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.

翻译：过去十年间，地理空间人工智能（GeoAI）的快速发展主要得益于深度学习与机器学习领域的突破性成就。越来越多来自地图学界的学者成功证明，GeoAI能够加速此前复杂的制图设计任务，甚至以全新方式激发制图创造力。尽管GeoAI前景广阔，但研究人员与从业者对其在地图学中的伦理问题日益关注。本文通过系统性内容分析与叙事综合方法，对整合GeoAI与地图学的研究进行梳理，以总结当前GeoAI应用于制图设计的发展趋势。基于本综述与综合，我们首先明确了GeoAI方法在地图学中的应用维度，包括数据源、数据格式、地图评估以及六种服务于各类制图任务的当代GeoAI模型（决策树、知识图谱与语义网络技术、深度卷积神经网络、生成对抗网络、图神经网络及强化学习）。进一步，我们归纳了GeoAI已有效应用的七类制图设计场景：地图概括、符号化、字体设计、地图阅读、地图解读、地图分析与地图生产。同时提出GeoAI与地图学整合中需应对的五项潜在伦理挑战：商品化、责任归属、隐私保护、偏差问题，以及透明度、可解释性与溯源性的综合议题。最后，我们为未来GeoAI制图研究指明四个潜在方向：GeoAI驱动的主动制图符号学、人机协同的GeoAI制图、基于GeoAI的地图即服务、生成式GeoAI制图。