Machine learning in cartography
(2024) In Cartography and Geographic Information Science 51(1). p.1-19- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/00af694a-27b4-4bae-96c1-4aded54d6f2f
- author
- Harrie, Lars LU ; Touya, Guillaume ; Oucheikh, Rachid LU ; Ai, Tinghua ; Courtial, Azelle and Richter, Kai Florian
- organization
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Cartography, deep learning, machine learning, map generalization, map labeling, pattern recognition, style transfer
- in
- Cartography and Geographic Information Science
- volume
- 51
- issue
- 1
- pages
- 19 pages
- publisher
- American Congress on Surveying and Mapping
- external identifiers
-
- scopus:85185246367
- ISSN
- 1523-0406
- DOI
- 10.1080/15230406.2023.2295948
- language
- English
- LU publication?
- yes
- id
- 00af694a-27b4-4bae-96c1-4aded54d6f2f
- date added to LUP
- 2024-03-20 12:20:45
- date last changed
- 2024-03-20 12:21:37
@misc{00af694a-27b4-4bae-96c1-4aded54d6f2f, abstract = {{<p>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.</p>}}, author = {{Harrie, Lars and Touya, Guillaume and Oucheikh, Rachid and Ai, Tinghua and Courtial, Azelle and Richter, Kai Florian}}, issn = {{1523-0406}}, keywords = {{Cartography; deep learning; machine learning; map generalization; map labeling; pattern recognition; style transfer}}, language = {{eng}}, number = {{1}}, pages = {{1--19}}, publisher = {{American Congress on Surveying and Mapping}}, series = {{Cartography and Geographic Information Science}}, title = {{Machine learning in cartography}}, url = {{http://dx.doi.org/10.1080/15230406.2023.2295948}}, doi = {{10.1080/15230406.2023.2295948}}, volume = {{51}}, year = {{2024}}, }