{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:20:42Z","timestamp":1781533242846,"version":"3.54.5"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T00:00:00Z","timestamp":1775865600000},"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":["42471486"],"award-info":[{"award-number":["42471486"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071455"],"award-info":[{"award-number":["42071455"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["42471486"],"award-info":[{"award-number":["42471486"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["42071455"],"award-info":[{"award-number":["42071455"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["CCNU25JC043"],"award-info":[{"award-number":["CCNU25JC043"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["CCNU25KYZHSY22"],"award-info":[{"award-number":["CCNU25KYZHSY22"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>(1) Background: Curve data compression plays a critical role in efficient storage, transmission, and multi-scale visualization of vector spatial data, especially for complex geographic boundaries. Achieving high compression efficiency while preserving geometric fidelity remains a challenging task. (2) Methods: This study proposes a vector curve compression framework based on a convolutional autoencoder. Curve data are segmented and resampled to unify network input, after which coordinate-difference sequences are encoded into low-dimensional latent vectors through convolutional layers and reconstructed via a symmetric decoder. (3) Results: Experiments conducted on a global island boundary dataset demonstrate that the proposed method achieves effective data reduction with stable reconstruction accuracy. Specifically, compared with the classical Douglas\u2013Peucker (DP) algorithm, Fourier series (FS) methods, and fully connected autoencoders (FCAs), the 1D CAE exhibits superior and more robust reconstruction performance, especially under high compression ratios. It achieves the lowest positional deviation (PD = 42.41) and the highest spatial fidelity (IoU = 0.9991, with a relative area error of only 0.0067%), while maintaining high computational efficiency (57.32 s). Sensitivity analyses reveal that a convolution kernel size of 1 \u00d7 7 and a segment length of 25 km yield the optimal trade-off between representational capacity and model stability. (4) Conclusions: The proposed method enables efficient vector curve compression and reliable coastline reconstruction, and is particularly suitable for small- and medium-scale cartographic applications up to a map scale of 1:250 K.<\/jats:p>","DOI":"10.3390\/ijgi15040164","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T07:29:29Z","timestamp":1776065369000},"page":"164","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Convolutional Autoencoder-Based Method for Vector Curve Data Compression"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5092-6530","authenticated-orcid":false,"given":"Shuo","family":"Zhang","sequence":"first","affiliation":[{"name":"Hubei Province Key Laboratory for Geographical Process Analysis & Simulation, Central China Normal University, Wuhan 430079, China"},{"name":"College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0874-1538","authenticated-orcid":false,"given":"Pengcheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Hubei Province Key Laboratory for Geographical Process Analysis & Simulation, Central China Normal University, Wuhan 430079, China"},{"name":"College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4717-4050","authenticated-orcid":false,"given":"Hongran","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingwu","family":"Guo","sequence":"additional","affiliation":[{"name":"Geomatics Institute, Wuhan 430022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112","DOI":"10.3138\/FM57-6770-U75U-7727","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":"Cartographica"},{"key":"ref_2","first-page":"18","article-title":"Study of realization method and improvement of Douglas\u2013Peucker algorithm of vector data compressing","volume":"7","author":"Yang","year":"2002","journal-title":"Bull. Surv. Mapp."},{"key":"ref_3","first-page":"123","article-title":"Discussion on the progressive improved algorithm for cartographic generalization of line features","volume":"40","author":"Li","year":"2015","journal-title":"Sci. Surv. Mapp."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yu, J., Chen, G., and Zhang, X. (2013). An improved Douglas\u2013Peucker algorithm aimed at simplifying natural shoreline into direction-line. Proceedings of the 21st International Conference on Geoinformatics, IEEE.","DOI":"10.1109\/Geoinformatics.2013.6626177"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/02693799208901921","article-title":"Algorithms for automated line generalization based on a natural principle of objective generalization","volume":"6","author":"Li","year":"1992","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_7","first-page":"221","article-title":"Multi-scale representation model for contour based on Fourier series","volume":"38","author":"Liu","year":"2013","journal-title":"Geom. Inf. Sci. Wuhan Univ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1080\/15230406.2015.1088799","article-title":"Fourier-based multi-scale representation and progressive transmission of cartographic curves on the Internet","volume":"43","author":"Liu","year":"2015","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_9","first-page":"921","article-title":"A head\u2013tail information break method oriented to multi-scale representation of polyline","volume":"49","author":"Liu","year":"2020","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.1080\/13658816.2020.1753203","article-title":"A multi-scale representation model of polyline based on head\/tail breaks","volume":"34","author":"Liu","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_11","first-page":"170","article-title":"Multi-scale representation and automatic generalization of relief based on wavelet analysis","volume":"26","author":"Wu","year":"2001","journal-title":"Geom. Inf. Sci. Wuhan Univ."},{"key":"ref_12","first-page":"488","article-title":"Scaleless representations for polyline spatial data based on wavelet analysis","volume":"29","author":"Wu","year":"2004","journal-title":"Geom. Inf. Sci. Wuhan Univ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/15230406.2021.2013944","article-title":"Polyline simplification based on the artificial neural network with constraints of generalization knowledge","volume":"49","author":"Du","year":"2022","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_14","first-page":"2209","article-title":"Automatic vector polyline simplification based on region proposal network","volume":"52","author":"Jiang","year":"2023","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Courtial, A., El 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_16","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_17","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_18","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1080\/15230406.2023.2218106","article-title":"A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints","volume":"51","author":"Yan","year":"2024","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_19","first-page":"1634","article-title":"Autoencoder neural network method for curve data compression","volume":"53","author":"Liu","year":"2024","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2905","DOI":"10.1080\/10095020.2025.2480815","article-title":"Deep learning in automatic map generalization: Achievements and challenges","volume":"28","author":"Yan","year":"2025","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cui, L., Niu, X., Qian, H., Wang, X., and Xu, J. (2025). A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data. Appl. Sci., 15.","DOI":"10.3390\/app15052383"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhu, Y., Zheng, N., Liu, W., Zhang, H., Zhao, X., and Liu, Y. (2024). Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network. Remote Sens., 16.","DOI":"10.3390\/rs16101736"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2294900","DOI":"10.1080\/10106049.2023.2294900","article-title":"Graph neural network-based identification of ditch matching patterns across multi-scale geospatial data","volume":"38","author":"Huang","year":"2023","journal-title":"Geocarto Int."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/72.554195","article-title":"Face recognition: A convolutional neural network approach","volume":"8","author":"Lawrence","year":"1997","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_26","unstructured":"Huawei Technologies Co., Ltd. (2025, March 10). Petal Maps. Available online: https:\/\/petalmaps.dre.agconnect.link\/."},{"key":"ref_27","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","unstructured":"National Oceanic and Atmospheric Administration (2009). Cartographic Generalization and Symbolization, NOAA Technical Report NOS 127 CGS 12."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/4\/164\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T08:22:19Z","timestamp":1776068539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/4\/164"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,11]]},"references-count":28,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["ijgi15040164"],"URL":"https:\/\/doi.org\/10.3390\/ijgi15040164","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,11]]}}}