Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

Mahabir, Ron; Schuchard, Ross; Crooks, Andrew; Croitoru, Arie; Stefanidis, Anthony

doi:10.3390/ijgi9060341

Cited by 29 publications

(12 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We proceed to manually screen images to determine their suitability for inclusion. The challenges of working with crowdsourced imagery are documented by previous studies (Alvarez Leon and Quinn, 2019;Mahabir et al, 2020). Accordingly, we exclude images for the following main reasons: (1) quality (blurred or discoloured images); (2) narrow field of view (images with high degree of visual obstruction); (3) routes that are not accessible by pedestrians (e.g.…”

Section: Data Selection and Screeningmentioning

confidence: 99%

Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions

Yap

Chang

Biljecki

2022

Environment and Planning B: Urban Analytics and City Science

View full text Add to dashboard Cite

Planning for active mobility satisfies many fundamental tenets of good urban design and planning. However, planning for active mobility is a complex endeavour due to numerous local, place-based factors that influence active mobility decisions. Recent advancements in urban data research have demonstrated the effectiveness of deep learning methods in evaluating active mobility potential for urban environments. At present, the incorporation of semantic information from deep learning models and street view imagery into spatio-temporal contexts remains a challenge. In particular, knowledge extraction from deep learning models remains an open question for urban planning and decision-making. Towards this issue, we propose a functional deep learning and network science workflow that employs open data from OpenStreetMap and Mapillary to assess factors affecting active mobility decisions and route planning. We demonstrate the generalisability of our analytical workflow through two case studies focusing on urban greenery in Nerima city (Japan) and urban visual complexity in Pasir Ris town (Singapore). Our results reveal clear patterns of heterogeneity in urban streetscapes and identify unevenness in street infrastructure provision based on destination types. Using this information, we propose specific areas for design intervention to improve active mobility planning. Our workflow is applicable for a diverse range of use cases making it relevant to a wide range of stakeholders, not limited to, urban researchers, policy makers and urban planners.

show abstract

Section: Data Selection and Screeningmentioning

confidence: 99%

Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions

Yap

Chang

Biljecki

2022

Environment and Planning B: Urban Analytics and City Science

View full text Add to dashboard Cite

show abstract

“…Street view imagery (SVI) is an emerging yet promising data source that provides rich urban spatial information, and has gained growing recognition recently owing to its usefulness, widespread availability and the increasing ease to process images in large batches (Ma et al, 2019;Mahabir et al, 2020;Biljecki and Ito, 2021). SVI data is commonly provided by commercial services such as Google Street View (GSV), and crowdsourced platforms such as Mapillary and KartaView.…”

Section: Street View Imagery For Lcz Classificationmentioning

confidence: 99%

Local Climate Zones: Lessons From Singapore and Potential Improvement With Street View Imagery

Ignatius

Hou

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Urban heat island (UHI) is considered a serious environmental issue in highly urbanized cities such as Singapore. To better quantify the UHI intensity, the local climate zones (LCZ) classification scheme was adopted to characterize land covers, and describe and compare their thermal performance. There are three commonly used LCZ classification approaches: manual sampling,World Urban Database and Access Portal Tools (WUDAPT) processing method using remote sensing, and geographical information system (GIS)-based method. Based on the current implementation of WUDAPT Level 0 method in the classification work in Singapore, the principal limitations are expounded. To overcome the deficiencies, street view imagery (SVI), which carries substantial urban spatial information, is regarded as a promising data source. This paper reviews the potential of SVI to better estimate certain LCZrelated properties, such as sky view factor (SVF). As it allows a detailed view on the ground objects, SVI opens up the possibility of identifying surface properties such as albedo, as well as anthropogenic heat sources. Although it is not a novel idea, there has been a lack of a comprehensive use of SVI in assisting LCZ classification from the ground up, especially in a high-density city such as Singapore. This paper overviews potential ways to incorporate SVI and identifies challenges such as coarse temporal resolution and spatial coverage constrained to drivable roads.

show abstract

“…Consequentially, a large volume of images with GPS information has been created and continues to be updated every day. Such VGI data also provide annotations to public data sets to assist urban land-use analysis (Mahabir et al 2020, Munoz et al 2020. Antoniou et al (2016) reviewed VGI images for mapping land-use patterns and found that more than half of the collected images are helpful to extract the land-use related information.…”

Section: Proximate Sensing Datamentioning

confidence: 99%

Urban land-use analysis using proximate sensing imagery: a survey

Qiao¹,

Yuan²

2021

Preprint

View full text Add to dashboard Cite

Urban regions are complicated functional systems that are closely associated with and reshaped by human activities. The propagation of online geographic information-sharing platforms and mobile devices equipped with Global Positioning System (GPS) greatly proliferates proximate sensing images taken near or on the ground at a close distance to urban targets. Studies leveraging proximate sensing imagery have demonstrated great potential to address the need for local data in urban land-use analysis. This paper reviews and summarizes the state-of-the-art methods and publicly available datasets from proximate sensing to support land-use analysis. We identify several research problems in the perspective of examples to support training of models and means of integrating diverse data sets. Our discussions highlight the challenges, strategies, and opportunities faced by the existing methods using proximate sensing imagery in urban land-use studies.

show abstract

Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

Cited by 29 publications

References 73 publications

Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions

Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions

Local Climate Zones: Lessons From Singapore and Potential Improvement With Street View Imagery

Urban land-use analysis using proximate sensing imagery: a survey

Contact Info

Product

Resources

About