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.