Detecting Occluded and Dense Trees in Urban Terrestrial Views With a High-Quality Tree Detection Dataset-Reference-Cited by-同舟云学术

Detecting Occluded and Dense Trees in Urban Terrestrial Views With a High-Quality Tree Detection Dataset

Author:

Wang Yongzhen¹^ORCID,Yan Xuefeng¹^ORCID,Bao Hexiang¹^ORCID,Chen Yiping²^ORCID,Gong Lina¹^ORCID,Wei Mingqiang³^ORCID,Li Jonathan⁴^ORCID

Affiliation:

1. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen, China

3. Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics, Shenzhen, China

4. Department of Geography and Environmental Management and Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada

Funder

National Natural Science Foundation of China

Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China

14th Five-Year Planning Equipment Pre-Research

Free Exploration of Basic Research Project, Local Science and Technology Development Fund Guided by the Central Government of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

Reference56 articles.

2. Faster R-CNN: Towards real-time object detection with region proposal networks;ren;Proc Adv Neural Inf Process Syst (NIPS),2015

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

5. BIR-Net: A Lightweight and Efficient Bilateral Interaction Road Extraction Network;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024