Street view imagery in urban analytics and GIS: A review

•Street-level imagery became ingrained as an important urban data source.•Most comprehensive review on street view imagery in geospatial and urban studies.•We have screened 619 papers to identify the state of the art, focusing on applications.•250 studies are classified into 10 application domains a...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Landscape and urban planning Ročník 215; s. 104217
Hlavní autori: Biljecki, Filip, Ito, Koichi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.11.2021
Predmet:
Built environment
computer vision
data collection
Deep learning
Ground-level
landscapes
Remote sensing
socioeconomics
spatial data
systematic review
transportation
Urban data science
Urban planning
vegetation
Deep learning
Ground-level
Urban data science
Built environment
Urban planning
Remote sensing
ISSN:0169-2046, 1872-6062
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:•Street-level imagery became ingrained as an important urban data source.•Most comprehensive review on street view imagery in geospatial and urban studies.•We have screened 619 papers to identify the state of the art, focusing on applications.•250 studies are classified into 10 application domains and span dozens of use cases. Street view imagery has rapidly ascended as an important data source for geospatial data collection and urban analytics, deriving insights and supporting informed decisions. Such surge has been mainly catalysed by the proliferation of large-scale imagery platforms, advances in computer vision and machine learning, and availability of computing resources. We screened more than 600 recent papers to provide a comprehensive systematic review of the state of the art of how street-level imagery is currently used in studies pertaining to the built environment. The main findings are that: (i) street view imagery is now clearly an entrenched component of urban analytics and GIScience; (ii) most of the research relies on data from Google Street View; and (iii) it is used across myriads of domains with numerous applications – ranging from analysing vegetation and transportation to health and socio-economic studies. A notable trend is crowdsourced street view imagery, facilitated by services such as Mapillary and KartaView, in some cases furthering geographical coverage and temporal granularity, at a permissive licence.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0169-2046
1872-6062
DOI:10.1016/j.landurbplan.2021.104217