Precision Forestry: Trees Counting in Urban Areas Using Visible Imagery based on an Unmanned Aerial Vehicle

被引:29
|
作者
Hassaan, Omair [1 ]
Nasir, Ahmad Kamal [1 ]
Roth, Hubert [2 ]
Khan, M. Fakhir [1 ]
机构
[1] Lahore Univ Management Sci, Lahore, Pakistan
[2] Univ Siegen, Dept Elektrotech & Informat, Fak 4, Lehrstuhl Regelungs & Steuerungstech RST, D-57068 Siegen, Germany
来源
IFAC PAPERSONLINE | 2016年 / 49卷 / 16期
关键词
Precision Forestry; Robotics; Vision; UAV;
D O I
10.1016/j.ifacol.2016.10.004
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This research work describes an approach to count trees in an urban environment. Furthermore it addresses the problems involved in detection of trees in aerial imagery. This work can be used to solve the problem of forest degradation and deforestation. Right now forest man labor isn't efficient enough to detect or prevent this problem. A multi-rotor UAV equipped with high resolution RGB camera was used to acquire aerial images and to count number of trees in surveyed area. Various issues involved in the robust implementation of proposed algorithm are discussed. The result of successful implementation of the proposed algorithm on multiple scenarios are also presented and we show that our naive approach is able to achieve approximate to 0.72 accuracy within reasonable amount of time. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码:16 / 21
页数:6
相关论文
共 50 条
  • [41] Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat
    D. Gómez-Candón
    A. I. De Castro
    F. López-Granados
    Precision Agriculture, 2014, 15 : 44 - 56
  • [42] Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat
    Gomez-Candon, D.
    De Castro, A. I.
    Lopez-Granados, F.
    PRECISION AGRICULTURE, 2014, 15 (01) : 44 - 56
  • [43] An Ensemble Machine Learning Model to Estimate Urban Water Quality Parameters Using Unmanned Aerial Vehicle Multispectral Imagery
    Lei, Xiangdong
    Jiang, Jie
    Deng, Zifeng
    Wu, Di
    Wang, Fangyi
    Lai, Chengguang
    Wang, Zhaoli
    Chen, Xiaohong
    REMOTE SENSING, 2024, 16 (12)
  • [44] Canopy Segmentation of Overlapping Fruit Trees Based on Unmanned Aerial Vehicle LiDAR
    Wang, Shiji
    Ji, Jie
    Zhao, Lijun
    Li, Jiacheng
    Zhang, Mian
    Li, Shengling
    AGRICULTURE-BASEL, 2025, 15 (03):
  • [45] Cooperative navigation of unmanned aerial vehicle swarm based on cooperative dilution of precision
    Chen, Mingxing
    Xiong, Zhi
    Liu, Jianye
    Wang, Rong
    Xiong, Jun
    INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2020, 17 (03)
  • [46] Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery
    Wang, Dejiang
    Huang, Yuping
    APPLIED SCIENCES-BASEL, 2024, 14 (07):
  • [47] Geometrical Characterization of Hazelnut Trees in an Intensive Orchard by an Unmanned Aerial Vehicle (UAV) for Precision Agriculture Applications
    Vinci, Alessandra
    Brigante, Raffaella
    Traini, Chiara
    Farinelli, Daniela
    REMOTE SENSING, 2023, 15 (02)
  • [48] Urban Tree Classification Based on Object-Oriented Approach and Random Forest Algorithm Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery
    Guo, Qian
    Zhang, Jian
    Guo, Shijie
    Ye, Zhangxi
    Deng, Hui
    Hou, Xiaolong
    Zhang, Houxi
    REMOTE SENSING, 2022, 14 (16)
  • [49] Accurate Measurement and Assessment of Typhoon-Related Damage to Roadside Trees and Urban Forests Using the Unmanned Aerial Vehicle
    Qin, Longjun
    Mao, Peng
    Xu, Zhenbang
    He, Yang
    Yan, Chunhua
    Hayat, Muhammad
    Qiu, Guo-Yu
    REMOTE SENSING, 2022, 14 (09)
  • [50] Tree Species Identification Based on FCN Using the Visible Images Obtained from an Unmanned Aerial Vehicle
    Dai Pengqin
    Ding Lixia
    Liu Lijuan
    Dong Luofan
    Huang Yiting
    LASER & OPTOELECTRONICS PROGRESS, 2020, 57 (10)