Conclusions: What Is Known and What Is Still Challenging About Landmarks - Landmarks: GIScience for Intelligent Services - page 204

world assumption we have discussed in Chap. 5 (on p. 139 ) . We have seen that
finding outstanding geographic objects may be supported by data mining methods
(Sect. 5.2.2 ) , and reasoning about object types (Sects. 5.3 and 5.5.2 ) .
A combination of these approaches will likely get us quite far in identifying
landmarks in densely populated areas, in particular in city centers and commercial
districts. However, given the uneven distribution of POI data and the lack of business
signage in residential suburbs, there are also large areas of our environment where
we would like an intelligent system to refer to landmarks, but it fails to do so for lack
of data. This is where user-generated content steps in. Implementing a platform that
allows users to contribute landmark information about their neighborhood would
be an ideal supplement to the machine learning techniques used for an automatic
identification of landmarks. Such a platform must be easy to use, allow for quick
rewards for the contributor, but it also needs to ensure that the contributed data can
be applied in landmark identification approaches in a straightforward manner [ 12 ] .
We see the OpenLandmarks platform as a first step in the right direction (see
Sect. 5.5.3 ) . A database such a user-generated content platform feeds into may be
pre-filled by all landmark candidates we can get from the data mining methods
discussed before. This would serve two purposes: first, contributors are not faced
with a blank canvas, but can already see what their contributions will (are supposed
to) look like. Second, the end result will be an integrated database that combines
user-generated content and automatically extracted landmarks. This will also allow
for correction mechanisms. Users may change information on landmarks the system
identified, and likewise the system may be able to flag improbable landmark
candidates submitted by a user.
It may well be the case that the success of such a user-generated content
approach to collecting landmark information depends on the developed
platform having a monopoly in collecting such data. Wikipedia would not
be so successful if there were six or seven other online encyclopedias of
similar status competing for contributors. The same holds for OpenStreetMap.
OSM data would be much less useful if every country (or even only every
continent) had their own platform for collecting topographic data, each with
slightly different mechanisms and data structures. In fact, a recent change of
the OSM license, which also affects the kind of data you can use as basis for
your contributions, has led several OSM contributors to fork OSM data into a
new data set where the old license still holds—this move has been particularly
strong in Australia. The consequences of this fork are still to be seen, but some
fragmentation and inconsistencies between the two data sets are most likely
to occur.
Context is an altogether different beast. Context and its operationalization has
been researched for a long time in human-computer interaction and location based