13 citations as recorded by crossref.

1. DeepMapScaler: a workflow of deep neural networks for the generation of generalised maps A. Courtial et al. https://doi.org/10.1080/15230406.2023.2267419
2. Keeping walls straight: data model and training set size matter for deep learning in building generalization C. Fu et al. https://doi.org/10.1080/15230406.2023.2264757
3. An Assessment of the Map-Style Influence on Generalization with CycleGAN: Taking Line Features as an Example H. Yu et al. https://doi.org/10.3390/ijgi13120418
4. An Improved Generative Adversarial Network for Generating Multi-Scale Electronic Map Tiles Considering Cartographic Requirements W. Zhu et al. https://doi.org/10.3390/ijgi13110398
5. Learning from vector data: enhancing vector-based shape encoding and shape classification for map generalization purposes M. Knura https://doi.org/10.1080/15230406.2023.2273397
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7. A Comparative Study of Various Deep Learning Approaches to Shape Encoding of Planar Geospatial Objects X. Yan & M. Yang https://doi.org/10.3390/ijgi11100527
8. GeoAI for map generalization in multi-scale cartography: foundations, a research agenda, and interdisciplinary perspectives Z. Zhou et al. https://doi.org/10.1080/13658816.2026.2626935
9. Deep learning in automatic map generalization: achievements and challenges X. Yan et al. https://doi.org/10.1080/10095020.2025.2480815
10. Machine learning in cartography L. Harrie et al. https://doi.org/10.1080/15230406.2023.2295948
11. Incorporating ideas of structure and meaning in interactive multi scale mapping environments G. Touya et al. https://doi.org/10.1080/23729333.2023.2215960
12. Reasoning cartographic knowledge in deep learning-based map generalization with explainable AI C. Fu et al. https://doi.org/10.1080/13658816.2024.2369535
13. Multi-label Contextual Operator Inference in Knowledge Acquisition for Building Generalization: A Multi-modal Learning Approach J. Senn et al. https://doi.org/10.1007/s41651-026-00251-w