Neural map style transfer exploration with GANs

References

• Arbelot, B., Vergne, R., Hurtut, T., & Thollot, J. (2017). Local texture-based color transfer and colorization. Computers and Graphics, 62(5), 15–27. https://doi.org/https://doi.org/10.1016/j.cag.2016.12.005
   Google Scholar
• Bogucka, E. P., & Meng, L. (2019). Projecting emotions from artworks to maps using neural style transfer. In Proceedings of the ICA (Vol. 2, pp. 1–8). Copernicus GmbH.
   Google Scholar
• Chen, X., Chen, S., Xu, T., Yin, B., Peng, J., Mei, X., & Li, H. (2020). SMAPGAN: Generative adversarial network-based semisupervised styled map tile generation method. In IEEE Transactions on Geoscience and Remote Sensing (pp. 1–19). Conference Name: IEEE Transactions on Geoscience and Remote Sensing.
   Google Scholar
• Christophe, S., Dumenieu, B., Turbet, J., Hoarau, C., Mellado, N., Ory, J., Loi, H., Masse, A., Arbelot, B., Vergne, R., Brédif, M., Hurtut, T., Thollot, J., & Vanderhaeghe, D. (2016). Map style formalization: Rendering techniques extension for cartography. In P. Bénard, H. Winnemöller (Eds.), Proceedings of Non-Photorealistic Animation and Rendering. The Eurographics Association. https://doi.org/http://doi.org/10.2312/exp.20161064
   Google Scholar
• Christophe, S., & Hoarau, C. (2012). Expressive map design based on pop art: Revisit of semiology of graphics?. Cartographic Perspectives 73, 61–74. https://doi.org/https://doi.org/10.14714/CP73.646
   Google Scholar
• Courtial, A., Ayedi, A. E., Touya, G., & Zhang, X. (2020). Exploring the potential of deep learning segmentation for mountain roads generalisation. ISPRS International Journal of Geo-Information, 9(5), 338. https://doi.org/https://doi.org/10.3390/ijgi9050338
   Google Scholar
• Dumoulin, V., Shlens, J., & Kudlur, M. (2016). A learned representation for artistic style. CoRR arXiv:1508.06576.
   Google Scholar
• Ganguli, S., Garzon, P., & Glaser, N. (2019). GeoGAN: A conditional GAN with reconstruction and style loss to generate standard layer of maps from satellite images. arXiv:1902.05611 [cs]. arXiv: 1902.05611.
   Google Scholar
• Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A neural algorithm of artistic style. CoRR., arXiv:1508.06576.
   Google Scholar
• Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2414–2423.
   Google Scholar
• Gatys, L. A., Ecker, A. S., Bethge, M., Hertzmann, A., & Shechtman, E. (2017). Controlling perceptual factors in neural style transfer. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
   Google Scholar
• Huang, X., Liu, M.-Y., Belongie, S., & Kautz, J. (2018). Multimodal unsupervised image-to-image translation. arXiv: 1804.04732.
   Google Scholar
• Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2016). Image-to-image translation with conditional adversarial networks. In CVPR 2017.
   Google Scholar
• Jenny, B., Heitzler, M., Singh, D., Farmakis-Serebryakova, M., Liu, J., & Hurni, L. (2020). Cartographic relief shading with neural networks. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1225–1235, doi: https://doi.org/10.1109/TVCG.2020.3030456.
   Google Scholar
• Kang, Y., Gao, S., & Roth, R. E. (2019). Transferring multiscale map styles using generative adversarial networks. International Journal of Cartography, 5(2-3), 115–141. https://doi.org/https://doi.org/10.1080/23729333.2019.1615729
   Google Scholar
• Kang, Y., Rao, J., Wang, W., Peng, B., Gao, S., & Zhang, F. (2020). Towards cartographic knowledge encoding with deep learning: A case study of building generalization. In AutoCarto 2020, the 23rd International Research Symposium on Cartography and GIScience.
   Google Scholar
• Kyprianidis, J. E., Collomosse, J., Wang, T., & Isenberg, T. (2013). State of the ‘Art’: A taxonomy of artistic stylization techniques for images and video. IEEE Transactions on Visualization and Computer Graphics, 19(5), 866–885. https://doi.org/https://doi.org/10.1109/TVCG.2012.160
   Google Scholar
• Li, J., Chen, Z., Zhao, X., & Shao, L. (2020). MapGAN: An intelligent generation model for network tile maps. Sensors, 20(11), 3119. https://doi.org/https://doi.org/10.3390/s20113119Number: 11 Publisher: Multidisciplinary Digital Publishing Institute
   Google Scholar
• Li, Y., Wang, N., Liu, J., & Hou, X. (2017). Demystifying neural style transfer. arXiv:1701.01036.
   Google Scholar
• Loi, H., Hurtut, T., Vergne, R., & Thollot, J. (2017). Programmable 2d arrangements for element texture design. ACM Transactions on Graphics, 36(4): 105a. https://doi.org/https://doi.org/10.1145/3072959.2983617
   Google Scholar
• Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. arXiv:1411.4038.
   Google Scholar
• Luan, F., Paris, S., Shechtman, E., & Bala, K. (2017). Deep photo style transfer. arXiv:1703.07511.
   Google Scholar
• Ory, J., Christophe, S., Fabrikant, S. I., & Bucher, B. (2015). How do map readers recognize a topographic mapping style?. The Cartographic Journal, 52(2), 193–203. https://doi.org/https://doi.org/10.1080/00087041.2015.1119459
   Google Scholar
• Park, D. Y., & Lee, K. H. (2019). Arbitrary Style Transfer with Style-Attentional Networks. In arXiv:1812.02342 [cs]. arXiv: 1812.02342.
   Google Scholar
• Semmo, A., Isenberg, T., & Döllner, J. (2017). Neural style transfer: A paradigm shift for image-based artistic rendering? In NPAR 2017 – Proceedings of Expressive Joint Symposium (pp. 5:1–5:13). ACM.
   Google Scholar
• Simo-Serra, E., Iizuka, S., & Ishikawa, H. (2017). Mastering sketching: Adversarial augmentation for structured prediction. arXiv:1703.08966 [cs]. arXiv: 1703.08966.
   Google Scholar
• Weinman, J., Chen, Z., Gafford, B., Gifford, N., Lamsal, A., & Niehus-Staab, L. (2019). Deep neural networks for text detection and recognition in historical maps. In IEEE 2019 International Conference on Document Analysis and Recognition (ICDAR) (pp. 902–909). ISSN: 2379–2140.
   Google Scholar
• Xu, Z., Wilber, M., Fang, C., Hertzmann, A., & Jin, H. (2019). Learning from multi-domain artistic images for arbitrary style transfer. In C. S. Kaplan, A. Forbes and S. DiVerdi (Eds.), ACM/EG Expressive Symposium. The Eurographics Association.
   Google Scholar
• Zhang, Y., Yin, Y., Zimmermann, R., Wang, G., Varadarajan, J., & Ng, S.-K. (2020). An enhanced GAN model for automatic satellite-to-map image conversion. IEEE Access, 8, 176704–176716. https://doi.org/https://doi.org/10.1109/Access.6287639Conference Name: IEEE Access
   Google Scholar
• Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In Computer Vision (ICCV), 2017 IEEE International Conference on.
   Google Scholar