Computational Aspects: How Landmarks Can Be Observed, Stored, and Analysed - Landmarks: GIScience for Intelligent Services - page 165

important and we will simply stick with user-generated content. Essentially, the idea
is to access users' knowledge about an environment in order to collect and update
the required information; the users serve as 'database' [ 41 ] .
Holone et al. [ 19 ] , for example, suggested a system that allows to mark route
segments as bad or inaccessible for wheelchair users—or people with similar
movement restrictions, even if only temporarily (e.g., pushing a baby stroller).
Karimi et al. [ 21 , 22 ] discussed SoNavNet , a social navigation network, where people
can provide and request recommendations for POIs and routes to these POIs. While
neither of these approaches specifically targets landmarks, both exploit users and
their willingness to contribute in order to provide better navigation services.
One way to motivate users to contribute the kind of data a service provider is
looking for is to set up an entertaining incentive, such as a mobile game, in a way that
the sought for data becomes a by-product of that game [ 2 , 54 ] . Bell et al. [ 2 ] designed
such a game, called EyeSpy , which collects photographs of city locations that
support navigation. In that game, players take photographs within a city environment
and/or produce text tags describing the environment. These photographs are geo-
referenced by using a phone's WiFi or GPS sensor readings. Other players then
need to confirm these photographs by moving close to the location where they were
taken and confirming that they have found what is depicted on the photographs.
Players get points for performing such confirmations, but they also get points based
on the popularity of a photograph, i.e., how often it gets confirmed by other players.
The idea behind this setup is that photographs become more popular if they are easy
to find and recognize. Therefore, in order to get more points, players will aim at
submitting photographs they believe to be easily recognizable. Such photographs
will also be easily recognizable in other contexts, for example, when provided
as navigation assistance. Thus, the useful by-products of EyeSpy are photographs
of a city environment that make specific locations more recognizable, i.e., have a
landmark character.
Richter and Winter [ 14 , 42 ] proposed mechanisms to collect landmark data in
OpenStreetMap. OpenStreetMap (OSM) is a topographic data set of the world
compiled from user-generated content. The OpenLandmarks application would
allow users to mark existing objects within the OSM data as landmarks. A first
prototype has been developed (Fig. 5.17 ) and used in some early experimentation.
Currently, the system's interface is map-based. If users identify a building as a
potential landmark while walking through a (city) environment, they may request
all buildings in their surroundings that are actually represented as polygons in OSM
to be highlighted as potential landmark candidates. Users may then select the one
they have identified on the map and describe its landmark characteristics either by
name (e.g., 'Flinders St Station') or by description ('big yellow train station').
It is envisioned to provide an improved version of such a tool to the Open-
StreetMap community—or in fact anyone interested in contributing—and this way
to build up a database of user-generated landmark candidates over time, similar
to what has been achieved with OpenStreetMap. While we believe that employing
mechanisms of user-generated content is the only realistic way of ever getting a