Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam
Over the last decade, Volunteered Geographic Information (VGI) has emerged as a viable source of information on cities. During this time, the nature of VGI has been evolving, with new types and sources of data continually being added. In light of this trend, this paper explores one such type of VGI...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/9/6/341 |
| _version_ | 1827716152245092352 |
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| author | Ron Mahabir Ross Schuchard Andrew Crooks Arie Croitoru Anthony Stefanidis |
| author_facet | Ron Mahabir Ross Schuchard Andrew Crooks Arie Croitoru Anthony Stefanidis |
| author_sort | Ron Mahabir |
| collection | DOAJ |
| description | Over the last decade, Volunteered Geographic Information (VGI) has emerged as a viable source of information on cities. During this time, the nature of VGI has been evolving, with new types and sources of data continually being added. In light of this trend, this paper explores one such type of VGI data: Volunteered Street View Imagery (VSVI). Two VSVI sources, Mapillary and OpenStreetCam, were extracted and analyzed to study road coverage and contribution patterns for four US metropolitan areas. Results show that coverage patterns vary across sites, with most contributions occurring along local roads and in populated areas. We also found that a few users contributed most of the data. Moreover, the results suggest that most data are being collected during three distinct times of day (i.e., morning, lunch and late afternoon). The paper concludes with a discussion that while VSVI data is still relatively new, it has the potential to be a rich source of spatial and temporal information for monitoring cities. |
| first_indexed | 2024-03-10T19:35:31Z |
| format | Article |
| id | doaj.art-3f1af2eb1eb842b5813ec09511c9821d |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T19:35:31Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-3f1af2eb1eb842b5813ec09511c9821d2023-11-20T01:46:17ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-05-019634110.3390/ijgi9060341Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCamRon Mahabir0Ross Schuchard1Andrew Crooks2Arie Croitoru3Anthony Stefanidis4Department of Computational and Data Sciences, George Mason University, Fairfax, VA 22030, USADepartment of Computational and Data Sciences, George Mason University, Fairfax, VA 22030, USADepartment of Computational and Data Sciences, George Mason University, Fairfax, VA 22030, USACenter for Geoinformatics and Geospatial Intelligence, George Mason University, Fairfax, VA 22030, USADepartment of Computer Science, William and Mary, Williamsburg, VA 23187, USAOver the last decade, Volunteered Geographic Information (VGI) has emerged as a viable source of information on cities. During this time, the nature of VGI has been evolving, with new types and sources of data continually being added. In light of this trend, this paper explores one such type of VGI data: Volunteered Street View Imagery (VSVI). Two VSVI sources, Mapillary and OpenStreetCam, were extracted and analyzed to study road coverage and contribution patterns for four US metropolitan areas. Results show that coverage patterns vary across sites, with most contributions occurring along local roads and in populated areas. We also found that a few users contributed most of the data. Moreover, the results suggest that most data are being collected during three distinct times of day (i.e., morning, lunch and late afternoon). The paper concludes with a discussion that while VSVI data is still relatively new, it has the potential to be a rich source of spatial and temporal information for monitoring cities.https://www.mdpi.com/2220-9964/9/6/341crowdsourcingvolunteered geographic informationstreet view imageryMapillaryOpenStreetCam |
| spellingShingle | Ron Mahabir Ross Schuchard Andrew Crooks Arie Croitoru Anthony Stefanidis Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam ISPRS International Journal of Geo-Information crowdsourcing volunteered geographic information street view imagery Mapillary OpenStreetCam |
| title | Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam |
| title_full | Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam |
| title_fullStr | Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam |
| title_full_unstemmed | Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam |
| title_short | Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam |
| title_sort | crowdsourcing street view imagery a comparison of mapillary and openstreetcam |
| topic | crowdsourcing volunteered geographic information street view imagery Mapillary OpenStreetCam |
| url | https://www.mdpi.com/2220-9964/9/6/341 |
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