Hi Labeling

Overview

Label placement in maps is a very challenging task that is critical for the overall map quality. Most previous work focused on designing and implementing fully automatic solutions, but the resulting visual and aesthetic quality has not reached the same level of sophistication that skilled human cartographers achieve. We investigate a different strategy that combines the strengths of humans and algorithms. In our proposed method, first an initial labeling is computed that has many well-placed labels but is not claiming to be perfect. Instead it serves as a starting point for an expert user who can then interactively and locally modify the labeling where necessary. In an iterative human-in-the-loop process alternating between user modifications and local algorithmic updates and refinements the labeling can be tuned to the user’s needs. We demonstrate our approach by performing different possible modification steps in a sample workflow with a prototypical interactive labeling editor. Further, we report computational performance results from a simulation experiment in QGIS, which investigates the differences between exact and heuristic algorithms for semi-automatic map labeling. To that end, we compare several alternatives for recomputing the labeling after local modifications and updates, as a major ingredient for an interactive labeling editor.

This is the QGIS labeling integration model of the open source project Hi-Labeling—an interactive labeling project developed at the Institute of Logic and Computation, TU Wien, Vienna, Austria.

So far our framework contains various labeling algoritghms, intrgated into the QGIS labeling engine. simpleweighted/ stable
mis [weighted/ stable]
kamis [unweighted/unstable] (see http://algo2.iti.kit.edu/kamis/)
maxHS [weighted/ stable]

Licence

This project is under MIT licence.

Reference

If you want to know more about our implemented algorithms, please refer to our paper:
Exploring Semi-Automatic Map Labeling
Fabian Klute, Guangping Li, Raphael Löffler, Martin Nöllenburg, Manuela Schmidt
Advances in Geographic Information Systems (SIGSPATIAL’19) (eds. Farnoush Banaei Kashani, Goce Trajcevski etc.), pages 13–22. ACM, 2019.
[bibtex] [pdf] [doi] Please acknowledge our work if you publish your result using our algorithms or code.

QgsLabelingEngine: https://qgis.org/api/classQgsLabelingEngine.html#details

Support

Please write us an Email if you have questions.

We are glad to get any comments and error reports.

A random instance generator is available upon request.

Acknowledgments

DynaMIS is part of the project “Human-Centered Algorithm Engineering: Graph and Map Visualization” supported by the Austrian Science Fund (FWF) under Grant P31119.