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.

就经济（例如可达性、成本、最佳路径）和生态（例如野火和野生动物保护）而言，有关林业道路的准确信息是森林管理的一个关键方面。在加拿大，事实上，在全球范围内，大多数省、州或地区政府都在其管辖范围内保存有关林业道路的矢量信息。然而，官方地图并不总是准确的，可能缺乏感兴趣的道路属性，并且并不总是最新的。机载激光扫描 (ALS) 已成为一项成熟的技术，通过提供高密度 3D 点云，每平方米至少进行 3 或 4 次测量，从而准确地表征和绘制广阔的区域。

本文解决了矢量林业路线图在大型景观（¿10000 km ²）。为此，我们开发了一个生产就绪、文档化和开源软件。根据从点云派生的指标，该方法生成道路概率栅格。然后，它使用现有的、不准确的道路网络地图来定义每条道路的大致起点和终点。然后，探路者通过计算概率栅格上两点之间的最小成本路径来检索准确的道路形状。使用算法给出的准确道路位置，然后根据点云的特征估计道路宽度和道路状态。我们证明我们的算法使用全自动方法以原生矢量形式检索道路中心线，在 95% 的道路中误差低于 3 m。道路位置的准确性使我们能够得出其他准确的测量值，