Artificial intelligence studies in cartography: a review and synthesis of methods, applications, and ethics

ABSTRACT

The past decade has witnessed the rapid development of geospatial artificial intelligence (GeoAI) primarily due to the ground-breaking achievements in deep learning and machine learning. A growing number of scholars from cartography have demonstrated successfully that GeoAI can accelerate previously complex cartographic design tasks and even enable cartographic creativity in new ways. Despite the promise of GeoAI, researchers and practitioners have growing concerns about the ethical issues of GeoAI for cartography. In this paper, we conducted a systematic content analysis and narrative synthesis of research studies integrating GeoAI and cartography to summarize current research and development trends regarding the usage of GeoAI for cartographic design. Based on this review and synthesis, we first identify dimensions of GeoAI methods for cartography such as data sources, data formats, map evaluations, and six contemporary GeoAI models, each of which serves a variety of cartographic tasks. These models include decision trees, knowledge graph and semantic web technologies, deep convolutional neural networks, generative adversarial networks, graph neural networks, and reinforcement learning. Further, we summarize seven cartographic design applications where GeoAI have been effectively employed: generalization, symbolization, typography, map reading, map interpretation, map analysis, and map production. We also raise five potential ethical challenges that need to be addressed in the integration of GeoAI for cartography: commodification, responsibility, privacy, bias, and (together) transparency, explainability, and provenance. We conclude by identifying four potential research directions for future cartographic research with GeoAI: GeoAI-enabled active cartographic symbolism, human-in-the-loop GeoAI for cartography, GeoAI-based mapping-as-a-service, and generative GeoAI for cartography.

KEYWORDS:

• GeoAI
• cartography
• maps
• Artificial intelligence
• deep learning
• ethics

Acknowledgments

The authors would like to thank people who attended the AutoCarto 2022 Conference and CartoAI workshop at the GIScience 2023 conference. The feedback and insights from those who participated the discussions have been valuable for this paper. In particular, Dr. Liqiu Meng, Mark Cygan, Dr. Alexander Kent, and Dr. Feng Yu. The authors would also like to acknowledge the anonymous reviewers whose critical reviews and suggestions have helped improve the quality of this paper. This work is supported by the Trewartha Research Award at the University of Wisconsin-Madison, Master’s Thesis Research Grant of the AAG Cartography Specialty Group. During the writing of the manuscript, ChatGPT was utilized as a tool solely for proofreading purposes, without contributing any ideas or perspectives to the content of the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Notes

1. https://www.scopus.com/

2. https://openai.com/blog/chatgpt

3. https://openai.com/dall-e-2

4. https://bard.google.com/

5. https://www.midjourney.com/

6. https://stablediffusionweb.com/

Additional information

Funding

The work was supported by the Trewartha Research Award, Department of Geography, University of Wisconsin-Madison, and the Master’s Thesis Research Grant of the AAG Cartography Specialty Group.