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Evaluation of The Tree Structure Segmentation Accuracy using PCD Deep Learning Models and Rule-Based Systems on LiDAR Data

첫 페이지 보기
  • 발행기관
    강원대학교 산림과학연구소 바로가기
  • 간행물
    강원대학교 산림과학연구소 학술대회 바로가기
  • 통권
    2024 International Symposium of Institute of Forest Science (2024.10)바로가기
  • 페이지
    pp.116-116
  • 저자
    Sang-Jin Lee, Jung-Soo Lee
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A467148

원문정보

초록

영어
The purpose of this study was to develop and evaluate Point Cloud Data (PCD) deep learning models and a rule-based system for segmenting tree structures (stems and crowns) using fixed terrestrial LiDAR data. The dataset comprised 48 Larix Kaemferi trees, which were collected and preprocessed. For the PCD deep learning models, three downsampled datasets consisting of 1024, 4096, and 16384 points were constructed from the original data. The data was divided into training (70%) and validation (30%) sets. Models were built using PointNet and PointNet++ architectures, resulting in a total of 12 tree structure segmentation models for accuracy comparison. The rule-based system was developed using the original data, applying techniques such as verticality checks, cylindrical structure detection, and slice-based circular fitting to detect the stem. It then segmented the stem through repetitive circle fitting and validation processes based on height. The average accuracy of the PCD deep learning tree structure segmentation models was approximately 95%, with the PointNet++ model using 16384 points achieving the highest classification accuracy of about 98%. The rule-based system achieved high classification accuracy of over 99% for both tree species. This study is expected to contribute to precise measurement and efficient management of forest resources by presenting automated methods for tree structure segmentation using AI technology and rule-based approaches. It is anticipated that this research will serve as a foundation for the advancement of forest digitalization, precision forest management technologies, forest structure analysis, and timber production estimation in various fields.

키워드

Tree Structure Segmentation Terrestrial LiDAR Point Deep-learning Rule-based System

저자

  • Sang-Jin Lee [ Department of Forest Management, Division of Forest Sciences, College of Forest and Forest and Environmental Sciences, Kangwon National University ]
  • Jung-Soo Lee [ Department of Forest Management, Division of Forest Sciences, College of Forest and Forest and Environmental Sciences, Kangwon National University ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    강원대학교 산림과학연구소 [Institute of Forest Science Kangwon National University]
  • 설립연도
    1975
  • 분야
    농수해양>임학
  • 소개
    강원대학교부설산림과학연구소(이하 “연구소”라 한다)는 산림에 관한 제반 학술적 연구를 통하여 산림자원의 효용을 밝히고 임업 및 임산업의 발전에 기여함을 목적으로 한다.

간행물

  • 간행물명
    강원대학교 산림과학연구소 학술대회
  • 간기
    부정기
  • 수록기간
    2017~2024
  • 십진분류
    KDC 526 DDC 634

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