1. [1] X. Lin, S. Ma, J. Jiang, Y. Hou, and T. Wo, ''Error bounded line simplification algorithms for trajectory compression: An experimental evaluation'', ACM Transactions on Database Systems (TODS), vol. 46, no. 3, pp. 1-44, 2021. [DOI:10.1145/3474373]

2. [2] J. L. Morrison, ''Map Generalization: Theory, Practice, and Economics'', in Second International Symposium on Computer-Assisted Cartography, 1975.

3. [3] K. Misue, ''Conditions of Preserving Mental Images by Contour Deformation'', in Proceedings of the 15th International Symposium on Visual Information Communication and Interaction, 2022, pp. 1-5. [DOI:10.1145/3554944.3554953]

4. [4] J. Jaafar, ''Line generalization: least square with double tolerance'', WIT Transactions on Information and Communication Technologies, vol. 26, 2002.

5. [5] B. Liu, X. Liu, D. Li, Y. Shi, G. Fernandez, and Y. Wang, ''A vector line simplification algorithm based on the Douglas--Peucker algorithm, monotonic chains and dichotomy'', ISPRS International Journal of Geo-Information, vol. 9, no. 4, p. 251, 2020. [DOI:10.3390/ijgi9040251]

6. [6] A. Pinandito and C. P. Wulandari, ''Integrating douglas-peucker line simplification into routeboxer algorithm on a map-based Android application'', in Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology, 2020, pp. 213-219. [DOI:10.1145/3427423.3427446]

7. [7] Y. Ren, J. Tang, and S. Wu, ''Geometric properties preserved line simplification algorithm based on fractal'', in 2011 IEEE International Geoscience and Remote Sensing Symposium, 2011, pp. 3019-3022. [DOI:10.1109/IGARSS.2011.6049852]

8. [8] V. H. Clayton, ''Cartographic generalization: a review of feature simplification and systematic point algorithms'', 1985.

9. [9] R. B. McMaster, ''Automated line generalization'', Cartographica: The International Journal for Geographic Information and Geovisualization, vol. 24, no. 2, pp. 74-111, 1987. [DOI:10.3138/3535-7609-781G-4L20]

10. [10] J. A. Young, ''Effects of automated cartographic generalization on linear map features'', Virginia Tech, 1991.

11. [11] D. Kotsur and V. Tereshchenko, ''Optimization heuristics for computing the Voronoi skeleton'', in International Conference on Computational Science, 2019, pp. 96-111. [DOI:10.1007/978-3-030-22734-0_8]

12. [12] D. H. Douglas and T. K. Peucker, ''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, vol. 10, no. 2, pp. 112-122, 1973. [DOI:10.3138/FM57-6770-U75U-7727]

13. [13] T. Samsonov and O. Yakimova, ''Regression modeling of reduction in spatial accuracy and detail for multiple geometric line simplification procedures'', International Journal of Cartography, vol. 6, no. 1, pp. 47-70, 2020. [DOI:10.1080/23729333.2019.1615745]

14. [14] R. B. McMaster, ''A mathematical evaluation of simplification algorithms (in computer cartography)'', Scanning Electron Microsc Meet at, pp. 267-276, 1983.

15. [15] B. J. Kronenfeld and J. Deng, ''Between the Lines: Measuring Areal Displacement in Line Simplification'', Advances in Cartography and GIScience of the ICA, vol. 1, p. NA-NA, 2019. [DOI:10.5194/ica-adv-1-9-2019]

16. [16] G. F. Jenks, ''Geographic logic in line generalization'', Cartographica: The International Journal for Geographic Information and Geovisualization, vol. 26, no. 1, pp. 27-42, 1989. [DOI:10.3138/L426-1756-7052-536K]

17. [17] Y. Shen, T. Ai, and Y. He, ''A new approach to line simplification based on image processing: A case study of water area boundaries'', ISPRS International Journal of Geo-Information, vol. 7, no. 2, p. 41, 2018. [DOI:10.3390/ijgi7020041]

18. [18] P. Zhao, Q. Zhao, C. Zhang, G. Su, Q. Zhang, and W. Rao, ''CLEAN: Frequent pattern-based trajectory compression and computation on road networks'', China Communications, vol. 17, no. 5, pp. 119-136, 2020. [DOI:10.23919/JCC.2020.05.011]

19. [19] J. Du, F. Wu, J. Yin, C. Liu, and X. Gong, ''Polyline simplification based on the artificial neural network with constraints of generalization knowledge'', Cartography and Geographic Information Science, vol. 49, no. 4, pp. 313-337, 2022. [DOI:10.1080/15230406.2021.2013944]

20. [20] M. Visvalingam and J. D. Whyatt, ''Line generalization by repeated elimination of points'', in Landmarks in Mapping, Routledge, 2017, pp. 144-155. [DOI:10.4324/9781351191234-14]

21. [21] M. Mizuta, ''Algebraic curve fitting for multidimensional data with exact squares distance'', in 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No. 96CH35929), 1996, vol. 1, pp. 516-521. [DOI:10.1109/ICSMC.1996.569845]

22. [22] B. Sheng, S. Wenzhong, F. Wenzheng, C. Pengxin, N. Mingyan, and X. Haodong, ''A tight coupling mapping method to integrate the ESKF, g2o, and point cloud alignment'', The Journal of Supercomputing, vol. 78, no. 2, pp. 1903-1922, 2022. [DOI:10.1007/s11227-021-03900-7]

23. [23] H. Liu et al., ''Influences of different surveying and mapping methods on fractal characteristics of gully-head shoulder lines'', Physical Geography, vol. 37, no. 6, pp. 387-408, 2016. [DOI:10.1080/02723646.2016.1228131]

24. [24] Y. Filippovska, V. Walter, and D. Fritsch, ''Quality evaluation of generalization algorithms'', ISPRS Commission II, WG II, vol. 7, 2008.

25. [25] M. Garland and Y. Zhou, ''Quadric-based simplification in any dimension'', ACM Transactions on Graphics (TOG), vol. 24, no. 2, pp. 209-239, 2005. [DOI:10.1145/1061347.1061350]

26. [26] Y. Zhang, H. Liu, D. Chen, and P. Xu, ''Stylized line rendering for three-dimensional models'', in 2018 International Conference on Information and Computer Technologies (ICICT), 2018, pp. 52-60. [DOI:10.1109/INFOCT.2018.8356840]

27. [27] W. Shi and C. Cheung, ''Performance evaluation of line simplification algorithms for vector generalization'', The Cartographic Journal, vol. 43, no. 1, pp. 27-44, 2006. [DOI:10.1179/000870406X93490]

28. [28] A. Lambora, K. Gupta, and K. Chopra, ''Genetic algorithm-A literature review'', in 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon), 2019, pp. 380-384. [DOI:10.1109/COMITCon.2019.8862255]