Artificial Intelligence Studies in Cartography: A Review and Synthesis
of Methods, Applications, and Ethics
- URL: http://arxiv.org/abs/2312.07901v1
- Date: Wed, 13 Dec 2023 05:15:57 GMT
- Title: Artificial Intelligence Studies in Cartography: A Review and Synthesis
of Methods, Applications, and Ethics
- Authors: Yuhao Kang and Song Gao and Robert E. Roth
- Abstract summary: We conduct a systematic content analysis and narrative synthesis of research studies integrating GeoAI and cartography.
We identify dimensions of GeoAI methods for cartography such as data sources, data formats, map evaluations, and six contemporary GeoAI models.
We raise five potential ethical challenges that need to be addressed in the integration of GeoAI for cartography.
- Score: 4.665390376528911
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
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