File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: The Coherence and Divergence Between the Objective and Subjective Measurement of Street Perceptions for Shanghai

TitleThe Coherence and Divergence Between the Objective and Subjective Measurement of Street Perceptions for Shanghai
Authors
KeywordsCoherence and divergence
Human perceptions
Machine learning
Street view imagery
Subjective and objective
Issue Date2022
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, v. 13725 LNAI, p. 244-256 How to Cite?
AbstractRecent development in Street View Imagery (SVI), Computer Vision (CV) and Machine Learning (ML) has allowed scholars to quantitatively measure human perceived street characteristics and perceptions at an unprecedented scale. Prior research has measured street perceptions either objectively or subjectively. However, there is little agreement on measuring these concepts. Fewer studies have systematically investigated the coherence and divergence between objective and subjective measurements of perceptions. Large divergence between the two measurements over the same perception can lead to different and even opposite spatial implications. Furthermore, what street environment features can cause the discrepancies between objectively and subjectively measured perceptions remain unexplained. To fill the gap, five pairwise (subjectively vs objectively measured) perceptions (i.e., complexity, enclosure, greenness, imageability, and walkability) are quantified based on Street View Imagery (SVI) and compared their overlap and disparity both statistically and through spatial mapping. With further insights on what features can explain the differences in each pairwise perceptions, and urban-scale mapping of street scene perceptions, this research provides valuable guidance on the future improvement of models.
Persistent Identifierhttp://hdl.handle.net/10722/336359
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSong, Qiwei-
dc.contributor.authorLi, Meikang-
dc.contributor.authorQiu, Waishan-
dc.contributor.authorLi, Wenjing-
dc.contributor.authorLuo, Dan-
dc.date.accessioned2024-01-15T08:26:08Z-
dc.date.available2024-01-15T08:26:08Z-
dc.date.issued2022-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, v. 13725 LNAI, p. 244-256-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/336359-
dc.description.abstractRecent development in Street View Imagery (SVI), Computer Vision (CV) and Machine Learning (ML) has allowed scholars to quantitatively measure human perceived street characteristics and perceptions at an unprecedented scale. Prior research has measured street perceptions either objectively or subjectively. However, there is little agreement on measuring these concepts. Fewer studies have systematically investigated the coherence and divergence between objective and subjective measurements of perceptions. Large divergence between the two measurements over the same perception can lead to different and even opposite spatial implications. Furthermore, what street environment features can cause the discrepancies between objectively and subjectively measured perceptions remain unexplained. To fill the gap, five pairwise (subjectively vs objectively measured) perceptions (i.e., complexity, enclosure, greenness, imageability, and walkability) are quantified based on Street View Imagery (SVI) and compared their overlap and disparity both statistically and through spatial mapping. With further insights on what features can explain the differences in each pairwise perceptions, and urban-scale mapping of street scene perceptions, this research provides valuable guidance on the future improvement of models.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.subjectCoherence and divergence-
dc.subjectHuman perceptions-
dc.subjectMachine learning-
dc.subjectStreet view imagery-
dc.subjectSubjective and objective-
dc.titleThe Coherence and Divergence Between the Objective and Subjective Measurement of Street Perceptions for Shanghai-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-031-22064-7_19-
dc.identifier.scopuseid_2-s2.0-85144466186-
dc.identifier.volume13725 LNAI-
dc.identifier.spage244-
dc.identifier.epage256-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000904475500019-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats