Deep Neural Network Architectures for Advanced Hiking Map Generation

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

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 chall