Polyline simplification using a region proposal network integrating raster and vector features
ABSTRACTPolyline 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...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2023.2275427 |
| _version_ | 1797645315012034560 |
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| author | Baode Jiang Shaofen Xu Zhiwei Li |
| author_facet | Baode Jiang Shaofen Xu Zhiwei Li |
| author_sort | Baode Jiang |
| collection | DOAJ |
| description | ABSTRACTPolyline 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. |
| first_indexed | 2024-03-11T14:44:35Z |
| format | Article |
| id | doaj.art-a4997600bb0649189afb983058607280 |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T14:44:35Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-a4997600bb0649189afb9830586072802023-10-30T12:29:01ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262023-12-0160110.1080/15481603.2023.2275427Polyline simplification using a region proposal network integrating raster and vector featuresBaode Jiang0Shaofen Xu1Zhiwei Li2School of Computer Science, China University of Geosciences, Wuhan, ChinaNational Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaABSTRACTPolyline 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.https://www.tandfonline.com/doi/10.1080/15481603.2023.2275427Map generalizationpolyline simplificationdeep learningregion proposal network |
| spellingShingle | Baode Jiang Shaofen Xu Zhiwei Li Polyline simplification using a region proposal network integrating raster and vector features GIScience & Remote Sensing Map generalization polyline simplification deep learning region proposal network |
| title | Polyline simplification using a region proposal network integrating raster and vector features |
| title_full | Polyline simplification using a region proposal network integrating raster and vector features |
| title_fullStr | Polyline simplification using a region proposal network integrating raster and vector features |
| title_full_unstemmed | Polyline simplification using a region proposal network integrating raster and vector features |
| title_short | Polyline simplification using a region proposal network integrating raster and vector features |
| title_sort | polyline simplification using a region proposal network integrating raster and vector features |
| topic | Map generalization polyline simplification deep learning region proposal network |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2023.2275427 |
| work_keys_str_mv | AT baodejiang polylinesimplificationusingaregionproposalnetworkintegratingrasterandvectorfeatures AT shaofenxu polylinesimplificationusingaregionproposalnetworkintegratingrasterandvectorfeatures AT zhiweili polylinesimplificationusingaregionproposalnetworkintegratingrasterandvectorfeatures |