GENERATIVE ADVERSARIAL NETWORKS TO GENERALISE URBAN AREAS IN TOPOGRAPHIC MAPS

11 citations as recorded by crossref.

1. Determining the optimal generalization operators for building footprints using an improved graph neural network model X. Niu et al. 10.1080/10106049.2024.2306265
2. Artificial intelligence studies in cartography: a review and synthesis of methods, applications, and ethics Y. Kang et al. 10.1080/15230406.2023.2295943
3. Learning from vector data: enhancing vector-based shape encoding and shape classification for map generalization purposes M. Knura 10.1080/15230406.2023.2273397
4. GANmapper: geographical data translation A. Wu & F. Biljecki 10.1080/13658816.2022.2041643
5. An Assessment of the Map-Style Influence on Generalization with CycleGAN: Taking Line Features as an Example H. Yu et al. 10.3390/ijgi13120418
6. Integrating domain knowledge and graph convolutional neural networks to support river network selection H. Yu et al. 10.1111/tgis.13104
7. Automated Map Generalization: Emerging Techniques and New Trends (Editorial) X. Zhang et al. 10.1007/s41651-024-00174-4
8. Evaluating the Geometry of Objects in Cartographic Generalization with Hu's Invariants J. Bac-Bronowicz et al. 10.1080/00087041.2024.2323333
9. Polyline simplification using a region proposal network integrating raster and vector features B. Jiang et al. 10.1080/15481603.2023.2275427
10. Constraint-Based Evaluation of Map Images Generalized by Deep Learning A. Courtial et al. 10.1007/s41651-022-00104-2
11. DeepMapScaler: a workflow of deep neural networks for the generation of generalised maps A. Courtial et al. 10.1080/15230406.2023.2267419