FedLD: Federated Learning for Privacy-Preserving Collaborative Landslide Detection-Reference-Cited by-同舟云学术

FedLD: Federated Learning for Privacy-Preserving Collaborative Landslide Detection

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Tang Xiaochuan¹^ORCID,Yan Xiaochuang¹,Yuan Xiaojun²^ORCID,Liu Xin¹,Lu Zhong¹,Wang Yu¹,Zhong Hao¹^ORCID,Li Dongfen¹,Catani Filippo³^ORCID

Affiliation:

1. College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China

2. National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu, China

3. Department of Geosciences, Machine Intelligence and Slope Stability Laboratory, University of Padua, Padua, Italy

Funder

National Key Research and Development Program of China

China Postdoctoral Science Foundation

Open Fund of Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Administration of China

Open Fund of Key Laboratory of Deep-Time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources, Chengdu University of Technology

“Geosciences for Sustainable Development” project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/8859/10365397/10621036.pdf?arnumber=10621036

Reference24 articles.

1. Landslide detection, monitoring and prediction with remote-sensing techniques

2. A practical trial of landslide detection from single-temporal Landsat8 images using contour-based proposals and random forest: a case study of national Nepal

3. An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images

4. Automatic Detection of Coseismic Landslides Using a New Transformer Method

5. Feature-Fusion Segmentation Network for Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data