# Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics arXiv: https://arxiv.org/abs/2312.07901 ยท HTML: https://mlsanity.com/paper/2312.07901/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 239 references that are in the map. - First posted on arXiv 13 Dec 2023. ## Builds on (most cited first, 24 of 24) - [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023) - [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016) - [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016) - [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022) - [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017) - [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019) - [LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection](https://mlsanity.com/paper/2006.10521/) (2020) - [Human-in-the-loop Artificial Intelligence](https://mlsanity.com/paper/1710.08191/) (2017) - [GeoGAN: A Conditional GAN with Reconstruction and Style Loss to Generate Standard Layer of Maps from Satellite Images](https://mlsanity.com/paper/1902.05611/) (2019) - [Transferring Multiscale Map Styles Using Generative Adversarial Networks](https://mlsanity.com/paper/1905.02200/) (2019) - [Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection](https://mlsanity.com/paper/2112.06104/) (2021) - [A smart local moving algorithm for large-scale modularity-based community detection](https://mlsanity.com/paper/1308.6604/) (2013) - [Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks](https://mlsanity.com/paper/2007.07404/) (2020) - [MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale](https://mlsanity.com/paper/2111.15592/) (2021) - [The Ethics of AI-Generated Maps: A Study of DALLE 2 and Implications for Cartography](https://mlsanity.com/paper/2304.10743/) (2023) - [Towards the automated large-scale reconstruction of past road networks from historical maps](https://mlsanity.com/paper/2202.04883/) (2022) - [Cartographic Relief Shading with Neural Networks](https://mlsanity.com/paper/2010.01256/) (2020) - [Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation](https://mlsanity.com/paper/2101.02144/) (2021) - [Detecting Visual Design Principles in Art and Architecture through Deep Convolutional Neural Networks](https://mlsanity.com/paper/2108.04048/) (2021) - [Deep Convolutional Neural Networks for Map-Type Classification](https://mlsanity.com/paper/1805.10402/) (2018) - [Narrative Cartography with Knowledge Graphs](https://mlsanity.com/paper/2112.00970/) (2021) - [STICC: A multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity](https://mlsanity.com/paper/2203.09611/) (2022) - [Identifying Wetland Areas in Historical Maps using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2108.04107/) (2021) - [Generating Multi-scale Maps from Remote Sensing Images via Series Generative Adversarial Networks](https://mlsanity.com/paper/2103.16909/) (2021) ## Built on by (most cited first, 4 of 4) - [GIS Copilot: Towards an Autonomous GIS Agent for Spatial Analysis](https://mlsanity.com/paper/2411.03205/) (2024) - [MapColorAI: Designing Contextually Relevant Choropleth Map Color Schemes Using a Large Language Model](https://mlsanity.com/paper/2503.15502/) (2025) - [CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation](https://mlsanity.com/paper/2505.09936/) (2025) - [ProKG-Dial: Progressive Multi-Turn Dialogue Construction with Domain Knowledge Graphs](https://mlsanity.com/paper/2508.01869/) (2025) ## Abstract (arXiv) 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.