Machine learning in cartography

Summary, in English

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.

• Författare: Lars Harrie; Guillaume Touya; Rachid Oucheikh; Tinghua Ai; Azelle Courtial; Kai Florian Richter
• Avdelning/ar: Institutionen för naturgeografi och ekosystemvetenskap; Centrum för geografiska informationssystem (GIS-centrum); eSSENCE: The e-Science Collaboration
• Publiceringsår: 2024
• Språk: Engelska
• Sidor: 1-19
• Publikation/Tidskrift/Serie: Cartography and Geographic Information Science
• Volym: 51
• Issue: 1
• Länkar: http://dx.doi.org/10.1080/15230406.2023.2295948
• Dokumenttyp: Artikel i tidskrift
• Förlag: American Congress on Surveying and Mapping
• Ämne: Other Computer and Information Science
• Nyckelord: Cartography; deep learning; machine learning; map generalization; map labeling; pattern recognition; style transfer
• Status: Published
• ISBN/ISSN/Övrigt: ISSN: 1523-0406