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Automatic forest road extraction from LiDAR data of mountainous areas

Philippe Even 1 Phuc Ngo 1
1 ADAGIO - Applying Discrete Algorithms to Genomics and Imagery
LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry
Abstract : In this paper, a framework is proposed to extract forest roads from LiDAR (Light Detection and Ranging) data in mountainous areas. For that purpose, an efficient and simple solution based on discrete geometry and mathematical morphology tools is proposed. The framework is composed of two steps: (i) detecting road candidates in DTM (Digital Terrain Model) views using a mathematical morphology filter and a fast blurred segment detector in order to select a set of road seeds; (ii) extracting road sections from the obtained seeds using only the raw LiDAR points to cope with DTM approximations. For the second step, a previous tool for fast extraction of linear structures directly from ground points was adapted to automatically process each seed. It first performs a recognition of the road structure under the seed. In case of success, the structure is tracked and extended as far as possible on each side of the segment before post-processing validation and cleaning. Experiments on real data over a wide mountain area (about 78 km^2) have been conducted to validate the proposed method.
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Contributor : Phuc Ngo <>
Submitted on : Friday, March 5, 2021 - 3:12:19 PM
Last modification on : Monday, March 22, 2021 - 4:50:05 PM


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  • HAL Id : hal-03144147, version 1


Philippe Even, Phuc Ngo. Automatic forest road extraction from LiDAR data of mountainous areas. DGMM 2021 - IAPR International Conference on Discrete Geometry and Mathematical Morphology, May 2021, Uppsala, Sweden. ⟨hal-03144147⟩



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