research-article

CrowdAtlas: self-updating maps for cloud and personal use

Authors:

Yanmin ZhuAuthors Info & Claims

MobiSys '13: Proceeding of the 11th annual international conference on Mobile systems, applications, and services

Pages 27 - 40

https://doi.org/10.1145/2462456.2464441

Published: 25 June 2013 Publication History

Abstract

The inaccuracy of manually created digital road maps is a persistent problem, despite their high economic value. We present CrowdAtlas, which automates map update based on people's travels, either individually or crowdsourced. Its mobile navigation app detects significant portions of GPS traces that do not conform to the existing map, as determined by state-of-the-art Viterbi map matching. When there is sufficient evidence collected, map inference algorithms can automatically update the map. The CrowdAtlas server aggregates exceptional traces from users with the navigation app as well as from other, large-scale data sources. From these it automatically generates high quality map updates, which can be propagated to its navigation app and other interested applications. Using CrowdAtlas app, we mapped out a 4.5 km^2 street block in Shanghai in less than half an hour and built a walking/cycling map of the SJTU campus. Using taxi traces collected from Beijing, we contributed completely computer-generated roads for this large, 61 km of missing roads to OpenStreetMap, the first set of open-source map community.

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cover image ACM Conferences

MobiSys '13: Proceeding of the 11th annual international conference on Mobile systems, applications, and services

June 2013

568 pages

ISBN:9781450316729

DOI:10.1145/2462456

General Chairs:
Hao-Hua Chu
National Taiwan University
,
Polly Huang
National Taiwan University
,
Program Chairs:
Romit Roy Choudhury
Duke University
,
Feng Zhao
Microsoft Research

Copyright © 2013 ACM.

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Published: 25 June 2013

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MobiSys'13: The 11th Annual International Conference on Mobile Systems, Applications, and Services

June 25 - 28, 2013

Taipei, Taiwan

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Wang SWang ZRuan SHan HXiong KYuan HYuan ZLi GBao JZheng Y(2024)DelvMap: Completing Residential Roads in Maps Based on Couriers’ Trajectories and Satellite ImageryIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.336583362(1-14)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3365833
Rimediotti JMontori FSciullo LBononi L(2024)Inferring the Urban Noise Pollution with Sparse Data through Crowdsensing2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops59983.2024.10502790(643-648)Online publication date: 11-Mar-2024
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