The efficient processing of large, often technically complex datasets requires dedicated algorithms and software. Structural descriptions of vegetation provide a means of estimating a range of ecologically pertinent attributes, such as height, volume, and above-ground biomass. ![]() By quantifying the three-dimensional structure of vegetation and underlying terrain using laser technology, ALS has been used extensively for enhancing geospatial knowledge in the fields of forestry and ecology. 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.Īirborne laser scanning (ALS) is a remote sensing technology known for its applicability in natural resources management. 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. From this conductivity raster, the method “drives” the roads iteratively by detecting and following road intersections. 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. ![]() This paper addresses the limitations of raster-based automatic forest road extraction and presents a method for producing a topologically accurate vectorial road network. Conventional automatic road mapping methods are raster-based and map roads as patches of disconnected pixels. ![]() 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). 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. Accurate information on road location is critical for forest management and conservation strategies.
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