan</creatorcontrib><creatorcontrib>Li, Deren</creatorcontrib><creatorcontrib>Huang, Xiao</creatorcontrib><title>Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images</title><title>Remote sensing (Basel, Switzerland)</title><description>Urban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface mapping from high-resolution remote sensing imagery. In this paper, we compare current extraction methods in terms of extraction units and extraction models and summarize their strengths and limitations. We discuss the challenges in impervious surface estimation from high spatial resolution remote sensing imagery in terms of selection of spatial resolution, spectral band, and extraction method. The uncertainties caused by clouds and snow, shadows, and vegetation occlusion are also analyzed. Automated sample labeling and remote sensing domain knowledge are the main directions in impervious surface extraction using deep learning methods. We should also focus on using continuous time series of high-resolution imagery and multi-source satellite imagery for dynamic monitoring of impervious surfaces.</description><subject>Cities</subject><subject>Climate change</subject><subject>Climatic changes</subject><subject>Deep learning</subject><subject>Environmental changes</subject><subject>High resolution</subject><subject>Hydrology</subject><subject>Image resolution</subject><subject>impervious surface estimation</subject><subject>Mapping</subject><subject>Meteorological satellites</subject><subject>Occlusion</subject><subject>Permeability</subject><subject>Population growth</subject><subject>Remote sensing</subject><subject>Satellite imagery</subject><subject>Spatial discrimination</subject><subject>Spatial resolution</subject><subject>Sustainable development</subject><subject>Urban areas</subject><subject>Urban development</subject><subject>urban mapping issues</subject><subject>Urbanization</subject><issn>2072-4292</issn><issn>2072-4292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNUU1r3DAQNaWFLsle-gsMuRWc6MMf0nEJaWNICOx2z-pIHjla1pYr2YX--2p3S5qZwwxv3nsMM1n2hZJbziW5C5FWlLCqZh-yFSMNK0om2cd3_edsHeOBpOCcSlKusp8PA4bejX3exrhgzN2YP8M0nZB90DDm7TBh-O38EvPdEiyYRNrH0_zR9a_FFqM_LrPzY77Fwc-Y73A8j9sBeozX2ScLx4jrf_Uq2397-HH_WDy9fG_vN0-FKQmZC9F0Xd1wYUADAnaGSdNYIawpGZW247XgxmqprS2RygqkhloYW2ndcY01v8rai2_n4aCm4AYIf5QHp86AD72CMDtzRIXEcillTbjWZSM6IJYSaEoORiBokbxuLl5T8L_SUWZ18EsY0_qKCSpLwQg7sW4vrB6SqRutnwOYlB0OzvgRrUv4pqlYXQlCZRJ8vQhM8DEGtG9rUqJOH1T_P8j_AlGfjsk</recordid><startdate>20230501</startdate><enddate>20230501</enddate><creator>Shao, Zhenfeng</creator><creator>Cheng, Tao</creator><creator>Fu, Huyan</creator><creator>Li, Deren</creator><creator>Huang, Xiao</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-2113-0280</orcidid><orcidid>https://orcid.org/0000-0002-4323-382X</orcidid></search><sort><creationdate>20230501</creationdate><title>Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images</title><author>Shao, Zhenfeng ; Cheng, Tao ; Fu, Huyan ; Li, Deren ; Huang, Xiao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c400t-87dd6738cabaeaedc29c7f88fc4219fd3683cfb9bff4e195a9ba68cf5bbd3be63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Cities</topic><topic>Climate change</topic><topic>Climatic changes</topic><topic>Deep learning</topic><topic>Environmental changes</topic><topic>High resolution</topic><topic>Hydrology</topic><topic>Image resolution</topic><topic>impervious surface estimation</topic><topic>Mapping</topic><topic>Meteorological satellites</topic><topic>Occlusion</topic><topic>Permeability</topic><topic>Population growth</topic><topic>Remote sensing</topic><topic>Satellite imagery</topic><topic>Spatial discrimination</topic><topic>Spatial resolution</topic><topic>Sustainable development</topic><topic>Urban areas</topic><topic>Urban development</topic><topic>urban mapping issues</topic><topic>Urbanization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shao, Zhenfeng</creatorcontrib><creatorcontrib>Cheng, Tao</creatorcontrib><creatorcontrib>Fu, Huyan</creatorcontrib><creatorcontrib>Li, Deren</creatorcontrib><creatorcontrib>Huang, Xiao</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric &amp; Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Earth, Atmospheric &amp; Aquatic Science Database</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Directory of Open Access Journals</collection><jtitle>Remote sensing (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shao, Zhenfeng</au><au>Cheng, Tao</au><au>Fu, Huyan</au><au>Li, Deren</au><au>Huang, Xiao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images</atitle><jtitle>Remote sensing (Basel, Switzerland)</jtitle><date>2023-05-01</date><risdate>2023</risdate><volume>15</volume><issue>10</issue><spage>2562</spage><pages>2562-</pages><issn>2072-4292</issn><eissn>2072-4292</eissn><abstract>Urban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface mapping from high-resolution remote sensing imagery. In this paper, we compare current extraction methods in terms of extraction units and extraction models and summarize their strengths and limitations. We discuss the challenges in impervious surface estimation from high spatial resolution remote sensing imagery in terms of selection of spatial resolution, spectral band, and extraction method. The uncertainties caused by clouds and snow, shadows, and vegetation occlusion are also analyzed. Automated sample labeling and remote sensing domain knowledge are the main directions in impervious surface extraction using deep learning methods. We should also focus on using continuous time series of high-resolution imagery and multi-source satellite imagery for dynamic monitoring of impervious surfaces.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/rs15102562</doi><orcidid>https://orcid.org/0000-0002-2113-0280</orcidid><orcidid>https://orcid.org/0000-0002-4323-382X</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2072-4292
ispartof Remote sensing (Basel, Switzerland), 2023-05, Vol.15 (10), p.2562
issn 2072-4292
2072-4292
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_e0f3999603bb478da0f10a743ac8eab8
source Publicly Available Content Database (Proquest) (PQ_SDU_P3)
subjects Cities
Climate change
Climatic changes
Deep learning
Environmental changes
High resolution
Hydrology
Image resolution
impervious surface estimation
Mapping
Meteorological satellites
Occlusion
Permeability
Population growth
Remote sensing
Satellite imagery
Spatial discrimination
Spatial resolution
Sustainable development
Urban areas
Urban development
urban mapping issues
Urbanization
title Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T02%3A09%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Emerging%20Issues%20in%20Mapping%20Urban%20Impervious%20Surfaces%20Using%20High-Resolution%20Remote%20Sensing%20Images&rft.jtitle=Remote%20sensing%20(Basel,%20Switzerland)&rft.au=Shao,%20Zhenfeng&rft.date=2023-05-01&rft.volume=15&rft.issue=10&rft.spage=2562&rft.pages=2562-&rft.issn=2072-4292&rft.eissn=2072-4292&rft_id=info:doi/10.3390/rs15102562&rft_dat=%3Cgale_doaj_%3EA752658019%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c400t-87dd6738cabaeaedc29c7f88fc4219fd3683cfb9bff4e195a9ba68cf5bbd3be63%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2819482028&rft_id=info:pmid/&rft_galeid=A752658019&rfr_iscdi=true