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

Abstract

AbstractShaded 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