IUNet-IF: identification of construction waste using unmanned aerial vehicle remote sensing and multi-layer deep learning methods-Reference-Cited by-同舟云学术

IUNet-IF: identification of construction waste using unmanned aerial vehicle remote sensing and multi-layer deep learning methods

Published:2022-12-17 Issue:19-24 Volume:43 Page:7181-7212
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Gao Siyan¹²^ORCID,Liu Yang¹³⁴,Cao Shisong¹³⁴,Chen Qiang¹³⁴^ORCID,Du Mingyi¹³⁴,Zhang Daixinyue¹^ORCID,Jia Jingjue¹,Zou Weilin²

Affiliation:

1. School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing, China

2. Tech Center, Zhengyuan Geomatics Group Co Ltd, Beijing, China

3. Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing, China

4. Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing Institute of Surveying and Mapping, Beijing, China

Funder

the National Key Research and Development Program of China

Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture

Beijing Key Laboratory of Urban Spatial Information Engineering

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2022.2155084

Reference51 articles.

1. Dense prediction of label noise for learning building extraction from aerial drone imagery

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4. Chen, J. 2019. “Optical remote sensing image target detection based on multi-scale deconvolution feature fusion network.” PhD diss., Xi’an University of Electronic Science and Technology.

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