Collecting a Ground Truth Dataset for OpenStreetMap

Transcription

1 Collecting a Ground Truth Dataset for OpenStreetMap Carsten Keßler Institute for Geoinformatics, University of Münster soon: Hunter College, CUNY

2 Background Study: Can we assess a feature s quality based on its trustworthiness? Problem: What is the gold standard for a feature s quality? Thursday, 10:30 Session 4.2

3 Known problem Haklay (2010): Ordnance Survey Girres & Touya (2010): Institut Géographique National Mooney et al. (2010): Ordnance Survey Ireland (and others) Zielstra and Zipf (2010): Tele Atlas

4 Comparing apples and oranges?

5 Comparing apples and oranges? Focus on spatial accuracy

6 Comparing apples and oranges? Focus on spatial accuracy What about: thematic accuracy, consistency, and completeness timeliness fitness for purpose harder, but also more interesting

7 Comparing apples and oranges? Focus on spatial accuracy What about: thematic accuracy, consistency, and completeness timeliness fitness for purpose harder, but also more interesting What about data that do not even exist anywhere else?

8 Comparing apples and oranges? Focus on spatial accuracy What about: thematic accuracy, consistency, and completeness timeliness fitness for purpose harder, but also more interesting What about data that do not even exist anywhere else? Should other datasets set the benchmark for the OSM data?

9 The Atlantic: How Google Builds Its Maps and What It Means for the Future of Everything

10 2 Proposals

11 1. Combining existing datasets

12 1. Combining existing datasets Lots of services explicitly collect information about places: Facebook, Foursquare, Yelp, Wikipedia/Wikidata, LOD Cloud,

13 1. Combining existing datasets Lots of services explicitly collect information about places: Facebook, Foursquare, Yelp, Wikipedia/Wikidata, LOD Cloud, Different thematic angles more complete

14 1. Combining existing datasets Lots of services explicitly collect information about places: Facebook, Foursquare, Yelp, Wikipedia/Wikidata, LOD Cloud, Different thematic angles more complete Complement authoritative/commercial reference data (scope)

15 Limitations and challenges APIs and terms of use set bounds to exploit these sources Only useful for specific lookups Different APIs Powerful and reliable identity reasoning required

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18 2. Crowdsourcing in the spirit of OSM Ad-hoc collection of required reference data Potential to use the data not just for comparison Problem: Motivation

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21 Conclusions

22 Conclusions OSM should define its own quality requirements

23 Conclusions OSM should define its own quality requirements Reference data: External sources + crowdsourcing + X

24 Conclusions OSM should define its own quality requirements Reference data: External sources + crowdsourcing + X Lots of research opportunities: Identity reasoning, HCI, social aspects, gamification,

25 Conclusions OSM should define its own quality requirements Reference data: External sources + crowdsourcing + X Lots of research opportunities: Identity reasoning, HCI, social aspects, gamification, Best incentive: increase the usage of the OSM data

26 Thank you!

27 References Girres and Touya (2010) Qualityassessment of the French OpenStreetMap dataset. Transactions in GIS, 14(4): Haklay (2010) How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environment and planning. B, Planning & design, 37(4):682. Zielstra and Zipf (2010) A Comparative Study of Proprietary Geodata and Volunteered Geographic Information for Germany. 13th AGILE International Conference on Geographic Information Science. Guimaraes, Portugal Mooney, Corcoran, and Winstanley (2010). Towards quality metrics for openstreetmap. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp ). ACM.