Global road extraction using a pseudo-label guided framework: from benchmark dataset to cross-region semi-supervised learning-Reference-Cited by-同舟云学术

Global road extraction using a pseudo-label guided framework: from benchmark dataset to cross-region semi-supervised learning

Published:2024-06-21 Issue: Volume: Page:1-19
ISSN:1009-5020
Container-title:Geo-spatial Information Science
language:en
Short-container-title:Geo-spatial Information Science

Author:

Lu Xiaoyan¹²³^ORCID,Zhong Yanfei³^ORCID,Zheng Zhuo⁴,Wang JunJue³,Chen Dingyuan³,Su Yu³

Affiliation:

1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China

2. Research Centre for Artificial Intelligence in Geomatics, The Hong Kong Polytechnic University, Hong Kong, China

3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

4. Department of Computer Science, Stanford University, Stanford, CA, USA

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2024.2362760

Reference44 articles.

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3. Chen, T., S. Kornblith, M. Norouzi, and G. Hinton. 2020. “A Simple Framework for Contrastive Learning of Visual Representations.” In the International Conference on Machine Learning, 1597–1607. Online, July 12–18.

4. SemiRoadExNet: A semi-supervised network for road extraction from remote sensing imagery via adversarial learning

5. SW-GAN: Road Extraction from Remote Sensing Imagery Using Semi-Weakly Supervised Adversarial Learning