{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:48:55Z","timestamp":1760057335396,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T00:00:00Z","timestamp":1738800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42371454"],"award-info":[{"award-number":["42371454"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Polyline simplification is a critical process in cartographic generalization, but the existing methods often fall short in considering the overall geographic morphology or local edge and vertex information of polylines. To enhance the graph convolutional structure for capturing crucial geographic element features and simultaneously learning vertex and edge features within map polylines, this study introduces a joint vertex\u2013edge feature graph convolutional network (VE-GCN). The VE-GCN extends the graph convolutional operator from vertex features to edge features and integrates edge and vertex features through a feature transformation layer, enhancing the model\u2019s capability to represent the shapes of polylines. To further improve this capability, the VE-GCN incorporates an architecture for retaining crucial geographic information. This architecture is composed of a structure for retaining local positional information and another for extracting multi-scale features. These components capture high\u2013low dimensional and large\u2013small scale features, contributing to polylines\u2019 comprehensive local and global representation. The experimental results on road and coastline datasets verified the effectiveness of the proposed network in maintaining the overall shape characteristics of simplified polylines. After fusing the edge features, the differential distance between the roads before and after simplification decreased from 1.06 to 0.18. The network ensures invariant global spatial relationships, making the simplified data well suited for cartographic generalization applications, especially in simplifying vector map elements.<\/jats:p>","DOI":"10.3390\/ijgi14020064","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T08:53:41Z","timestamp":1738832021000},"page":"64","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VE-GCN: A Geography-Aware Approach for Polyline Simplification in Cartographic Generalization"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8219-0087","authenticated-orcid":false,"given":"Siqiong","family":"Chen","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"State Key Laboratory of Geo-Information Engineering, Xi\u2019an 710054, China"}]},{"given":"Anna","family":"Hu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7421-4915","authenticated-orcid":false,"given":"Yongyang","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geo-Information Engineering, Xi\u2019an 710054, China"},{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1870-5570","authenticated-orcid":false,"given":"Haitao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zhong","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"key":"ref_1","first-page":"426","article-title":"A generalization of geographic conditions maps constrained by both spatial and semantic scales","volume":"50","author":"Yin","year":"2021","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_2","unstructured":"Lan, Q.P. (2010). Research on Multi-Scale Concatenated Update Methods for Map Data. [Ph.D. thesis, Wuhan University]."},{"key":"ref_3","unstructured":"Shen, Y.L. (2019). Simplified Representation of Map Elements from Computer Vision Perspective. [Ph.D. thesis, Wuhan University]."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4158","DOI":"10.1080\/10106049.2021.1878288","article-title":"Segmentation and sampling method for complex polyline generalization based on a generative adversarial network","volume":"37","author":"Du","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_5","first-page":"1645","article-title":"Overview of the Research Progress in Automated Map Generalization","volume":"46","author":"Wu","year":"2017","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_6","first-page":"525","article-title":"Improvements of linear features simplification algorithm based on vertexes clustering","volume":"30","author":"Li","year":"2013","journal-title":"J. Geomat. Sci. Technol."},{"key":"ref_7","first-page":"112","article-title":"Algorithms for the reduction of the number of points required to represent a digitized line or its caricature","volume":"10","author":"Douglas","year":"1973","journal-title":"Cartogr. Int. J. Geogr. Inf. Geovis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1111\/cgf.13993","article-title":"LOCALIS: Locally-adaptive Line Simplification for GPU-based Geographic Vector Data Visualization","volume":"39","author":"Amiraghdam","year":"2020","journal-title":"Comput. Graph. Forum"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, B., Liu, X.C., Li, D.J., Shi, Y.T., Fernandez, G., and Wang, Y.D. (2020). A Vector Line Simplification Algorithm Based on the Douglas-Peucker Algorithm, Monotonic Chains and Dichotomy. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9040251"},{"key":"ref_10","first-page":"349","article-title":"Using Genetic Algorithms for Solving Problems in Automated Line Simplification","volume":"32","author":"Wu","year":"2003","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_11","unstructured":"Jiang, B., and Nakos, B. (2003, January 3\u20135). Line Simplification Using Self-Organizing Maps. Proceedings of the ISPRS Workshop on Spatial Analysis and Decision Making, Hong Kong, China."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H.T., and Zhu, Z.X. (2018, January 13\u201319). Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.isprsjprs.2019.02.010","article-title":"A graph convolutional neural network for classification of building patterns using spatial vector data","volume":"150","author":"Yan","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","first-page":"635","article-title":"The Simplification Model of Linear Objects Based on Ant Colony Optimization Algorithm","volume":"40","author":"Zheng","year":"2011","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_15","first-page":"744","article-title":"A Line Simplification Method Based on Support Vector Machine","volume":"45","author":"Duan","year":"2020","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1080\/15481603.2013.823748","article-title":"Building simplification using backpropagation neural networks: A combination of cartographers\u2019 expertise and raster-based local perception","volume":"50","author":"Cheng","year":"2013","journal-title":"GISci. Remote Sens."},{"key":"ref_17","first-page":"118","article-title":"Line Generalization Based on Structure Analysis","volume":"40","author":"Zhang","year":"2001","journal-title":"Acta Sci. Nat. Univ. Sunyatseni"},{"key":"ref_18","first-page":"443","article-title":"Simplifying Line with Oblique Dividing Curve Method","volume":"36","author":"Qian","year":"2007","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1179\/caj.1993.30.1.46","article-title":"Line generalisation by repeated elimination of points","volume":"30","author":"Visvalingam","year":"1993","journal-title":"Cartogr. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/02693799208901921","article-title":"Algorithms for the Automated Line Generalization Based on Natural Principle of Objective Generalization","volume":"6","author":"Li","year":"1992","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3138\/W157-5W4P-63V7-7385","article-title":"Critical Points Detection Using the Length Ratio (LR) for Line Generalization","volume":"40","author":"Nakos","year":"2003","journal-title":"Cartographica"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1109\/34.31447","article-title":"On the Detection of Dominant Points on Digital Curves","volume":"11","author":"Teh","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","first-page":"49","article-title":"Numerical Method for Generalizing the Linear Elements of Large-Scale Maps, Based on the Example of Rivers","volume":"37","author":"Chrobak","year":"2000","journal-title":"Cartogr. Int. J. Geogr. Inf. Geovis."},{"key":"ref_24","first-page":"450","article-title":"Improvement and Assessment of Li-Openshaw Algorithm","volume":"36","author":"Zhu","year":"2007","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_25","first-page":"45","article-title":"An Improved Local Length Ratio Method for Curve Simplification and Its Evaluation","volume":"27","author":"Liu","year":"2011","journal-title":"Geogr. Geo-Inf. Sci."},{"key":"ref_26","first-page":"40","article-title":"An Improved Local Measure Method for the Importance of Vertices in Curve Simplification","volume":"25","author":"Deng","year":"2009","journal-title":"Geogr. Geo-Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shen, Y.L., Ai, T.H., and He, Y.K. (2018). A new approach to line simplification based on image processing: A case study of water area boundaries. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7020041"},{"key":"ref_28","first-page":"3","article-title":"Line Generalization Based on Analysis of Shape Characteristics","volume":"25","author":"Wang","year":"1998","journal-title":"Cartogr. Geogr. Inf. Syst."},{"key":"ref_29","first-page":"778","article-title":"Chart Depth Contour Simplification Based on Delaunay Triangulation","volume":"44","author":"Li","year":"2019","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_30","first-page":"343","article-title":"A Binary Tree Representation of Curve Hierarchical Structure in Depth","volume":"30","author":"Ai","year":"2001","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_31","first-page":"533","article-title":"The Line Feature Simplification Algorithm Preserving Curve Bend Feature","volume":"31","author":"Huang","year":"2014","journal-title":"J. Geomat. Sci. Technol."},{"key":"ref_32","first-page":"1096","article-title":"Line Feature Simplification Method Based on Bend Group Division","volume":"42","author":"Qian","year":"2017","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_33","unstructured":"Ma, L. (2017, January 2\u20137). Features extraction of buildings and generalization using deep learning. Proceedings of the 28th International Cartographic Conference, Washington, DC, USA."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1080\/23729333.2019.1615729","article-title":"Transferring multiscale map styles using generative adversarial networks","volume":"5","author":"Kang","year":"2019","journal-title":"Int. J. Cartogr."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Courtial, A., Ayedi, A., Touya, G., and Zhang, X. (2020). Exploring the potential of deep learning segmentation for mountain roads generalisation. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9050338"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2275427","DOI":"10.1080\/15481603.2023.2275427","article-title":"Polyline simplification using a region proposal network integrating raster and vector features","volume":"60","author":"Jiang","year":"2023","journal-title":"GISci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2302","DOI":"10.1111\/tgis.12965","article-title":"Data-driven polyline simplification using a stacked autoencoder-based deep neural network","volume":"26","author":"Yu","year":"2022","journal-title":"Trans. GIS"},{"key":"ref_38","first-page":"373","article-title":"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","volume":"51","author":"Du","year":"2022","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Guo, X., Liu, J.N., Wu, F., and Qian, H.Z. (2022). A Method for Intelligent Road Network Selection Based on Graph Neural Network. Data, 7.","DOI":"10.3390\/ijgi12080336"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Buffelli, D., and Vandin, F. (2023). The Impact of Global Structural Information in Graph Neural Networks Applications. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/data7010010"},{"key":"ref_41","unstructured":"Defferrard, M., Bresson, X., and Vandergheynst, P. (2016, January 5\u201310). Convolutional neural networks on graphs with fast localized spectral filtering. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1080\/15230406.2013.803707","article-title":"Scale-specific automated line simplification by vertex clustering on a hexagonal tessellation","volume":"40","author":"Raposo","year":"2013","journal-title":"Cartogr. Geogr. Inf. Sci."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/2\/64\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:27:51Z","timestamp":1760027271000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/2\/64"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,6]]},"references-count":42,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["ijgi14020064"],"URL":"https:\/\/doi.org\/10.3390\/ijgi14020064","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2025,2,6]]}}}