Generation and optimisation of colour-shaded relief maps using neural networks

Abstract

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.

Keywords:

• Shaded relief
• colour-shaded relief maps
• deep learning
• neural network
• cartography

Acknowledgements

The authors would like to thank the editors and anonymous reviewers for their valuable comments and suggestions.

Author contributions

Conceptualisation, Chenglin Bian and Shaomei Li; methodology, Chenglin Bian; software, Chenglin Bian and Guangzhi Yin; validation, Jingzhen Ma, Bowei Wen and Linghui Kong; formal analysis, Jingzhen Ma; investigation, Bowei Wen; resources, Shaomei Li; data curation, Linghui Kong; writing—original draft preparation, Chenglin Bian; writing—review and editing, Chenglin Bian, Shaomei Li, Jingzhen Ma and Guangzhi Yin; visualisation, Jingzhen Ma; supervision, Shaomei Li; project administration, Shaomei Li; funding acquisition, Shaomei Li, Jingzhen Ma and Bowei Wen. All authors have read and agreed to the published version of the manuscript.

Data availability statement

The shaded relief images used for the study are copyright by Swisstopo and available at https://www.swisstopo.admin.ch by purchase. The data generated during the study can be obtained from the corresponding author.

Disclosure statement

The authors report there are no competing interests to declare.

Additional information

Funding

This research was funded by the National Natural Science Foundation of China under Grant [numbers 42101454, 42101455].