Deep Neural Network Architectures for Advanced Hiking Map Generation

Olivier Schirm, Maxime Devanne, Jonathan Weber, Arnaud Lecus, Germain Forestier, Cédric Wemmert

2025

Abstract

The automation of hiking map generation using deep learning represents a pivotal advancement in geospatial analysis. This study investigates the application of neural network architectures to derive accurate hiking maps from GPS trajectory data, exclusively collected via the Visorando mobile application. By exploring the utility of 17 distinct raster features derived from geospatial data, we identify the heatmap as the most effective input for mapping intricate trail networks, achieving superior performance across accuracy, segmentation, and connectivity metrics. Among various architectures evaluated, HRNet emerged as the most efficient model, demonstrating exceptional results when combined with optimal input features, significantly outperforming state-of-the-art approaches in intersection detection and trail segmentation. This research introduces a novel framework for converting vector-based GPS traces into rasterized data suitable for convolutional neural networks, overcoming challenges like noisy inputs and terrain variability. The findings establish a new benchmark for efficiency and accuracy in hiking map generation, offering valuable insights for the broader field of automated map inference reliant on GPS data. Future work will explore direct spatio-temporal processing of vector data, eliminating raster conversion for enhanced scalability and precision.

Paper Citation

in Harvard Style

Schirm O., Devanne M., Weber J., Lecus A., Forestier G. and Wemmert C. (2025). Deep Neural Network Architectures for Advanced Hiking Map Generation. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 531-541. DOI: 10.5220/0013351700003905

in Bibtex Style

@conference{icpram25,
author={Olivier Schirm and Maxime Devanne and Jonathan Weber and Arnaud Lecus and Germain Forestier and Cédric Wemmert},
title={Deep Neural Network Architectures for Advanced Hiking Map Generation},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={531-541},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013351700003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Deep Neural Network Architectures for Advanced Hiking Map Generation
SN - 978-989-758-730-6
AU - Schirm O.
AU - Devanne M.
AU - Weber J.
AU - Lecus A.
AU - Forestier G.
AU - Wemmert C.
PY - 2025
SP - 531
EP - 541
DO - 10.5220/0013351700003905
PB - SciTePress