A Zone-Based Approach for
Placing Annotation Labels on Metro Maps

Recently, hand-drawn metro map illustrations often employ both internal and external labels in a way that they can assign enough information such as textual and image annotations to each landmark. In this project, we present a zone-based approach for placing such annotation labels so that we can fully enhance the aesthetic criteria of the label arrangement. 

Our algorithm begins by decomposing the map domain into three different zones where we can limit the position of each label according to its type. The optimal positions of labels of each type are evaluated by referring to the zone segmentation over the map. By dilating metro line into different zones helps embedded textual and image labels to be placed as close as possible to their corresponding sites.

Finally, a new genetic-based approach is introduced to compute the optimal layout of such annotation labels, where the order in which the labels are embedded into the map is improved through the revolutionary computation algorithm. In our framework, we currently place textual and image annotation labels independently, and avoid overlapping in a greedy approach individually.

We also equipped a semantic zoom functionality, so that we can freely change the position and scale of the metro map. Also, automatically resizing image labels gives more flexibility to this annotation system.