Pathway detection with LiDAR

Mapping of pathways is very difficult. We don't know exactly, where are the hidden ways in the forest, because we don't see through the trees on the aerial photos. :)....but, with LiDAR datasets that kind of mapping is possible.....important information is this, military reasons etc..




PATHWAY DETECTION AND GEOMETRICAL DESCRIPTION FROM ALS DATA IN
FORESTED MOUNTANEOUS AREA
Nicolas David, Cl´ement Mallet, Thomas Pons, Adrien Chauve, Fr´ed´eric Breta


"ABSTRACT:
In the last decade, airborne laser scanning (ALS) systems have become an alternative source for the acquisition of altimeter data. Compared to high resolution orthoimages, one of the main advantages of ALS is the ability of the laser beam to penetrate vegetation and reach the ground underneath. Therefore, 3D point clouds are essential data for computing Digital Terrain Models (DTM) in natural and vegetated areas. DTMs are a key product for many applications such as tree detection, flood modelling, archeology or road detection. Indeed, in forested areas, traditional image-based algorithms for road and pathway detection would partially fail due to their occlusion by the canopy cover. Thus, crucial information for forest management and fire prevention such as road width and slope would be misevaluated. This paper deals with road and pathway detection in a complex forested mountaneous area and with their geometrical parameter extraction using lidar data. Firstly, a three-step image-based methodology is proposed to detect road regions. Lidar feature orthoimages are first generated. Then, road seeds are both automatically and semi-automatically detected. And, a region growing algorithm is carried out to retrieve the full pathways from the seeds previously detected. Secondly, these pathways are vectorized using morphological tools, smoothed, and discretized. Finally, 1D sections within the lidar point cloud are successively generated for each point of the pathways to estimate more accurately road widths in 3D. We also retrieve a precise location of the pathway borders and centers, exported as vector data.

CONCLUSION
A full workflow for the pathway detection on mountainous area, from raw ALS data to vector database objects, have been proposed. With the increasing use of ALS data for DTM generation, such workflow should enabled to decrease the data acquisition cost for mapping institute. The detected pathways could also be used both for improving DTM generation and as features for strip adjustment and registration. The results show the feasibility of generating and updating pathway databases from ALS data, but their quality is still insufficient to be used on a production context for mapping agencies. In order to tackle the mentioned issues, it has been draw perspectives to improve robustness and automaticity of pathway detection."