CrowdAtlas: self-updating maps for cloud and personal use

  • Authors:

  • Yin Wang;Xuemei Liu;Hong Wei;George Forman;Chao Chen;Yanmin Zhu

  • Affiliations:

  • HP Labs, Palo Alto, CA, USA;Baidu Inc., Shanghai, China;Shanghai Jiao Tong University, Shanghai, China;HP Labs, Palo Alto, CA, USA;Shanghai Jiao Tong University, Shanghai, China;Shanghai Jiao Tong University, Shanghai, China

  • Venue:
  • Proceeding of the 11th annual international conference on Mobile systems, applications, and services
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

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.