Accurate and Efficient Urban Street Tree Inventory with Deep Learning on Mobile Phone Imagery-Reference-Cited by-同舟云学术

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Accurate and Efficient Urban Street Tree Inventory with Deep Learning on Mobile Phone Imagery

Published:2023-11-28 Issue: Volume:34 Page:486-493
ISSN:
Container-title:2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
language:
Short-container-title:

Author:

Khan Asim¹,Nawaz Umair²,Ulhaq Anwaar³,Gondal Iqbal⁴,Javed Sajid⁵

Affiliation:

1. Khalifa University,Department of Mechanical and Nuclear Engineering,Abu Dhabi,United Arab Emirates (UAE)

2. Namal University,Department of Electrical Engineering,Mianwali,Pakistan

3. Charles Sturt University,School of Computing. Mathematics and Engineering,Port Macquarie,Australia

4. Royal Melbourne Institute of Technology (RMIT) University,School of Computing Technologies,Melbourne,Australia

5. Khalifa University,Department of Electrical Engineering and Computer Science,Abu Dhabi,United Arab Emirates (UAE)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10410651/10410903/10410974.pdf?arnumber=10410974

Reference28 articles.

1. Evaluation of Close-Range Photogrammetry Image Collection Methods for Estimating Tree Diameters

2. Prediction of Individual Tree Diameter and Height to Crown Base Using Nonlinear Simultaneous Regression and Airborne LiDAR Data

3. Development of Measuring Device for Diameter at Breast Height of Trees

4. Estimation of DBH at Forest Stand Level Based on Multi-Parameters and Generalized Regression Neural Network

5. Estimation of DBH at Forest Stand Level Based on Multi-Parameters and Generalized Regression Neural Network

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