Mapping Trails and Tracks in the Boreal Forest Using LiDAR and Convolutional Neural Networks
Bibliographic record
Abstract
Trails and tracks are the detectable signs of passage of wildlife and off-highway vehicles in natural landscapes. They record valuable information on the presence and movement of animals and humans. However, published works aimed at mapping trails and tracks with remote sensing are nearly absent from the peer-reviewed literature. Here, we demonstrate the capacity of high-density LiDAR (light detection and ranging) and convolutional neural networks to map undifferentiated trails and tracks automatically across a diverse study area in the Canadian boreal forest. We compared maps developed with LiDAR from a drone platform (10 cm digital terrain model) with those from a piloted-aircraft platform (50 cm digital terrain model). We found no significant difference in the accuracy of the two maps. In fact, the piloted-aircraft map (F1 score of 77 ± 9%) performed nominally better than the drone map (F1 score of 74 ± 6%) and demonstrated a better balance among error types. Our maps reveal a 2829 km network of trails and tracks across the 59 km2 study area. These features are especially abundant in peatlands, where the density of detected trails and tracks was 68 km/km2. We found a particular tendency for wildlife and off-highway vehicles to adopt linear industrial disturbances like seismic lines into their movement networks. While linear disturbances covered just 7% of our study area, they contained 27% of all detected trails and tracks. This type of funnelling effect alters the movement patterns of humans and wildlife across the landscape and impedes the recovery of disturbed areas. While our work is a case study, the methods developed have broader applicability, showcasing the potential to map trails and tracks across large areas using remote sensing and convolutional neural networks. This capability can benefit diverse research and management communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".