ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 211–219, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-211-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 211–219, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-211-2020

  03 Aug 2020

03 Aug 2020

LIVE EXTRACTION OF CURVILINEAR STRUCTURES FROM LIDAR RAW DATA

P. Even and P. Ngo P. Even and P. Ngo
  • Université de Lorraine, LORIA (UMR 7503), Nancy, France

Keywords: Lidar, raw data processing, digital geometry, forested area, mountainous area, road extraction, ridge extraction

Abstract. In this paper, a general framework is proposed for live extraction of curvilinear structures such as roads or ridges from airborne LiDAR raw data, in the scope of present and past man-environment interaction studies. Unlike most approaches in literature, classified ground points are directly processed here, rather than derived products such as digital terrain models (DTM). This allows to detect possible lacks of ground points due to LiDAR signal occlusions caused by dense coniferous canopies. An efficient and simple solution based on discrete geometry tools is described for supervised context in which the user just indicates where the extraction should take place. Fast response times are required to ensure a good man-system interaction.

The framework performance is first evaluated on the example of the extraction of forest roads in a mountainous area, as these objects are well marked in the DTM and hence provide some kind of ground truth. Good execution time and accuracy level are reported. Then this framework is applied to the detection of prominent curvilinear structures, which are much more diffuse objects, but of greater interest than roads in the scope of the present project. Achieved results show high potential of the proposed approach to help archaeologists and geomorphologists in finding areas of interest for future prospection using LiDAR data.