https://orcid.org/0000-0003-3252-1495
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ABSTRACT
Machine learning is increasingly used as a computing paradigm in cartographic research. In this extended editorial, we provide some background of the papers in the CaGIS special issue Machine Learning in Cartography with a special focus on pattern recognition in maps, cartographic generalization, style transfer, and map labeling. In addition, the paper includes a discussion about map encodings for machine learning applications and the possible need for explicit cartographic knowledge and procedural modeling in cartographic machine learning models.
Acknowledgment
The authors would like to thank the anonymous reviewers for their constructive comments.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
The first and third authors were funded by eSSENCE@LU 7:1: “Data-driven automation of map labeling – enabling affordable high-quality maps” financed by the Swedish research council. The second and fifth authors were financed by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme [grant agreement No. 101003012 - LostInZoom].