We ask a set of users to independently detect and annotate all the buildings on a map. We do not have a priori knowledge about the location or event the existence of buildings on this map.

I would like to aggregate/cluster their annotations in a way that only 'consensual' annotations are taking into account. That means that:

  • I do not know a priori the number of clusters /object on the map (unsupervised approach)
  • each point in a cluster should belong to a different user (democratic clustering constraint) i.e. a cluster is formed only if the candidate points, spatially enough close, are from several users. with for instance a threshold (if 60% of the users annotate the same area, we consider it as a building)

Which (modified) clustering algorithm will be appropriated for such task? (I work with R)?


  • The density of the objects on the map is changing. one map could have a dense area of buildings + sparse subareas of buildings). how to choose dynamically the distance between 2 points to consider they are enough close?

Tried algorithm:

A density based algorithm (DBSCAN) but: - it does not take into account the democratic constraint in the formation of the cluster. - there is a problem to handle the change of spatial density on a map ( dense area of building + sparse area of building on the same map)


  • $\begingroup$ The question makes it sound like the only attribute of the georeferenced coordinates is the user, but that would be highly unusual: isn't each coordinate pair also identified by the object it purports to locate? If so, what need is there for an independent clustering algorithm? $\endgroup$ – whuber Jan 6 '12 at 17:04
  • $\begingroup$ The task asked to a set of users is to detect and annotate buildings on a map . So the only attribute associated with coordinates is the user having done the annotation. Since only one user do an error , several users do the same task. I would like a algorithm for such purpose. $\endgroup$ – nicom Jan 14 '12 at 9:56

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