Correction, update, and enhancement of vectorial forestry road maps using ALS data, a pathfinder, and seven metrics

Accurate information about forestry roads is a key aspect of forest management in terms of economy (e.g. accessibility, cost, optimal path) and ecology (e.g. wildfire and wildlife protection). In Canada, and in fact, globally, most provincial, state or territory governments maintain vectorial information on the forestry roads under their jurisdiction. However, official maps are not always accurate, may lack road attributes of interest and are not always up-to-date. Airborne Laser Scanning (ALS) has become an established technology to accurately characterize and map broad territories by providing high density 3D point-clouds with, at least, 3 or 4 measurements per square meter. This paper addresses the problem of the automatic updating, fixing, and enhancement of vectorial forestry road maps over large landscapes (¿10000 km2). For this purpose, we developed a production ready, documented and open-source software. From metrics derived from the point-cloud the method produces a raster of road probability. It then uses an existing, inaccurate, map of the road network to define approximate start and end points for each road. Then, a pathfinder retrieves the accurate road shape by computing the least cost path between the two points on the probability raster. Using the accurate road position given by the algorithm, road width and road state are then estimated based the on characteristics of the point-cloud. We demonstrate that our algorithm retrieves the centrelines of roads in a natively vectorial form with an error below 3 m in 95% of the roads using a fully automatic method. The accuracy of the road location allows us to derive other accurate measurements, including the state of the roads. •We accurately relocated roads from governmental database using ALS point-clouds.•We measured the width and estimated the state of each road segment.•The method is natively vectorial and uses pathfinding instead of pixel classification.•The method is open-source, fully packaged, documented, and production-ready.