Automatic vector polyline simplification based on region proposal network

doi:10.11947/j.AGCS.2023.20220363

Abstract

Abstract: To address the problem of existing vector polyline simplification algorithms lacking intelligence, an automatic vector polyline simplification algorithm based on regional proposal network is proposed. Firstly, a depth-separable convolutional neural network is used to realize the convolutional feature extraction of raster polylines. Then, the generation method of region proposals in the region proposal network is improved by combining the coordinate information of the polylines, which is in order to establish the correspondence between the proposal region and the possible bending combinations. Finally, after unifying the size of the bending feature map, the automatic detection of bending units is completed by binary classification according to the convolutional features corresponding to the region proposals, and polyline simplification is achieved by deleting the bending units. In this paper, the model is trained and tested on the coastline dataset, and the effectiveness of the model is verified by comparison experiments with different rasterization parameters, different backbone networks, cross-scale simplification, and different types of line simplification. Experimental results show that this algorithm can intelligently learn the simplification knowledge from the existing polyline simplification cases, and make full use of the vector and raster features of polylines to identify and locate the bending units, and finally complete the automatic simplification of vector polylines while the network can be trained end-to-end.

Key words: line simplification, region proposal network, map generalization, deep learning

CLC Number:

P283

JIANG Baode, XU Shaofen, WU Yong, WANG Miao. Automatic vector polyline simplification based on region proposal network[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(12): 2209-2222.

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