Vectorial and topologically valid segmentation of forestry road networks from ALS data

• Published in: International journal of applied earth observation and geoinformation Vol. 118; p. 103267
• Main Authors: Roussel, Jean-Romain, Bourdon, Jean-François, Morley, Ilythia D., Coops, Nicholas C., Achim, Alexis
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-04-2023; Elsevier
• Subjects: Airborne lidar; Forest road networks; Least cost path; Road extraction; Vectorial layer; Road extraction; Least cost path; Vectorial layer; Forest road networks; Airborne lidar
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2023.103267

Accurate information on road location is critical for forest management and conservation strategies. Road location data supports the analysis of road accessibility and usability and is a critical information layer for forest harvest, financial planning, wildfire suppression, and protection activities. The global expanse of forests, their remoteness, and difficulty to access have necessitated the development of automatic or semi-automatic remote sensing methodologies to map roads using passive optical imagery or Airborne Laser Scanning (ALS). Conventional automatic road mapping methods are raster-based and map roads as patches of disconnected pixels. This paper addresses the limitations of raster-based automatic forest road extraction and presents a method for producing a topologically accurate vectorial road network. Our method, presented as a fully documented and open-source software tool, uses metrics derived from an ALS point cloud to produce a raster of road conductivity. From this conductivity raster, the method “drives” the roads iteratively by detecting and following road intersections. We demonstrate the method’s efficacy using a road network in Quebec, Canada, where 96% of the roads in a binary raster, and 84% using our probability map, are vectorized properly from an ALS point cloud with 4% false positives. Our proposed method may significantly reduce the training requirements of machine learning techniques used to classify roads by being very robust to false positive and false negative classifications.
•We designed a novel algorithm for mapping forestry roads based on pathfinding.•The output is natively vectorial and topologically valid.•The vectorization step can be applied to maps classified by machine learning.•The method has the potential to reduce requirements for the training of classifiers.