File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Mapping seasonal changes of street greenery using multi-temporal street-view images

TitleMapping seasonal changes of street greenery using multi-temporal street-view images
Authors
KeywordsDeep learning
Human-centered perspective
Seasonal differences
Street greenery
Street-view images
Issue Date1-May-2023
PublisherElsevier
Citation
Sustainable Cities and Society, 2023, v. 92 How to Cite?
AbstractStreet greenery offers various benefits to urban environments. In regions with climatic variations among seasons, seasonal changes of vegetation may lead to fluctuations in the benefits provided by street greenery. It is vital to monitor and measure the seasonal changes of street greenery. Previous studies have analyzed changes of street greenery, mainly from an aerial view. However, aerial views may not be equivalent to residents' visual experiences. This study aims to quantitatively characterize seasonal differences in street greenery based on multi-temporal street-view images which can simulate pedestrians' view. The Gulou District in Nanjing, China, is selected for a pilot study. We collected multi-temporal street-view images through an online street-view service. Deep learning-based algorithms were used to extract seasonal street greenery from street-view images. The results revealed significant seasonal differences in street greenery in the Gulou District. We classified four street greening patterns, including (1) Deciduous and evergreen mixed pattern; (2) Deciduous-dominant pattern; (3) No-plant pattern; (4) Evergreen-dominant pattern, with distinct seasonal change characteristics. For each pattern, we explored possible adjustments in planting arrangements. Our work is a preliminary attempt, and the proposed framework could assist in future sustainable greening design and planning.
Persistent Identifierhttp://hdl.handle.net/10722/337484
ISSN
2023 Impact Factor: 10.5
2023 SCImago Journal Rankings: 2.545
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHan, Y-
dc.contributor.authorZhong, T-
dc.contributor.authorYeh, AGO-
dc.contributor.authorZhong, X-
dc.contributor.authorChen, M-
dc.contributor.authorLü, G -
dc.date.accessioned2024-03-11T10:21:14Z-
dc.date.available2024-03-11T10:21:14Z-
dc.date.issued2023-05-01-
dc.identifier.citationSustainable Cities and Society, 2023, v. 92-
dc.identifier.issn2210-6707-
dc.identifier.urihttp://hdl.handle.net/10722/337484-
dc.description.abstractStreet greenery offers various benefits to urban environments. In regions with climatic variations among seasons, seasonal changes of vegetation may lead to fluctuations in the benefits provided by street greenery. It is vital to monitor and measure the seasonal changes of street greenery. Previous studies have analyzed changes of street greenery, mainly from an aerial view. However, aerial views may not be equivalent to residents' visual experiences. This study aims to quantitatively characterize seasonal differences in street greenery based on multi-temporal street-view images which can simulate pedestrians' view. The Gulou District in Nanjing, China, is selected for a pilot study. We collected multi-temporal street-view images through an online street-view service. Deep learning-based algorithms were used to extract seasonal street greenery from street-view images. The results revealed significant seasonal differences in street greenery in the Gulou District. We classified four street greening patterns, including (1) Deciduous and evergreen mixed pattern; (2) Deciduous-dominant pattern; (3) No-plant pattern; (4) Evergreen-dominant pattern, with distinct seasonal change characteristics. For each pattern, we explored possible adjustments in planting arrangements. Our work is a preliminary attempt, and the proposed framework could assist in future sustainable greening design and planning.-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofSustainable Cities and Society-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectDeep learning-
dc.subjectHuman-centered perspective-
dc.subjectSeasonal differences-
dc.subjectStreet greenery-
dc.subjectStreet-view images-
dc.titleMapping seasonal changes of street greenery using multi-temporal street-view images-
dc.typeArticle-
dc.identifier.doi10.1016/j.scs.2023.104498-
dc.identifier.scopuseid_2-s2.0-85149630758-
dc.identifier.volume92-
dc.identifier.eissn2210-6715-
dc.identifier.isiWOS:000961881200001-
dc.identifier.issnl2210-6707-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats