Generation and optimisation of colour-shaded relief maps using neural networks
Shaded relief is a primary tool used to effectively portray three-dimensional terrain on a two-dimensional plane surface. Colour-shaded relief maps use colour variations to effectively represent elevation changes and even capture the natural hues of surface landscapes. This study evaluates and propo...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-01-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2024.2322085 |
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| author | Chenglin Bian Shaomei Li Jingzhen Ma Guangzhi Yin Bowei Wen Linghui Kong |
| author_facet | Chenglin Bian Shaomei Li Jingzhen Ma Guangzhi Yin Bowei Wen Linghui Kong |
| author_sort | Chenglin Bian |
| collection | DOAJ |
| description | Shaded relief is a primary tool used to effectively portray three-dimensional terrain on a two-dimensional plane surface. Colour-shaded relief maps use colour variations to effectively represent elevation changes and even capture the natural hues of surface landscapes. This study evaluates and proposes methods for creating colour-shaded relief maps using neural networks. Four distinct neural network shading models were trained using a dataset composed of slices from ‘digital elevation model (DEM)–manual colour-shaded relief maps’. The aim was to generate colour-shaded relief maps based on DEM data specific to the mapped area. The experimental results suggest that all four types of network-based shaded relief maps models effectively depict the primary terrain features within the mapped area. The CGAN (UNet generator) model yields the most optimal results, showcasing the superior cartographic generalisation of relief and delineation of terrain structures compared with the other models. Specialised training was conducted for the CGAN (UNet generator) shaded relief model to improve the clarity and authenticity of colour-shaded relief maps. |
| format | Article |
| id | doaj-art-8f7b3aade3cd4a159a96212af6be1128 |
| institution | OA Journals |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-8f7b3aade3cd4a159a96212af6be11282025-08-20T01:59:21ZengTaylor & Francis GroupGeocarto International1010-60491752-07622024-01-0139110.1080/10106049.2024.2322085Generation and optimisation of colour-shaded relief maps using neural networksChenglin Bian0Shaomei Li1Jingzhen Ma2Guangzhi Yin3Bowei Wen4Linghui Kong5Information Engineering University, Zhengzhou, Henan, ChinaInformation Engineering University, Zhengzhou, Henan, ChinaInformation Engineering University, Zhengzhou, Henan, ChinaInformation Engineering University, Zhengzhou, Henan, ChinaInformation Engineering University, Zhengzhou, Henan, ChinaInformation Engineering University, Zhengzhou, Henan, ChinaShaded relief is a primary tool used to effectively portray three-dimensional terrain on a two-dimensional plane surface. Colour-shaded relief maps use colour variations to effectively represent elevation changes and even capture the natural hues of surface landscapes. This study evaluates and proposes methods for creating colour-shaded relief maps using neural networks. Four distinct neural network shading models were trained using a dataset composed of slices from ‘digital elevation model (DEM)–manual colour-shaded relief maps’. The aim was to generate colour-shaded relief maps based on DEM data specific to the mapped area. The experimental results suggest that all four types of network-based shaded relief maps models effectively depict the primary terrain features within the mapped area. The CGAN (UNet generator) model yields the most optimal results, showcasing the superior cartographic generalisation of relief and delineation of terrain structures compared with the other models. Specialised training was conducted for the CGAN (UNet generator) shaded relief model to improve the clarity and authenticity of colour-shaded relief maps.https://www.tandfonline.com/doi/10.1080/10106049.2024.2322085Shaded reliefcolour-shaded relief mapsdeep learningneural networkcartography |
| spellingShingle | Chenglin Bian Shaomei Li Jingzhen Ma Guangzhi Yin Bowei Wen Linghui Kong Generation and optimisation of colour-shaded relief maps using neural networks Geocarto International Shaded relief colour-shaded relief maps deep learning neural network cartography |
| title | Generation and optimisation of colour-shaded relief maps using neural networks |
| title_full | Generation and optimisation of colour-shaded relief maps using neural networks |
| title_fullStr | Generation and optimisation of colour-shaded relief maps using neural networks |
| title_full_unstemmed | Generation and optimisation of colour-shaded relief maps using neural networks |
| title_short | Generation and optimisation of colour-shaded relief maps using neural networks |
| title_sort | generation and optimisation of colour shaded relief maps using neural networks |
| topic | Shaded relief colour-shaded relief maps deep learning neural network cartography |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2024.2322085 |
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