Is deep learning the new agent for map generalization?

References

• Borgo, R., Lee, B., Bach, B., Fabrikant, S., Jianu, R., Kerren, A., … Zhou, M. (2017). Crowdsourcing for information visualization: Promises and pitfalls. In D. Archambault, H. Purchase, & T. Hoßfeld (Eds.), Evaluation in the crowd. Crowdsourcing and human-centered experiments (pp. 96–138). Cham: Springer.
   Google Scholar
• Burghardt, D., & Neun, M. (2006). Automated sequencing of generalisation services based on collaborative filtering. In M. Raubal, H. J. Miller, A. U. Frank, & M. F. Goodchild (Eds.), Geographic information science – 4th international conference GIScience (pp. 41–46). Münster, Germany: IFGI prints.
   Google Scholar
• Duchêne, C., Baella, B., Brewer, C. A., Burghardt, D., Buttenfield, B. P., Gaffuri, J., … Wiedemann, A. (2014). Generalisation in practice within national mapping agencies. In D. Burghardt, C. Duchêne, & W. Mackaness (Eds.), Abstracting geographic information in a data rich world (pp. 329–391). Cham: Springer International Publishing.
   Google Scholar
• Duchêne, C., Touya, G., Taillandier, P., Gaffuri, J., Ruas, A., & Renard, J. (2018). Multi-agents systems for cartographic generalization: Feedback from past and on-going research (Technical report). France: IGN.
   Google Scholar
• Cheng, C., Liu, Q., Li, X., & Wang, Y. (2013). Building simplification using backpropagation neural networks: A combination of cartographers’ expertise and raster-based local perception. GIScience & Remote Sensing, 50(5), 527–542. doi: 10.1080/15481603.2013.823748
   Web of Science ®Google Scholar
• Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … Bengio, Y. (2014). Generative adversarial nets. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), Advances in neural information processing systems 27 (pp. 2672–2680). Montreal: Curran Associates, Inc.
   Google Scholar
• Grünreich, D. (1985). Ein vorschlag zum aufbau einer grossmassstäbigen topographischkartographischen-Datenbank unter besonderer berücksichtigung der grundrissdateides ALK-systems. Nachrichten aus dem Karten- und Vermessungswesen, Series I, 95:55+.
   Google Scholar
• Harrie, L. E. (2003). Weight-Setting and quality assessment in simultaneous graphic generalization. The Cartographic Journal, 40(3), 221–233. doi: 10.1179/000870403225012925
   Web of Science ®Google Scholar
• Harrie, L., Stigmar, H., & Djordjevic, M. (2015). Analytical estimation of map readability. ISPRS International Journal of Geo-Information, 4(2), 418–446. doi: 10.3390/ijgi4020418
   Web of Science ®Google Scholar
• Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017). Image-to-Image translation with conditional adversarial networks. CVPR 2017.
   Google Scholar
• Karsznia, I., & Weibel, R. (2018). Improving settlement selection for small-scale maps using data enrichment and machine learning. Cartography and Geographic Information Science, 45(2), 111–127. doi: 10.1080/15230406.2016.1274237
   Web of Science ®Google Scholar
• Kilpelainen, T. (2000). Knowledge acquisition for generalization rules. Cartography and Geographic Information Science, 27(1), 41–50. doi: 10.1559/152304000783547993
   Google Scholar
• Landrieu, L., & Simonovsky, M. (2018). Large-scale point cloud semantic segmentation with superpoint graphs. In Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, USA.
   Google Scholar
• LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. doi: 10.1109/5.726791
   Web of Science ®Google Scholar
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. doi: 10.1038/nature14539
   PubMed Web of Science ®Google Scholar
• Ma, L. (2017). Features extraction of buildings and generalization using deep learning. In Proceedings of 28th International Cartographic Conference, Washington, DC, USA.
   Google Scholar
• Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. doi: 10.1109/TKDE.2009.191
   Web of Science ®Google Scholar
• Plazanet, C., Bigolin, N., & Ruas, A. (1998). Experiments with learning techniques for spatial model enrichment and line generalization. Geoinformatica, 2(4), 315–333. doi: 10.1023/A:1009753320636
   Google Scholar
• Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. CoRR, abs/1505.04597.
   Google Scholar
• Ruas, A., & Duchêne, C. (2007). A prototype generalisation system based on the multi-agent system paradigm. In W. A. Mackaness, A. Ruas, & L. T. Sarjakoski (Eds.), Generalisation of geographic information (pp. 269–284). Amsterdam: Elsevier.
   Google Scholar
• Sester, M. (2000). Knowledge acquisition for the automatic interpretation of spatial data. International Journal of Geographical Information Science, 14(1), 1–24. doi: 10.1080/136588100240930
   Web of Science ®Google Scholar
• Sester, M., Feng, Y., & Thiemann, F. (2018). Building generalization using deep learning. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4, 565–572. doi: 10.5194/isprs-archives-XLII-4-565-2018
   Google Scholar
• Simo-Serra, E., Iizuka, S., Sasaki, K., & Ishikawa, H. (2016). Learning to simplify: Fully convolutional networks for rough sketch cleanup. ACM Transactions on Graphics, 35(4), article no. 121. doi: 10.1145/2897824.2925972
   Web of Science ®Google Scholar
• Steiniger, S., Lange, T., Burghardt, D., & Weibel, R. (2008). An approach for the classification of urban building structures based on discriminant analysis techniques. Transactions in GIS, 12(1), 31–59. doi: 10.1111/j.1467-9671.2008.01085.x
   Google Scholar
• Stoter, J., Burghardt, D., Duchêne, C., Baella, B., Bakker, N., Blok, C., … Schmid, S. (2009). Methodology for evaluating automated map generalization in commercial software. Computers, Environment and Urban Systems, 33(5), 311–324. doi: 10.1016/j.compenvurbsys.2009.06.002
   Web of Science ®Google Scholar
• Stoter, J., Zhang, X., Stigmar, H., & Harrie, L. (2014). Evaluation in generalisation. In D. Burghardt, C. Duchêne, & W. Mackaness (Eds.), Abstracting geographic information in a data Rich world, Lecture notes in geoinformation and cartography (pp. 259–297). Springer International Publishing.
   Google Scholar
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9.
   Google Scholar
• Taillandier, P., Duchêne, C., & Drogoul, A. (2011). Automatic revision of rules used to guide the generalisation process in systems based on a trial and error strategy. International Journal of Geographical Information Science, 25(12), 1971–1999. doi: 10.1080/13658816.2011.566568
   Web of Science ®Google Scholar
• Touya, G. (2010). A road network selection process based on data enrichment and structure detection. Transactions in GIS, 14(5), 595–614. doi: 10.1111/j.1467-9671.2010.01215.x
   Web of Science ®Google Scholar
• Touya, G. (2015). Lessons learned from research on multimedia summarization. In Proceedings of 18th ICA workshop on generalisation and multiple representation, Rio de Janeiro, Brazil.
   Google Scholar
• Touya, G. (2017). Vers l'automatisation de la production de cartes. Habilitation thesis, Université Paris Est.
   Google Scholar
• Touya, G. and Dumont, M. (2017). Progressive block graying and landmarks enhancing as intermediate representations between buildings and urban areas. In Proceedings of 20th ICA workshop on generalisation and multiple representation, Washington, DC, USA.
   Google Scholar
• Touya, G., Bucher, B., Falquet, G., Jaara, K., & Steiniger, S. (2014). Modelling geographic relationships in automated environments. In D. Burghardt, C. Duchêne, & W. Mackaness (Eds.), Abstracting geographic information in a data rich world (pp. 53–82). Berlin: Springer.
   Google Scholar
• Touya, G., Lokhat, I., & Duchêne, C. (2019). CartAGen: an Open Source Research Platform for Map Generalization. ICC’19.
   Google Scholar
• Weibel, R., Keller, S., & Reichenbacher, T. (1995). Overcoming the knowledge acquisition bottleneck in map generalization: The role of interactive systems and computational intelligence. In A. U. Frank, & W. Kuhn (Eds.), Spatial information theory, a theoretical basis for GIS (pp. 139–156). Berlin: Springer.
   Google Scholar
• Xu, Y., Chen, Z., Xie, Z., & Wu, L. (2017). Quality assessment of building footprint data using a deep autoencoder network. International Journal of Geographical Information Science, 31(10), 1929–1951. doi: 10.1080/13658816.2017.1341632
   Web of Science ®Google Scholar
• Zhou, Q., & Li, Z. (2016). Empirical determination of geometric parameters for selective omission in a road network. International Journal of Geographical Information Science, 30(2), 263–299. doi: 10.1080/13658816.2015.1085538
   Web of Science ®Google Scholar
• Zhou, Q., & Li, Z. (2017). A comparative study of various supervised learning approaches to selective omission in a road network. The Cartographic Journal, 54(3), 254–264. doi: 10.1179/1743277414Y.0000000083
   Web of Science ®Google Scholar