Cartographic Relief Shading with Neural Networks

TL;DR

This paper demonstrates that neural networks, specifically U-Net models, can replicate hand-drawn relief shading on maps, producing high-quality terrain visualizations quickly and effectively.

Contribution

The authors introduce a neural network approach to generate relief shading that closely mimics manual cartographic shading, capturing essential artistic principles.

Findings

Neural network shading closely resembles hand-drawn relief art

Generated shading is of high quality according to expert evaluations

Shading can be produced in seconds from digital elevation models

Abstract

Shaded relief is an effective method for visualising terrain on topographic maps, especially when the direction of illumination is adapted locally to emphasise individual terrain features. However, digital shading algorithms are unable to fully match the expressiveness of hand-crafted masterpieces, which are created through a laborious process by highly specialised cartographers. We replicate hand-drawn relief shading using U-Net neural networks. The deep neural networks are trained with manual shaded relief images of the Swiss topographic map series and terrain models of the same area. The networks generate shaded relief that closely resemble hand-drawn shaded relief art. The networks learn essential design principles from manual relief shading such as removing unnecessary terrain details, locally adjusting the illumination direction to accentuate individual terrain features, and varying brightness to emphasise larger landforms. Neural network shadings are generated from digital elevation models in a few seconds, and a study with 18 relief shading experts found that they are of high quality.