Articlegreen2022

Deriving map images of generalised mountain roads with generative adversarial networks

43 citations0/0 files storedInternational Journal of Geographical Information Systems

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Geographic Information Systems Studies Historical Geography and Cartography Generative Adversarial Networks and Image Synthesis

Abstract

Map generalisation is a process that transforms geographic information for a cartographic at a specific scale. The goal is to produce legible and informative maps even at small scales from a detailed dataset. The potential of deep learning to help in this task is still unknown. This article examines the use case of mountain road generalisation, to explore the potential of a specific deep learning approach: generative adversarial networks (GAN). Our goal is to generate images that depict road maps generalised at the 1:250k scale, from images that depict road maps of the same area using un-generalised 1:25k data. This paper not only shows the potential of deep learning to generate generalised mountain roads, but also analyses how the process of deep learning generalisation works, compares supervised and unsupervised learning and explores possible improvements. With this experiment we have exhibited an unsupervised model that is able to generate generalised maps evaluated as good as the reference and reviewed some possible improvements for deep learning-based generalisation, including training set management and the definition of a new road connectivity loss. All our results are evaluated visually using a four questions process and validated by a user test conducted on 113 individuals.

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References

2012Assuring the quality of volunteered geographic information 2005Optimization approaches for generalization and data abstraction 2010A Road Network Selection Process Based on Data Enrichment and Structure Detection 2019Is deep learning the new agent for map generalization?1995Overcoming the knowledge acquisition bottleneck in map generalization: The role of interactive systems and computational intelligence 2020Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation 2007Evaluation in the Map Generalisation Process 2011A typology of operators for maintaining legible map designs at multiple scales

Cited by

2024Machine learning in cartography 2023Move and remove: Multi-task learning for building simplification in vector maps with a graph convolutional neural network 2023A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints 2023Using machine learning and data enrichment in the selection of roads for small-scale maps 2025Deep learning in automatic map generalization: achievements and challenges

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