Abstract：Polyline simplification is crucial for cartography and spatial database management. In recent decades, various rule-based algorithms for vector polyline simplification have been proposed. However, most existing algorithms lack parameter self-adaptive capabilities and require repeated manual parameter adjustments when applied to different polylines. While deep-learning-based methods have recently been introduced for raster polyline image simplification, they cannot achieve end-to-end simplification when the input data and output results are vector polylines. This paper proposes a new deep-learning-based method for vector polyline simplification by integrating both the vector and raster features of the polyline. Specifically, a deep separable convolutional residual neural network was first used to extract the convolutional features of each image. Then, the region proposal network was modified to generate proposal boxes using vector coordinates, and these proposal boxes were used to locate the convolutional feature maps of bends. Finally, convolutional feature maps were fed into a binary classification network to identify unimportant vertices that should be omitted for vector polyline simplification. The experimental results indicated that the proposed method can exploit raster and vector features to achieve automatic and effective polyline simplification without prior map generalization knowledge and manual settings of rules and parameters. The polyline simplification results of the proposed method have a higher compression ratio of coordinate points and lower shape deformation and deviation than the results generated by the classic Wang and Müller (WM) algorithm and Support Vector Machine (SVM) based algorithm, which shows the potential of the proposed method for future applications in map generalization.
KeyWord：Map generalization; polyline simplification; deep learning; region proposal network;

相关文献：
1.Fine-grained wetland classification for national wetland reserves using multi-source remote sensing data and Pixel Information Expert Engine (PIE-Engine)
2.Histogram matching-based semantic segmentation model for crop classification with Sentinel-2 satellite imagery
3.Assessment of an evapotranspiration algorithm accounting for land cover types and photosynthetic perspectives using remote sensing images
4.An innovative lightweight 1D-CNN model for efficient monitoring of large-scale forest composition: a case study of Heilongjiang Province, China
5.Surface deformation detection and attribution in the Mountain-Oasis-Desert Landscape in north Tianshan Mountains
6.D-FusionNet: road extraction from remote sensing images using dilated convolutional block
7.Leaf area index and aboveground biomass estimation of an alpine peatland with a UAV multi-sensor approach
8.Upscaling peatland mapping with drone-derived imagery: impact of spatial resolution and vegetation characteristics
9.Nearshore bathymetry estimation through dual-time phase satellite imagery in the absence of in-situ data
10.Impacts of the data quality of remote sensing vegetation index on gross primary productivity estimation