Paper Info
Title
Object Geolocation from Crowdsourced Street Level Imagery.
Abstract
We explore the applicability and limitations of a state-of-the-art object detection and geotagging system [4] applied to crowdsourced image data. Our experiments with imagery from Mapillary crowdsourcing platform demonstrate that with increasing amount of images, the detection accuracy is getting close to that obtained with high-end street level data. Nevertheless, due to excessive camera position noise, the estimated geolocation (position) of the detected object is less accurate on crowdsourced Mapillary imagery than with high-end street level imagery obtained by Google Street View.
Year
DOI
Venue
2018
10.1007/978-3-030-13453-2_7
Nemesis/UrbReas/SoGood/IWAISe/GDM@PKDD/ECML
Field
DocType
Citations 
Object detection,Computer vision,Crowdsourcing,Computer science,Geolocation,Geotagging,Artificial intelligence
Conference
0
PageRank 
References 
Authors
0.34
4
2
Name
Order
Citations
PageRank
Vladimir A. Krylov113314.81
Rozenn Dahyot234032.62