Polyline simplification using a region proposal network integrating raster and vector features

References

• Ai, T. 2021. “Some Thoughts on Deep Learning Enabling Cartography.” Acta Geodaetica et Cartographica Sinica 50 (9): 1170. https://doi.org/10.11947/j.AGCS.2021.20210091.
   Google Scholar
• Ai, T., S. Ke, M. Yang, and J. Li. 2017. “Envelope Generation and Simplification of Polylines Using Delaunay Triangulation.” International Journal of Geographical Information Science 31 (2): 297–18. https://doi.org/10.1080/13658816.2016.1197399.
   Web of Science ®Google Scholar
• Cheng, B., Q. Liu, X. Li, and Y. Wang. 2013. “Building Simplification Using Backpropagation Neural Networks: A Combination of Cartographers’ Expertise and Raster-Based Local Perception.” GIScience & Remote Sensing 50 (5): 527–542. https://doi.org/10.1080/15481603.2013.823748.
   Web of Science ®Google Scholar
• Chen, Q., L. Wang, S. L. Waslander, and X. Liu. 2020. “An End-To-End Shape Modeling Framework for Vectorized Building Outline Generation from Aerial Images.” ISPRS Journal of Photogrammetry and Remote Sensing 170 (December): 114–126. https://doi.org/10.1016/j.isprsjprs.2020.10.008.
   Google Scholar
• Courtial, A., A. E. Ayedi, G. Touya, and X. Zhang. 2020. “Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation.” ISPRS International Journal of Geo-Information 9 (5): 338. https://doi.org/10.3390/ijgi9050338.
   Web of Science ®Google Scholar
• Courtial, A., G. Touya, and X. Zhang. 2021. “Generative Adversarial Networks to Generalise Urban Areas in Topographic Maps.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2021 (June): 15–22. https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-15-2021.
   Google Scholar
• Courtial, A., G. Touya, and X. Zhang. 2022. “Constraint-Based Evaluation of Map Images Generalized by Deep Learning.” Journal of Geovisualization and Spatial Analysis 6 (1): 13. https://doi.org/10.1007/s41651-022-00104-2.
   Google Scholar
• Courtial, A., G. Touya, and X. Zhang. 2023. “Deriving Map Images of Generalised Mountain Roads with Generative Adversarial Networks.” International Journal of Geographical Information Science 37 (3): 499–528. https://doi.org/10.1080/13658816.2022.2123488.
   Web of Science ®Google Scholar
• Douglas, D. H., and T. K. Peucker. 1973. “Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or Its Caricature.” Cartographica: The International Journal for Geographic Information and Geovisualization 10 (2): 112–122. https://doi.org/10.3138/FM57-6770-U75U-7727.
   Google Scholar
• Duan, P., H. Qian, H. He, L. Xie, and D. Luo. 2020. “A Line Simplification Method Based on Support Vector Machine.” Geomatics and Information Science of Wuhan University 45 (5): 744–52, 783. https://doi.org/10.13203/j.whugis20180434.
   Google Scholar
• Du, J., F. Wu, Z. H. U. Li, C. Liu, and A. Wang. 2022. “An Ensemble Learning Simplification Approach Based on Multiple Machine-Learning Algorithms with the Fusion Using of Raster and Vector Data and a Use Case of Coastline Simplification.” Acta Geodaetica et Cartographica Sinica 51 (3): 373. https://doi.org/10.11947/j.AGCS.2022.20210135.
   Google Scholar
• Du, J., F. Wu, R. Xing, X. Gong, and L. Yu. 2021. “Segmentation and Sampling Method for Complex Polyline Generalization Based on a Generative Adversarial Network.” Geocarto International 37 (14), February): 4158–4180. https://doi.org/10.1080/10106049.2021.1878288.
   Web of Science ®Google Scholar
• Feng, Y., F. Thiemann, and M. Sester. 2019. “Learning Cartographic Building Generalization with Deep Convolutional Neural Networks.” ISPRS International Journal of Geo-Information 8 (6): 258. https://doi.org/10.3390/ijgi8060258.
   Web of Science ®Google Scholar
• Girshick, R. 2015. “Fast R-CNN.” In 2015 IEEE International Conference on Computer Vision (ICCV) 1440–1448. https://doi.org/10.1109/ICCV.2015.169.
   Google Scholar
• Isola, P., J. Y. Zhu, T. Zhou, and A. A. Efros. 2017. “Image-To-Image Translation with Conditional Adversarial Networks.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 5967–5976. Honolulu, HI: IEEE. Accessed August 23, 2022. https://doi.org/10.1109/CVPR.2017.632.
   Google Scholar
• Jiang, B. 2003. “Line Simplification Using Self Organizing Maps,” January. Accssed November 25, 2022. https://www.academia.edu/2611465.
   Google Scholar
• Kang, Y., S. Gao, and R. E. Roth. 2019. “Transferring Multiscale Map Styles Using Generative Adversarial Networks.” International Journal of Cartography 5 (2–3): 115–141. https://doi.org/10.1080/23729333.2019.1615729.
   Web of Science ®Google Scholar
• Karsznia, I., and K. Sielicka. 2020. “When Traditional Selection Fails: How to Improve Settlement Selection for Small-Scale Maps Using Machine Learning.” ISPRS International Journal of Geo-Information 9 (4): 230. https://doi.org/10.3390/ijgi9040230.
   Web of Science ®Google Scholar
• Karsznia, I., and R. Weibel. 2018. “Improving Settlement Selection for Small-Scale Maps Using Data Enrichment and Machine Learning.” Cartography and Geographic Information Science 45 (2): 111–127. https://doi.org/10.1080/15230406.2016.1274237.
   Web of Science ®Google Scholar
• Kent, A. 2017. “Trust Me, I’m a Cartographer: Post-Truth and the Problem of Acritical Cartography.” The Cartographic Journal 54 (3): 193–195. https://doi.org/10.1080/00087041.2017.1376489.
   Web of Science ®Google Scholar
• LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436–444. https://doi.org/10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• Lee, J., H. Jang, J. Yang, and K. Yu. 2017. “Machine Learning Classification of Buildings for Map Generalization.” ISPRS International Journal of Geo-Information 6 (10): 309. https://doi.org/10.3390/ijgi6100309.
   Web of Science ®Google Scholar
• Li, W., and C.-Y. Hsu. 2020. “Automated Terrain Feature Identification from Remote Sensing Imagery: A Deep Learning Approach.” International Journal of Geographical Information Science 34 (4): 637–660. https://doi.org/10.1080/13658816.2018.1542697.
   Web of Science ®Google Scholar
• Li, Z., and S. Openshaw. 1992. “Algorithms for Automated Line Generalization 1 Based on a Natural Principle of Objective Generalization.” International Journal of Geographical Information Systems 6 (5): 373–389. https://doi.org/10.1080/02693799208901921.
   Web of Science ®Google Scholar
• Liu, P., X. Li, W. Liu, and T. Ai. 2016. “Fourier-Based Multi-Scale Representation and Progressive Transmission of Cartographic Curves on the Internet.” Cartography and Geographic Information Science 43 (5): 454–468. https://doi.org/10.1080/15230406.2015.1088799.
   Web of Science ®Google Scholar
• McMaster, R. B. 1986. “A Statistical Analysis of Mathematical Measures for Linear Simplification.” The American Cartographer 13 (2): 103–116. https://doi.org/10.1559/152304086783900059.
   Web of Science ®Google Scholar
• Park, W., and K. Yu. 2010. “Generalization of Digital Topographic Map Using Hybrid Line Simplification.” In Proceedings of the ASPRS 2010 Annual Conference 6. San Diego.
   Google Scholar
• Rao, J., S. Gao, Y. Kang, and Q. Huang. 2020. “LSTM-Trajgan: A Deep Learning Approach to Trajectory Privacy Protection.” arXiv Accessed. https://doi.org/10.48550/arXiv.2006.10521. November 25, 2022.
   Google Scholar
• Ren, S., K. He, R. Girshick, and J. Sun. 2017. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (6): 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031.
   PubMed Web of Science ®Google Scholar
• Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, edited by N. Navab, J. Hornegger, M. W. William, and F. F. Alejandro, Lecture Notes in Computer Science 234–241. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-24574-4_28.
   Google Scholar
• Sandler, M., A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen. 2018. “Mobilenetv2: Inverted Residuals and Linear Bottlenecks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Salt Lake City, UT, USA) 4510–4520 doi:10.1109/CVPR.2018.00474.
   Google Scholar
• Sester, M., Y. Feng, and F. Thiemann. 2018. “BUILDING GENERALIZATION USING DEEP LEARNING.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII–4 (September): 565–572. https://doi.org/10.5194/isprs-archives-XLII-4-565-2018.
   Google Scholar
• Song, J., and R. Miao. 2016. “A Novel Evaluation Approach for Line Simplification Algorithms Towards Vector Map Visualization.” ISPRS International Journal of Geo-Information 5 (12): 223. https://doi.org/10.3390/ijgi5120223.
   Web of Science ®Google Scholar
• Stanislawski, L. V., B. P. Buttenfield, P. Bereuter, S. Savino, and C. A. Brewer. 2014. “Generalisation Operators”. In Abstracting Geographic Information in a Data Rich World, In edited by D. Burghardt, C. Duchêne, and W. Mackaness, Lecture Notes in Geoinformation and Cartography 157–195. Cham: Springer International Publishing. Accessed November 25, 2022. https://doi.org/10.1007/978-3-319-00203-3_6
   Google Scholar
• Touya, G., X. Zhang, and I. Lokhat. 2019. “Is Deep Learning the New Agent for Map Generalization?” International Journal of Cartography 5 (2–3): 142–157. https://doi.org/10.1080/23729333.2019.1613071.
   Web of Science ®Google Scholar
• Visvalingam, M., and J. D. Whyatt. 1993. “Line Generalisation by Repeated Elimination of Points.” The Cartographic Journal 30 (1): 46–51. https://doi.org/10.1179/caj.1993.30.1.46.
   Web of Science ®Google Scholar
• Vrotsou, K., H. Janetzko, C. Navarra, G. Fuchs, D. Spretke, F. Mansmann, N. Andrienko, and G. Andrienko. 2015. “SimpliFly: A Methodology for Simplification and Thematic Enhancement of Trajectories.” IEEE Transactions on Visualization and Computer Graphics 21 (1): 107–121. https://doi.org/10.1109/TVCG.2014.2337333.
   PubMed Web of Science ®Google Scholar
• Wang, Z., and J.-C. Müller. 1998. “Line Generalization Based on Analysis of Shape Characteristics.” Cartography and Geographic Information Systems 25 (1): 3–15. https://doi.org/10.1559/152304098782441750.
   Google Scholar
• Wessel, P., and W. H. F. Smith. 1996. “A Global, Self-Consistent, Hierarchical, High-Resolution Shoreline Database.” Journal of Geophysical Research: Solid Earth 101 (B4): 8741–8743. https://doi.org/10.1029/96JB00104.
   Web of Science ®Google Scholar
• Yan, X., T. Ai, M. Yang, X. Tong, and Q. Liu. 2020. “A Graph Deep Learning Approach for Urban Building Grouping.” Geocarto International: 1–24. https://doi.org/10.1080/10106049.2020.1856195.
   Web of Science ®Google Scholar
• Yang, M., T. Yuan, X. Yan, T. Ai, and C. Jiang. 2022. “A Hybrid Approach to Building Simplification with an Evaluator from a Backpropagation Neural Network.” International Journal of Geographical Information Science 36 (2): 280–309. https://doi.org/10.1080/13658816.2021.1873998.
   Web of Science ®Google Scholar
• Yu, W., and Y. Chen. 2022. “Data‐Driven Polyline Simplification Using a Stacked Autoencoder‐Based Deep Neural Network.” Transactions in GIS 26 (5): 2302–2325. June, tgis.12965. https://doi.org/10.1111/tgis.12965.
   Web of Science ®Google Scholar
• Zhou, Q., and Z. Li. 2017. “A Comparative Study of Various Supervised Learning Approaches to Selective Omission in a Road Network.” The Cartographic Journal 54 (3): 254–264. https://doi.org/10.1179/1743277414Y.0000000083.
   Web of Science ®Google Scholar