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Abstract: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.

Routing and navigation web services are becoming widely used, and make use of both commercial and VGI datasets. It is now becoming widely acknowledged that a 'one fits all' method of generating and presenting routes is not applicable. In particular, the accessibility of places for the mobility impaired has become a key focus with several services addressing topics such as how accessible locations of interest are and how to best generate routes for people who need to consider additional factors. Though datasources such as OpenStreetMap (OSM) are well suited for such topics, several issues including the quality of the underlying data remain. Through the use of quality assessment tools it is possible to identify areas with inadequate data completeness with regards to the information needed for the mobility impaired and thus encourage the enrichment of these areas through specialised tagging applications. Such data can then be used in routing and navigation services which focus on ensuring that routes being generated and presented fit the personal requirements of the traveller.

When producing optimal routes through an environment, considering the incline of surfaces can be of great benefit in a number of use cases. For instance, steep segments need to be avoided for energy-efficient routes and for routes that are suitable for mobility-restricted people. Such incline information may be derived from digital elevation models (DEMs). However, the corresponding data capturing methods (e.g. airborne LiDAR, photogrammetry, and terrestrial surveying) are expensive. Current low-cost and open-licensed DEM (e.g. Shuttle Radar Topography Mission [SRTM] and Advanced Spaceborne Thermal Emission and Reflection Radiometer [ASTER]) generally do not have sufficient horizontal resolution or vertical accuracy, and lack a global coverage. Therefore, we have investigated an alternative low-cost approach which derives street incline values from GPS traces that have been voluntarily collected by the OpenStreetMap contributors. Despite the poor absolute accuracy of this data, the relative accuracy of traces seems to be sufficient enough to compute incline values with reasonable accuracy. A validation shows that the accuracy of incline values calculated from GPS traces slightly outperforms incline values derived from SRTM-1 DEM, though results depend on how many traces per street segment are used for computation. ARTICLE HISTORY

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.