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.

地理人工智能（GeoAI）在测绘学中的应用与研究展现了巨大潜力，同时也引发了伦理问题的关注，本文通过对 GeoAI 和测绘学整合的研究进行综述和分析，总结了关于 GeoAI 在测绘设计中的应用方法以及相关的伦理挑战，并提出了未来的研究方向。

地理制图中的人工智能研究：方法、应用和伦理的综述与综合

Artificial Intelligence Studies in Cartography: A Review and Synthesis  of Methods, Applications, and Ethics

We propose Cartography Active Learning (CAL), a novel Active Learning (AL)
algorithm that exploits the behavior of the model on individual instances
during training as a proxy to find the most informative instances for labeling.
CAL is inspired by data maps, which were recently proposed to derive insights
into dataset quality (Swayamdipta et al., 2020). We compare our method on
popular text classification tasks to commonly used AL strategies, which instead
rely on post-training behavior. We demonstrate that CAL is competitive to other
common AL methods, showing that training dynamics derived from small seed data
can be successfully used for AL. We provide insights into our new AL method by
analyzing batch-level statistics utilizing the data maps. Our results further
show that CAL results in a more data-efficient learning strategy, achieving
comparable or better results with considerably less training data.

提出了一种名为 “Cartography Active Learning”(CAL) 的主动学习算法，该算法通过利用模型在单个实例上的行为作为代理来查找最具信息价值的实例进行标注。与其他常用的 AL 策略相比，CAL 是一种更有效的学习策略，可以通过利用小种子数据产生的训练动态来成功地进行 AL。