A Convolutional Autoencoder-Based Method for Vector Curve Data Compression

Bibliographic Data

ID22034283
AuthorsShuo Zhang (0000-0002-7812-4260, Central China Normal University), Pengcheng Liu (0000-0002-0874-1538, Central China Normal University, corresponding author), Hongran Ma (0009-0006-4717-4050, Wuhan University), Mingwu Guo (Institute of Geodesy and Geophysics)
Year2026
Volume15
Issue4
Pages164
Publication date2026-04-11
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi15040164
OpenAlexW7153992231
LanguageEN
References cited16

(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–Peucker (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 × 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

Compression ratio · Data compression · Spatial analysis · Advanced Data Compression Techniques · Computer Graphics and Visualization Techniques · Data Management and Algorithms

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