Abstract
Among cartographic generalisation problems, the generalisation of sinuous bends in mountain roads has always been a popular one due to its difficulty. Recent research showed the potential of deep learning techniques to overcome some remaining research problems regarding the automation of cartographic generalisation. This paper explores this potential on the popular mountain road generalisation problem, which requires smoothing the road, enlarging the bend summits, and schematising the bend series by removing some of the bends. We modelled the mountain road generalisation as a deep learning problem by generating an image from input vector road data, and tried to generate it as an output of the model a new image of the generalised roads. Similarly to previous studies on building generalisation, we used a U-Net architecture to generate the generalised image from the ungeneralised image. The deep learning model was trained and evaluated on a dataset composed of roads in the Alps extracted from IGN (the French national mapping agency) maps at 1:250,000 (output) and 1:25,000 (input) scale. The results are encouraging as the output image looks like a generalised version of the roads and the accuracy of pixel segmentation is around 65%. The model learns how to smooth the output roads, and that it needs to displace and enlarge symbols but does not always correctly achieve these operations. This article shows the ability of deep learning to understand and manage the geographic information for generalisation, but also highlights challenges to come.
Highlights
Map generalisation seeks to adapt a precise geographic data-set for visualisation at a smaller scale
The code was implemented in the Google Colaboratory platform with the available graphic processing unit (GPU) for standard licences
We present some results obtained with the U-Net segmentation of mountain road images
Summary
Map generalisation seeks to adapt a precise geographic data-set for visualisation at a smaller scale. This task is still highly challenging to automate and human intervention is required. Researchers tried for decades to automate map generalisation by designing various geometrical operators [1] and complicated processes to orchestrate individual operators in an efficient way [2]. Machine learning was an early solution to overcome traditional methods and capture the knowledge of human cartographers on how to orchestrate geometrical transformations [4,5,6,7]. The first attempts to generalise buildings with a U-Net (a convolutional neural network for image segmentation) [9] showed very promising results [10]
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