From Footsteps to Maps : A Workflow to Create Hiking Maps for Remote Areas
Bibliographic record
Abstract
Hiking is an increasingly popular outdoor activity that significantly contributes to the growth of tourism in mountainous and remote regions. As this trend continues, the demand for reliable, user-friendly maps for safe and environmentally conscious exploration also grows. However, many remote hiking areas lack maps due to limited availability and quality of official spatial data, and because there is often no personnel with the necessary GIS expertise or problem-specific guidance available for creating such maps. This thesis presents a detailed workflow for creating printed hiking maps for areas where conventional data sources lack information or detail, based on GPS tracks and open-source data using ArcGIS Pro. The workflow addresses data integration, cleaning, combining local knowledge with existing contextual data, terrain visualization, and map layout preparation—focusing on the trails surrounding the remote hiking lodge Nuk Tessli in British Columbia, Canada. The result of the workflow will be a large-scale hiking map designed for offline navigation, enhanced safety and to contribute to environmental protection. By demonstrating how easily collectible GPS data in combination with basic GIS techniques and local insights can be used to create accurate and informative hiking maps, this work contributes to improving the accessibility of GIS tools for a broader group of users, and through that ultimately the availability of maps for remote areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".