image: Twenty feature points were selected on the constructed 2D road network map (Ahmed’s method, Figure 11a) and 3D road network map (paper method, Figure 11b) and used for quantitative evaluation of positional accuracy. Ahmed’s method yielded an average horizontal deviation of 11.318 meters. The 3D road network significantly reduced this horizontal deviation to 4.201 meters and achieved a vertical error of 7.656 meters. The figure shows that the paper method is superior to the Ahmed’s method in the accuracy of horizontal spatial positioning and has acceptable elevation precision. Note that the background map in the figure is sourced from the standard Tianditu Map.
Credit: Beijing Zhongke Journal Publising Co. Ltd.
A novel method for creating high precise and enriched three-dimensional (3D) outdoor hiking road network maps, utilizing crowdsourced trajectory data shared by hikers, can improve outdoor safety and navigation, according to a new study published in the Journal of Geo-information Science on February 7th.Titled "Outdoor Hiking Navigation Road Network Map Construction Using Crowd-Source Trajectory Data", this research addresses gaps in current mapping systems that prioritize urban vehicular routes over outdoor hiking trails and lack elevation details essential for reliable outdoor navigation.
Researchers from Central South University, Changsha, China, developed a two-layer system to generate such 3D road maps with enriched elevation, slope, and aspect information by using crowdsourced trajectories. The first layer, the “road network generation layer”, extracts the two-dimensional (2D) geometric and topological structure of outdoor road network using a trajectory density stratification strategy. The second layer, the “elevation extraction layer”, estimates and optimizes elevation information, generating a high-resolution elevation grid that matches the 2D road network to create a 3D road network map.
To validate their method, the team tested it with 1,170 outdoor trajectories collected in 2021 from the Yuelu Mountain Scenic Area in Changsha, China, though the Six Feet Outdoors website (foooooot.com). The generated road maps demonstrated an average deviation of 4.201 meters in 2D spatial positioning and an elevation error of 7.656 meters, proving the effectiveness of the paper method.
"This advancement in outdoor 3D road mapping significantly benefits from the increasing availability of crowdsourced trajectory data", said Dr. Tang, lead author of the study. "By integrating elevation data, we can create much more detailed and informative outdoor hiking road maps, which are crucial for safe and effective route planning in complex outdoor environments, which holds significant implications not only for recreational hikers but also for search and rescue operations".
This study expands the possibility of crowdsourced 3D mapping and enhances the accuracy of outdoor navigation maps by integrating elevation information. Unlike traditional 2D maps, the new 3D hiking maps generated offer richer and more precise information, which improves route planning, navigation, and potential contributes to increased safety for hikers and outdoor enthusiasts.
For more details, please refer to the original article:
Outdoor hiking navigation road network map construction using crowd-source trajectory data
https://www.sciengine.com/JGIS/doi/10.12082/dqxxkx.2025.240479 （If you want to see the English version of the full text, please click on the “iFLYTEK Translation” in the article page.）
Article Title
Outdoor Hiking Navigation Road Network Map Construction Based on Multi-source Trajectory Data
Article Publication Date
24-Jan-2025