Identifying Streetscape Features Using VHR Imagery and Deep Learning Applications

Deep Learning (DL) based identification and detection of elements in urban spaces through Earth Observation (EO) datasets have been widely researched and discussed. Such studies have developed state-of-the-art methods to map urban features like building footprint or roads in detail. This study delve...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 13; číslo 17; s. 3363
Hlavní autoři:	Verma, Deepank, Mumm, Olaf, Carlow, Vanessa Miriam
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.09.2021
Témata:	Algorithms Braunschweig Buildings Computer vision Corridors data collection Datasets Deep Learning Identification methods Image segmentation Infrastructure Machine learning Neural networks object detection Object recognition Remote sensing road detection Roads & highways semantic segmentation spatial data State-of-the-art reviews streetscape Training Urban areas vegetation
ISSN:	2072-4292, 2072-4292
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Deep Learning (DL) based identification and detection of elements in urban spaces through Earth Observation (EO) datasets have been widely researched and discussed. Such studies have developed state-of-the-art methods to map urban features like building footprint or roads in detail. This study delves deeper into combining multiple such studies to identify fine-grained urban features which define streetscapes. Specifically, the research focuses on employing object detection and semantic segmentation models and other computer vision methods to identify ten streetscape features such as movement corridors, roadways, sidewalks, bike paths, on-street parking, vehicles, trees, vegetation, road markings, and buildings. The training data for identifying and classifying all the elements except road markings are collected from open sources and finetuned to fit the study’s context. The training dataset is manually created and employed to delineate road markings. Apart from the model-specific evaluation on the test-set of the data, the study creates its own test dataset from the study area to analyze these models’ performance. The outputs from these models are further integrated to develop a geospatial dataset, which is additionally utilized to generate 3D views and street cross-sections for the city. The trained models and data sources are discussed in the research and are made available for urban researchers to exploit.
AbstractList	Deep Learning (DL) based identification and detection of elements in urban spaces through Earth Observation (EO) datasets have been widely researched and discussed. Such studies have developed state-of-the-art methods to map urban features like building footprint or roads in detail. This study delves deeper into combining multiple such studies to identify fine-grained urban features which define streetscapes. Specifically, the research focuses on employing object detection and semantic segmentation models and other computer vision methods to identify ten streetscape features such as movement corridors, roadways, sidewalks, bike paths, on-street parking, vehicles, trees, vegetation, road markings, and buildings. The training data for identifying and classifying all the elements except road markings are collected from open sources and finetuned to fit the study’s context. The training dataset is manually created and employed to delineate road markings. Apart from the model-specific evaluation on the test-set of the data, the study creates its own test dataset from the study area to analyze these models’ performance. The outputs from these models are further integrated to develop a geospatial dataset, which is additionally utilized to generate 3D views and street cross-sections for the city. The trained models and data sources are discussed in the research and are made available for urban researchers to exploit.
Author	Carlow, Vanessa Miriam Mumm, Olaf Verma, Deepank
Author_xml	– sequence: 1 givenname: Deepank surname: Verma fullname: Verma, Deepank – sequence: 2 givenname: Olaf orcidid: 0000-0001-6628-7874 surname: Mumm fullname: Mumm, Olaf – sequence: 3 givenname: Vanessa Miriam orcidid: 0000-0003-0513-9717 surname: Carlow fullname: Carlow, Vanessa Miriam
BookMark	eNptkU9r3DAQxUVJoWmaSz-BoZdS2FbS2JJ8DGnSLCyU_kmvYiyPFy1eyZW0h_32tbOlCaG6SMz89Hjz5jU7CzEQY28F_wjQ8k8pCxAaQMELdi65lqtatvLsyfsVu8x5x-cDIFpen7Nv655C8cPRh231oySikh1OVN0SlkOiXN3npfXr7nu13uOW0rHC0FefiaZqQ5jC0r2aptE7LD6G_Ia9HHDMdPn3vmD3tzc_r-9Wm69f1tdXm5WDti4rZVANAsHM3lBo0XMHQ9cY6IypleYKQHeDkqoTqjMIvQA16Bak61vTKw0XbH3S7SPu7JT8HtPRRvT2oRDT1mIq3o1kO9kYLqWjWpkahDK8U67Vg-oUSG7crPX-pDWl-PtAudi9z47GEQPFQ7ZSgWpgsTWj756hu3hIYZ7UykaLZs62Wcx9OFEuxZwTDf8MCm6XZdnHZc0wfwY7Xx7CLAn9-L8vfwC4sZT9
CitedBy_id	crossref_primary_10_1111_mice_13277 crossref_primary_10_1080_10106049_2024_2364679 crossref_primary_10_3389_fbuil_2023_1216888 crossref_primary_10_1016_j_compenvurbsys_2024_102087 crossref_primary_10_1016_j_scs_2023_104673 crossref_primary_10_1016_j_scs_2023_105047 crossref_primary_10_1177_23998083241261100 crossref_primary_10_3390_su15065547
Cites_doi	10.1109/TPAMI.2018.2858826 10.1080/00330124.2015.1065546 10.1109/CVPR.2012.6248074 10.1109/DICTA.2011.90 10.1109/CVPRW.2018.00031 10.1068/b060047 10.1016/0169-2046(87)90017-X 10.1016/j.healthplace.2018.07.001 10.1016/j.polgeo.2014.06.004 10.1016/j.amepre.2010.09.034 10.1109/ICCV.2019.00749 10.1007/978-3-319-46448-0_12 10.1109/VCIP.2017.8305148 10.1007/s11524-015-9982-z 10.1559/152304081784447318 10.1109/WACV.2017.90 10.1109/ICCV.2017.372 10.1068/b34144t 10.1101/532952 10.1109/CVPR.2018.00496 10.1109/IGARSS.2018.8518701 10.4324/9781315554464 10.1109/CVPR.2016.350 10.1080/01431161.2015.1054049 10.1016/j.jvcir.2015.11.002 10.1007/978-3-319-24574-4_28 10.1177/0361198119837194 10.1177/0022002184015001005 10.1016/0034-4257(87)90053-8 10.1016/j.geog.2018.11.008 10.1080/14036096.2020.1724193 10.1016/S0272-4944(05)80063-5 10.1109/TGRS.2018.2878510 10.1007/s10940-021-09506-9 10.1109/IVS.2015.7225753 10.1007/s10844-019-00587-4 10.1016/j.landurbplan.2016.07.003 10.1109/CVPRW.2018.00034 10.2352/ISSN.2470-1173.2016.10.ROBVIS-392 10.1080/713683961 10.1186/1471-2458-14-1094 10.1016/j.jenvp.2012.09.003 10.1016/j.socscimed.2014.06.042 10.3390/rs10091419 10.1007/978-3-030-01234-2_49 10.1109/IGARSS.2019.8900453 10.1016/j.landurbplan.2018.09.020 10.1109/CVPR.2016.352 10.3390/rs12142225 10.1109/BigDataCongress.2018.00014 10.1371/journal.pone.0247535 10.1016/j.buildenv.2020.107340 10.1109/CVPRW.2014.121 10.3390/rs12071128 10.1007/s11263-014-0733-5 10.1016/j.socscimed.2013.06.030 10.1177/0265813516686971 10.1007/s11524-010-9505-x 10.1186/s12889-020-8300-1
ContentType	Journal Article
Copyright	2021 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.
Copyright_xml	– notice: 2021 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.
DBID	AAYXX CITATION 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD 8FE 8FG ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F28 FR3 H8D H8G HCIFZ JG9 JQ2 KR7 L6V L7M L~C L~D M7S P5Z P62 P64 PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7S9 L.6 DOA
DOI	10.3390/rs13173363
DatabaseName	CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Ecology Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC ProQuest Central ProQuest Technology Collection Natural Science Collection Earth, Atmospheric & Aquatic Science Environmental Sciences and Pollution Management ProQuest One ProQuest Central ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest One Academic ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection AGRICOLA AGRICOLA - Academic DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database Materials Research Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China Materials Business File Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences Engineered Materials Abstracts Natural Science Collection Chemoreception Abstracts ProQuest Central (New) Engineering Collection ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Engineering Database Aluminium Industry Abstracts ProQuest One Academic Eastern Edition Electronics & Communications Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection Ceramic Abstracts Ecology Abstracts Biotechnology and BioEngineering Abstracts ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Central (Alumni Edition) ProQuest One Community College Earth, Atmospheric & Aquatic Science Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Engineering Collection Biotechnology Research Abstracts ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Materials Science & Engineering Collection Corrosion Abstracts AGRICOLA AGRICOLA - Academic
DatabaseTitleList	CrossRef Publicly Available Content Database AGRICOLA
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography
EISSN	2072-4292
ExternalDocumentID	oai_doaj_org_article_b258022ce468431680b6c97f6b63208c 10_3390_rs13173363
GroupedDBID	29P 2WC 2XV 5VS 8FE 8FG 8FH AADQD AAHBH AAYXX ABDBF ABJCF ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ BHPHI BKSAR CCPQU CITATION E3Z ESX FRP GROUPED_DOAJ HCIFZ I-F IAO ITC KQ8 L6V LK5 M7R M7S MODMG M~E OK1 P62 PCBAR PHGZM PHGZT PIMPY PQGLB PROAC PTHSS TR2 TUS 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD ABUWG AZQEC C1K DWQXO F28 FR3 H8D H8G JG9 JQ2 KR7 L7M L~C L~D P64 PKEHL PQEST PQQKQ PQUKI PRINS 7S9 L.6
ID	FETCH-LOGICAL-c394t-68a6f1a38072a171d0c3fb583b8846706337bf626b16b8a3d136f7932cd98d673
IEDL.DBID	DOA
ISICitedReferencesCount	8
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000694504700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2072-4292
IngestDate	Fri Oct 03 12:39:59 EDT 2025 Sun Nov 09 13:32:38 EST 2025 Fri Jul 25 09:31:00 EDT 2025 Sat Nov 29 07:14:59 EST 2025 Tue Nov 18 22:29:49 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	17
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c394t-68a6f1a38072a171d0c3fb583b8846706337bf626b16b8a3d136f7932cd98d673
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0001-6628-7874 0000-0003-0513-9717
OpenAccessLink	https://doaj.org/article/b258022ce468431680b6c97f6b63208c
PQID	2571500057
PQPubID	2032338
ParticipantIDs	doaj_primary_oai_doaj_org_article_b258022ce468431680b6c97f6b63208c proquest_miscellaneous_2636534670 proquest_journals_2571500057 crossref_primary_10_3390_rs13173363 crossref_citationtrail_10_3390_rs13173363
PublicationCentury	2000
PublicationDate	2021-09-01
PublicationDateYYYYMMDD	2021-09-01
PublicationDate_xml	– month: 09 year: 2021 text: 2021-09-01 day: 01
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	Remote sensing (Basel, Switzerland)
PublicationYear	2021
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	ref_50 Kondo (ref_39) 2017; 157 ref_58 ref_12 ref_55 ref_54 ref_53 ref_52 Rundle (ref_11) 2011; 40 Badland (ref_13) 2010; 87 Wang (ref_33) 2015; 36 ref_51 ref_18 ref_17 Groenewegen (ref_6) 2013; 94 ref_15 ref_59 ref_61 ref_60 Haack (ref_29) 1987; 21 ref_69 Golombek (ref_37) 2019; 2673 ref_68 Abass (ref_5) 2021; 38 Isaacs (ref_8) 2000; 5 ref_66 Rzotkiewicz (ref_16) 2018; 52 ref_65 ref_20 ref_63 Hull (ref_25) 1992; 12 Altman (ref_9) 1976; Volume 1 ref_28 ref_27 ref_26 Azimi (ref_67) 2019; 57 Lindal (ref_2) 2013; 33 Jensen (ref_30) 1981; 8 Neuheuser (ref_72) 2018; 45 Harvey (ref_1) 2016; 68 Badland (ref_14) 2015; 92 Rossetti (ref_21) 2019; 181 Morello (ref_70) 2009; 36 Everingham (ref_57) 2015; 111 Heris (ref_36) 2020; 7 ref_35 ref_34 Nasar (ref_24) 1987; 14 Benedikt (ref_71) 1979; 6 ref_31 Liu (ref_42) 2019; 10 ref_38 Saito (ref_62) 2016; 60 Cain (ref_10) 2014; 116 Razakarivony (ref_32) 2016; 34 Verma (ref_22) 2020; 186 Leibe (ref_46) 2016; Volume 9907 ref_47 ref_45 ref_44 ref_43 ref_41 Zhang (ref_19) 2020; 55 ref_40 ref_3 Drozdzewski (ref_4) 2014; 42 ref_49 He (ref_56) 2015; 77 ref_48 Nasar (ref_23) 1984; 15 Lin (ref_64) 2020; 42 ref_7
References_xml	– volume: 42 start-page: 318 year: 2020 ident: ref_64 article-title: Focal Loss for Dense Object Detection publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2018.2858826 – volume: 68 start-page: 149 year: 2016 ident: ref_1 article-title: Measuring Urban Streetscapes for Livability: A Review of Approaches publication-title: Prof. Geogr. doi: 10.1080/00330124.2015.1065546 – ident: ref_28 doi: 10.1109/CVPR.2012.6248074 – volume: Volume 9907 start-page: 1 year: 2016 ident: ref_46 article-title: A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning publication-title: Computer Vision—ECCV 2016 – ident: ref_43 doi: 10.1109/DICTA.2011.90 – ident: ref_59 doi: 10.1109/CVPRW.2018.00031 – volume: 6 start-page: 47 year: 1979 ident: ref_71 article-title: To take hold of space: Isovists and isovist fields publication-title: Environ. Plan. B Plan. Des. doi: 10.1068/b060047 – volume: 14 start-page: 117 year: 1987 ident: ref_24 article-title: Environmental correlates of evaluative appraisals of central business district scenes publication-title: Landsc. Urban Plan. doi: 10.1016/0169-2046(87)90017-X – volume: 52 start-page: 240 year: 2018 ident: ref_16 article-title: Systematic review of the use of Google Street View in health research: Major themes, strengths, weaknesses and possibilities for future research publication-title: Health Place doi: 10.1016/j.healthplace.2018.07.001 – volume: 42 start-page: 66 year: 2014 ident: ref_4 article-title: Using history in the streetscape to affirm geopolitics of memory publication-title: Polit. Geogr. doi: 10.1016/j.polgeo.2014.06.004 – volume: 40 start-page: 94 year: 2011 ident: ref_11 article-title: Using google street view to audit neighborhood environments publication-title: Am. J. Prev. Med. doi: 10.1016/j.amepre.2010.09.034 – ident: ref_68 doi: 10.1109/ICCV.2019.00749 – ident: ref_20 doi: 10.1007/978-3-319-46448-0_12 – ident: ref_58 doi: 10.1109/VCIP.2017.8305148 – ident: ref_31 – volume: 92 start-page: 923 year: 2015 ident: ref_14 article-title: Assessing Walking and Cycling Environments in the Streets of Madrid: Comparing On-Field and Virtual Audits publication-title: J. Urban Health doi: 10.1007/s11524-015-9982-z – ident: ref_48 – ident: ref_69 – volume: 8 start-page: 127 year: 1981 ident: ref_30 article-title: Urban change detection mapping using landsat digital data publication-title: Am. Cartogr. doi: 10.1559/152304081784447318 – ident: ref_66 doi: 10.1109/WACV.2017.90 – ident: ref_50 doi: 10.1109/ICCV.2017.372 – volume: 36 start-page: 837 year: 2009 ident: ref_70 article-title: A digital image of the city: 3D isovists in Lynch’s urban analysis publication-title: Environ. Plan. B Plan. Des. doi: 10.1068/b34144t – ident: ref_38 – ident: ref_45 – ident: ref_41 doi: 10.1101/532952 – ident: ref_49 doi: 10.1109/CVPR.2018.00496 – ident: ref_61 doi: 10.1109/IGARSS.2018.8518701 – ident: ref_3 doi: 10.4324/9781315554464 – ident: ref_26 doi: 10.1109/CVPR.2016.350 – volume: 36 start-page: 3144 year: 2015 ident: ref_33 article-title: Road network extraction: A neural-dynamic framework based on deep learning and a finite state machine publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2015.1054049 – volume: 34 start-page: 187 year: 2016 ident: ref_32 article-title: Vehicle detection in aerial imagery: A small target detection benchmark publication-title: J. Vis. Commun. Image Represent. doi: 10.1016/j.jvcir.2015.11.002 – ident: ref_51 doi: 10.1007/978-3-319-24574-4_28 – volume: 2673 start-page: 125 year: 2019 ident: ref_37 article-title: Use of Aerial LiDAR in Measuring Streetscape and Street Trees publication-title: Transp. Res. Rec. doi: 10.1177/0361198119837194 – volume: Volume 1 start-page: 37 year: 1976 ident: ref_9 article-title: Environmental Aesthetics: The Environment as a Source of Affect publication-title: Human Behavior and Environment: Advances in Theory and Research – ident: ref_47 – volume: 15 start-page: 79 year: 1984 ident: ref_23 article-title: Visual Preferences in Urban Street Scenes publication-title: J. Cross. Cult. Psychol. doi: 10.1177/0022002184015001005 – volume: 21 start-page: 201 year: 1987 ident: ref_29 article-title: An assessment of landsat MSS and TM data for urban and near-urban land-cover digital classification publication-title: Remote Sens. Environ. doi: 10.1016/0034-4257(87)90053-8 – volume: 10 start-page: 10 year: 2019 ident: ref_42 article-title: An automatic method for road centerline extraction from post-earthquake aerial images publication-title: Geod. Geodyn. doi: 10.1016/j.geog.2018.11.008 – volume: 38 start-page: 204 year: 2021 ident: ref_5 article-title: Talk on the Street: The Impact of Good Streetscape Design on Neighbourhood Experience in Low-density Suburbs publication-title: Hous. Theory Soc. doi: 10.1080/14036096.2020.1724193 – volume: 12 start-page: 101 year: 1992 ident: ref_25 article-title: Validity of Photo-Based Scenic Beauty Judgements publication-title: J. Environ. Psychol. doi: 10.1016/S0272-4944(05)80063-5 – volume: 57 start-page: 2920 year: 2019 ident: ref_67 article-title: Aerial LaneNet: Lane-Marking Semantic Segmentation in Aerial Imagery Using Wavelet-Enhanced Cost-Sensitive Symmetric Fully Convolutional Neural Networks publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2878510 – ident: ref_18 doi: 10.1007/s10940-021-09506-9 – ident: ref_65 doi: 10.1109/IVS.2015.7225753 – volume: 55 start-page: 535 year: 2020 ident: ref_19 article-title: Analysis of street crime predictors in web open data publication-title: J. Intell. Inf. Syst. doi: 10.1007/s10844-019-00587-4 – volume: 157 start-page: 193 year: 2017 ident: ref_39 article-title: Landscape and Urban Planning The association between urban trees and crime: Evidence from the spread of the emerald ash borer in Cincinnati publication-title: Landsc. Urban Plan. doi: 10.1016/j.landurbplan.2016.07.003 – ident: ref_44 – ident: ref_53 doi: 10.1109/CVPRW.2018.00034 – volume: 60 start-page: 1 year: 2016 ident: ref_62 article-title: Multiple object extraction from aerial imagery with convolutional neural networks publication-title: Electron. Imaging doi: 10.2352/ISSN.2470-1173.2016.10.ROBVIS-392 – volume: 5 start-page: 145 year: 2000 ident: ref_8 article-title: The Urban Picturesque: An Aesthetic Experience of Urban Pedestrian Places publication-title: J. Urban Des. doi: 10.1080/713683961 – volume: 77 start-page: 10437 year: 2015 ident: ref_56 article-title: Deep Residual Learning for Image Recognition publication-title: Multimed. Tools Appl. – ident: ref_7 doi: 10.1186/1471-2458-14-1094 – volume: 33 start-page: 26 year: 2013 ident: ref_2 article-title: Architectural variation, building height, and the restorative quality of urban residential streetscapes publication-title: J. Environ. Psychol. doi: 10.1016/j.jenvp.2012.09.003 – volume: 116 start-page: 82 year: 2014 ident: ref_10 article-title: Contribution of streetscape audits to explanation of physical activity in four age groups based on the Microscale Audit of Pedestrian Streetscapes (MAPS) publication-title: Soc. Sci. Med. doi: 10.1016/j.socscimed.2014.06.042 – ident: ref_40 doi: 10.3390/rs10091419 – ident: ref_52 doi: 10.1007/978-3-030-01234-2_49 – ident: ref_54 doi: 10.1109/IGARSS.2019.8900453 – ident: ref_12 – volume: 181 start-page: 169 year: 2019 ident: ref_21 article-title: Explaining subjective perceptions of public spaces as a function of the built environment: A massive data approach publication-title: Landsc. Urban Plan. doi: 10.1016/j.landurbplan.2018.09.020 – ident: ref_27 doi: 10.1109/CVPR.2016.352 – ident: ref_34 doi: 10.3390/rs12142225 – ident: ref_63 doi: 10.1109/BigDataCongress.2018.00014 – volume: 7 start-page: 1 year: 2020 ident: ref_36 article-title: A rasterized building footprint dataset for the United States publication-title: Sci. Data – ident: ref_35 doi: 10.1371/journal.pone.0247535 – volume: 186 start-page: 107340 year: 2020 ident: ref_22 article-title: Predicting human perception of the urban environment in a spatiotemporal urban setting using locally acquired street view images and audio clips publication-title: Build. Environ. doi: 10.1016/j.buildenv.2020.107340 – ident: ref_15 doi: 10.1109/CVPRW.2014.121 – ident: ref_55 doi: 10.3390/rs12071128 – volume: 111 start-page: 98 year: 2015 ident: ref_57 article-title: The Pascal Visual Object Classes Challenge: A Retrospective publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-014-0733-5 – volume: 94 start-page: 26 year: 2013 ident: ref_6 article-title: Streetscape greenery and health: Stress, social cohesion and physical activity as mediators publication-title: Soc. Sci. Med. doi: 10.1016/j.socscimed.2013.06.030 – ident: ref_60 – volume: 45 start-page: 797 year: 2018 ident: ref_72 article-title: A tool to predict perceived urban stress in open public spaces publication-title: Environ. Plan. B Urban Anal. City Sci. doi: 10.1177/0265813516686971 – volume: 87 start-page: 1007 year: 2010 ident: ref_13 article-title: Can virtual streetscape audits reliably replace physical streetscape audits? publication-title: J. Urban Health doi: 10.1007/s11524-010-9505-x – ident: ref_17 doi: 10.1186/s12889-020-8300-1
SSID	ssj0000331904
Score	2.342121
Snippet	Deep Learning (DL) based identification and detection of elements in urban spaces through Earth Observation (EO) datasets have been widely researched and...
SourceID	doaj proquest crossref
SourceType	Open Website Aggregation Database Enrichment Source Index Database
StartPage	3363
SubjectTerms	Algorithms Braunschweig Buildings Computer vision Corridors data collection Datasets Deep Learning Identification methods Image segmentation Infrastructure Machine learning Neural networks object detection Object recognition Remote sensing road detection Roads & highways semantic segmentation spatial data State-of-the-art reviews streetscape Training Urban areas vegetation
SummonAdditionalLinks	– databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9NAEB5BigQXHm0RgYAWlUsPVr1ee717Qg20KpeoVKXqzfK-UiRwQpwg9d8zY2_SolZcuNpra7Xz3pn5BuCDthmXLrWJ1sYkuQqoB2vFE20tmjtODQn9sIlyMlGXl_o0Xri1saxyrRM7Re1mlu7ID5C1OIH3F-XH-a-EpkZRdjWO0HgIW4RUlg9ga3w0OT3b3LKkAlkszXtcUoHx_cGi5WgyhZDiL0vUAfbf0cedkTl-9r_bew5Po3vJDnt-eAEPfLMNj-Ok86vrHfjad-Z23U2sT0m3VAPFyBdcYezNuiICdnFyxr78JICLa1Y3jn32fs4iGOuUHd5Ke-_Ct-Oj808nSRyrkFih82UiVS0DrwlpPqt5yZFSIphCCaPIGUGfRZQmYKBjuDSqFo4LGVCMM-u0crIUL2HQzBr_CpjHT2QuVWEEBmqFMk57F4K1SgeZOzWE_fURVzZijtPoix8Vxh5EjuqGHEPY26yd90gb964aE6U2Kwgdu3swW0yrKGyVyQrqILYet0at_io10uoySCNFlio7hNGaiFUU2ba6oeAQ3m9eo7BRBqVu_GyFa6SQhaBDev3vX7yBJxkVv3TFaCMYLBcr_xYe2d_L7-3iXeTSP-Yu73E priority: 102 providerName: ProQuest
Title	Identifying Streetscape Features Using VHR Imagery and Deep Learning Applications
URI	https://www.proquest.com/docview/2571500057 https://www.proquest.com/docview/2636534670 https://doaj.org/article/b258022ce468431680b6c97f6b63208c
Volume	13
WOSCitedRecordID	wos000694504700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: DOA dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M~E dateStart: 20090101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: P5Z dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PCBAR dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M7S dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: BENPR dateStart: 20090301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PIMPY dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT-QwDLYQIC0XxGNXDI9REFw4VEyaNk2OPAbBgVF57IrdS9WkCSBBQfNA4sJvx27LMGiRuHDJoXGlyE78kO3PANvahlwWHRtobUwQKY96MFc80NaiuePUkFAPm0h6PXV1pdOJUV9UE1bDA9eM2zVhTN2g1kVSUdu26hhpdeKlkSLsKEvaF72eiWCq0sECr1YnqvFIBcb1u_0BR1MphBQfLFAF1P-fHq6My9ECzDdeIdurT7MIU65cgh_NgPKb52U4qxtqq6YkVmeSB1S6xMiFG2HIzKrcP_tzfM5O7gmX4pnlZcEOnXtkDYbqNdubyFb_hN9H3cuD46CZhhBYoaNhIFUuPc8JID7MecKRwcKbWAmjyIdAV0MkxmN8Yrg0KhcFF9Lj6wttoVUhE_ELpsuH0q0Ac_iLRH7GRmB8FStTaFd4b63SXkaFasHOG4cy20CF08SKuwxDBuJm9s7NFmyNaR9rgIxPqfaJ0WMKArWuPqCos0bU2VeibsH6m5iy5qUNMlQ5nIY6xEkLNsfb-EYo8ZGX7mGENFLIWBCTVr_jHGswF1JlS1Vptg7Tw_7IbcCsfRreDvptmNnv9tLzdnUh21RLekHrSxfXNP6H--nJafr3Ffoe5Tg
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLVK58EYsFDACDhyixnHi2AeECqXaVdvVggoqpxA_UpAguyS7oP1T_EZm8tiCQNx64JpMrMTzZWbsGX8D8FjbiEsX2kBrY4JYFWgHc8UDbS26O04HEtpmE-lkok5O9HQDfvRnYaissreJjaF2M0t75DsILU7k_Un6fP41oK5RlF3tW2i0sDjwq--4ZKufjfdQv0-iaP_V8ctR0HUVCKzQ8SKQKpcFz4loPcp5yvFFRWESJYwiX4wuW6SmwDjfcGlULhwXskAUR9Zp5WQqcNwLsBkj2NUANqfjo-n79a5OKBDSYdzyoAqhw52q5uiihZDiN8_XNAj4w_43Tm3_yv82HVfhchc-s90W79dgw5fXYavr5P5xdQNetyePm9NbrE2511TjxSjWXVa-Zk2RBHs3esPGX4jAY8Xy0rE97-esI5s9Zbu_pPVvwttz-aJbMChnpb8NzOMjMpYqMQIXookyTntXFNYqXcjYqSE87VWa2Y5TnVp7fM5wbUXqz87UP4RHa9l5yyTyV6kXhIy1BLF_Nxdm1WnWGZPMIPQw9rIeX42oDFRopNVpIY0UUajsELZ70GSdSaqzM8QM4eH6NhoTyhDlpZ8tUUYKmQiapDv_HuIBbI2Ojw6zw_Hk4C5ciqjQpym824bBolr6e3DRflt8qqv73R_C4MN5o_AnallJsg
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3db9MwED-NDgEvfCMKA4yABx6ixXHq2A8IdSvVqqGqTID2FmLH3iZBWpIW1H-Nv467fHQgEG974DW5WI79853tu_sdwHNtIy7z0AZaGxPEyqMezBQPtLVo7jglJDTFJpLpVB0f69kW_OhyYSisstOJtaLO55buyHcRWpzI-_EA79uwiNlo_HrxNaAKUuRp7cppNBA5dOvveHyrXk1GONcvomj85v3-QdBWGAis0PEykCqTnmdEuh5lPOHYaeHNQAmjyC6j-RaJ8bjnN1walYmcC-kR0ZHNtcplIrDdS7CtUDTqwfZsf294tLnhCQXCO4wbTlQhdLhbVhzNtRBS_GYF62IBf9iC2sCNb_zPQ3MTrrfbajZs1sEt2HLFbbjaVng_Xd-Bd01Gcp3VxRpXfEWxX4z2wKvSVawOnmAfD47Y5AsRe6xZVuRs5NyCtSS0J2z4i7v_Lny4kD-6B71iXrj7wBx-ImOpBkbgAXWgTK5d7r21SnsZ56oPL7vpTW3LtU4lPz6neOYiKKTnUOjDs43somEY-avUHqFkI0Gs4PWDeXmStkomNdGAMqetw64RxYEKjbQ68dJIEYXK9mGnA1DaqqoqPUdPH55uXqOSIc9RVrj5CmWkkANBg_Tg3008gSsIvfTtZHr4EK5FFP9Tx-PtQG9ZrtwjuGy_Lc-q8nG7WBh8umgQ_gRWclIi
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Identifying+Streetscape+Features+Using+VHR+Imagery+and+Deep+Learning+Applications&rft.jtitle=Remote+sensing+%28Basel%2C+Switzerland%29&rft.au=Deepank+Verma&rft.au=Olaf+Mumm&rft.au=Vanessa+Miriam+Carlow&rft.date=2021-09-01&rft.pub=MDPI+AG&rft.eissn=2072-4292&rft.volume=13&rft.issue=17&rft.spage=3363&rft_id=info:doi/10.3390%2Frs13173363&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_b258022ce468431680b6c97f6b63208c
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2072-4292&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2072-4292&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2072-4292&client=summon