As the discussion in the previous section has addressed several times (see also Table 1), a major challenge to identifying landmark candidates reliably and to sufficient numbers across environments is the lack of data that consistently provides detailed enough information on geographic objects and their attributes. Accordingly, it seems rather optimistic to base the identification of landmark candidates on such data if this is to be done on a large scale spanning whole cities, countries, or even globally. Raubal and Winter’s approach has been very important conceptually for driving research, but it is not scalable.
As we have also seen, generally a lightweight approach to identifying landmark candidates seems more promising, as for example the one chosen by Duckham et al. . Relying only on type and location information has very few computational demands. It also reduces demands posed on the underlying data. But as discussed in the previous section, such an approach has some disadvantages as well, namely potential ambiguity in categorization and the fact that not all individuals of a category will be equally suitable as landmarks. Therefore, such an approach would ideally be augmented with some mechanisms to flexibly adapting both category and suitability ratings. Overall, a smart combination of principles implemented in existing approaches might present a solution here, further discussed in the following.
In the proposed new approach, uniformly assigning the same landmarkness value to all objects of a specific category will form the base assessment of landmark suitability. Any application using this landmark data may then include feedback mechanisms that would allow users to mark the usefulness of a given object up or down, and also to disagree with its categorization. These proposed changes can initially be kept to the user who made them, i.e., personalize their landmarkness settings. Aggregated over multiple users, these proposed changes may also change general settings of both suitability ratings and categorization.
In some more detail, while using types provides an easy, lightweight approach, uniformity of landmarkness in a given category will not hold in the real world. For example, some places of worship will be more salient than others; compare St. Peter’s Cathedral with a small ‘place of worship’ room hidden away at an airport. These differences may be captured by enabling users of a system to provide such feedback. If users are presented with a landmark they deem unsuitable, for instance, if they cannot even detect it, there may be a simple mechanism to mark them as not useful in the system. In the same manner, they may also mark referenced objects as particularly useful landmarks (e.g., by using simple ‘\(+\)’ and ‘−’ or ‘thumbs-up’ and ‘thumbs-down’ buttons). This would then change the landmarkness value for the individual object, initially only for the individual user. If a specific user repeatedly marks down (or up) objects of the same type, say street furniture or retail outlets, a system may also infer user preferences from this behavior and, thus, adapt globally landmark selection for this user accordingly.
Initially, this will lead to a type-based, but more personalized landmark selection for individual users. However, as so often with such approaches, user behavior may also be aggregated to perform general adaptations. For example, if repeated rejections of an object occur across multiple users, this may be taken as indication that the specific object is generally not suited as a landmark. Following the same reasoning, such behavior may also lead to adapting landmarkness values for a whole category of objects. In case repeatedly multiple objects of the same category get marked down by multiple users, this may indicate that the initial judgement of the category’s suitability as landmark candidate needs to be re-evaluated.
There are some caveats with the proposed approach. Clearly, also a type-based approach to identifying landmark candidates depends on an underlying data set. While this set has less demands on object attributes, it would still need to provide a reasonably uniform coverage of objects of various categories with their geographic location. It is doubtful whether such a data set currently exists even for a single city. For example, experiments presented in  and an analysis of the Swiss OSM data  have shown that even in a highly developed country, such as Switzerland, geographic data may not be uniformly fit for use in such an approach. But similar to some existing social network platforms, users may be encouraged to submit additional landmark candidates themselves, either integrated into a navigation application, or probably more usefully as a standalone application. Such user-generated content comes with the usual issues, such as potential errors or even malicious user behavior. But again, firstly this contributed data may be used to improve navigation experience for the contributing user. As such, it may not be necessary to make the data available to other users immediately, but some moderation mechanisms could be incorporated. One such option may be to set up a game-like application, where newly contributed landmark candidates would first need to be ‘found’ by other users, before they will be used globally; similar to the approach in .
When using a type-based approach assigning landmarkness values also strongly depends on the underlying categorization scheme—the ‘object ontology’ if you will. And since the type names will most likely also be used when referring to landmark objects in user interaction (e.g., ‘turn left at the church’, ‘move towards the museum’) this scheme also has a strong influence on user interaction. It is highly likely that not all users will agree with how an object is referred to all the time, i.e., they may have a different conceptualization of what kind of object it is than what the system assumes. Again, it would be possible to implement some feedback mechanism that allows users to change an object’s categorization (or just its label). This would first and foremost result in personalization, i.e., and adaptation for an individual user. But as with usefulness, these changes may be feedback into the overall system and with multiple users providing the same, or very similar, feedback, categorization may change globally.
Clearly, implementing such a new approach to identifying landmark candidates requires thorough evaluation and testing. This should be done on at least three levels: targeted studies that test the usability and usefulness of the new approach’s individual elements; a medium-term study that tests how and where individual users employ the feedback mechanisms or add new landmark information; and finally a medium- to long-term study with multiple users observing the effects and interplay of the different feedback mechanisms on global landmarkness settings. The first level of evaluation is mainly meant to ensure that the implemented procedures and interaction mechanisms actually work. It may follow ‘standard’ procedures of user and usability testing and should also be preceded or accompanied by software testing and some geo-spatial analysis of the underlying data—the base landmarkness assessments and their distribution. The second level will evaluate how the different implemented components interplay in the longer run, for example, whether some of them counter each other and how (much) personalization will occur. It will also allow for assessing user acceptance of the different mechanisms and their willingness to continuously use the system. Finally, the third level of evaluation will provide similar insights to the second level, but in addition will shed some light on desired and undesired effects of user and software components interplay when multiple users with potentially conflicting interests are involved. It will also show whether user contributions will be reasonably uniformly distributed or whether there are similar biases to data distribution as we observe in many UGC data sets. The latter case would then require some counter-measures, for example, by setting up incentives to explore potential landmarks in less covered areas in some game-like settingsFootnote 1—which would need to be evaluated again of course.
To conclude, using a type-based approach ensures that there is a reasonable base level of useful landmark candidates, which can be determined quickly and with low effort. Providing a range of feedback and interaction mechanisms then allows for fine-tuning such a system to accommodate individual differences, but also mis-classifications that are bound to occur in such a heuristic approach. Clearly, we cannot expect users to evaluate all landmark references all the time, but providing feedback will have immediate benefits, particularly for those references that did not work well for a user. Thus, given an engaging, unobtrusive user interface a smart combination of a simple, but well-balanced base selection of landmark candidates with elaborate inference mechanisms based on user feedback may prove to be the scalable solution missing so far.