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With the advent of location-aware smartphones, the desire for pedestrian-based navigation services has increased. Unlike car-based services where instructions generally are comprised of distance and road names, pedestrian instructions should instead focus on the delivery of landmarks to aid in navigation. OpenStreetMap (OSM) contains a vast amount of geospatial information that can be tapped into for identifying these landmark features. This paper presents a prototype navigation service that extracts landmarks suitable for navigation instructions from the OSM dataset based on several metrics. This is coupled with a short comparison of landmark availability within OSM, differences in routes between locations with different levels of OSM completeness and a short evaluation of the suitability of the landmarks provided by the prototype. Landmark extraction is performed on a server-side service, with the instructions being delivered to a pedestrian navigation application running on an Android mobile device.
Adam Rousell; Alexander Zipf. Towards a Landmark-Based Pedestrian Navigation Service Using OSM Data. ISPRS International Journal of Geo-Information 2017, 6, 64 .
AMA StyleAdam Rousell, Alexander Zipf. Towards a Landmark-Based Pedestrian Navigation Service Using OSM Data. ISPRS International Journal of Geo-Information. 2017; 6 (3):64.
Chicago/Turabian StyleAdam Rousell; Alexander Zipf. 2017. "Towards a Landmark-Based Pedestrian Navigation Service Using OSM Data." ISPRS International Journal of Geo-Information 6, no. 3: 64.
An increasing number of Volunteered Geographic Information (VGI) and social media platforms have been continuously growing in size, which have provided massive georeferenced data in many forms including textual information, photographs, and geoinformation. These georeferenced data have either been actively contributed (e.g., adding data to OpenStreetMap (OSM) or Mapillary) or collected in a more passive fashion by enabling geolocation whilst using an online platform (e.g., Twitter, Instagram, or Flickr). The benefit of scraping and streaming these data in stand-alone applications is evident, however, it is difficult for many users to script and scrape the diverse types of these data. On 14 June 2016, a pre-conference workshop at the AGILE 2016 conference in Helsinki, Finland was held. The workshop was called “LINK-VGI: LINKing and analyzing VGI across different platforms”. The workshop provided an opportunity for interested researchers to share ideas and findings on cross-platform data contributions. One portion of the workshop was dedicated to a hands-on session. In this session, the basics of spatial data access through selected Application Programming Interfaces (APIs) and the extraction of summary statistics of the results were illustrated. This paper presents the content of the hands-on session including the scripts and guidelines for extracting VGI data. Researchers, planners, and interested end-users can benefit from this paper for developing their own application for any region of the world.
Levente Juhász; Adam Rousell; Jamal Jokar Arsanjani. Technical Guidelines to Extract and Analyze VGI from Different Platforms. Data 2016, 1, 15 .
AMA StyleLevente Juhász, Adam Rousell, Jamal Jokar Arsanjani. Technical Guidelines to Extract and Analyze VGI from Different Platforms. Data. 2016; 1 (3):15.
Chicago/Turabian StyleLevente Juhász; Adam Rousell; Jamal Jokar Arsanjani. 2016. "Technical Guidelines to Extract and Analyze VGI from Different Platforms." Data 1, no. 3: 15.