Building Extraction from High-Resolution Remote Sensing Images Using Improved HRNet Method-Reference-Cited by-同舟云学术

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Building Extraction from High-Resolution Remote Sensing Images Using Improved HRNet Method

Published:2024-07-07 Issue: Volume: Page:7982-7985
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Bai Jiaqi¹,Jia Chen¹,Yu Shengwen¹,Sun Lin¹,Zhang Ling²,Chang Zexiu³,Hou AiXia⁴

Affiliation:

1. Shandong University of Science and Technology,College of Geomatics,Qingdao,P.R. China

2. Shandong Drug And Food Vocational College,Zibo,P.R. China

3. Shandong Provincial Institute of Land Surveying and Mapping,Jinan,P.R. China

4. Binzhou Polytechnic,Binzhou,P.R. China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10642302.pdf?arnumber=10642302

Reference5 articles.

1. Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States

2. Automatic Building Footprint Extraction from Multi-Resolution Remote Sensing Images Using a Hybrid FCN

3. Remote Sensing Image Segmentation Method based on HRNET

4. Semantic Segmentation of Urban Buildings Using a High-Resolution Network (HRNet) with Channel and Spatial Attention Gates[J];Choi;Remote Sensing,2021

5. BuildMapper: A fully learnable framework for vectorized building contour extraction

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