Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification-Reference-Cited by-同舟云学术

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Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification

Published:2021-10 Issue:10 Volume:59 Page:8288-8304
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Moya Luis^ORCID,Geis Christian^ORCID,Hashimoto Masakazu,Mas Erick^ORCID,Koshimura Shunichi^ORCID,Strunz Gunter

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9547579/09321713.pdf?arnumber=9321713

Cited by 13 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

1. Applying the multi-dimensional damage assessment (MDDA) methodology to the Cumbre Vieja volcanic eruption in La Palma (Spain);Natural Hazards;2024-07-20

2. Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey;Buildings;2024-03-26

3. Improving Operational Use of Post-Disaster Damage Assessment for Urban Search and Rescue by Integrated Graph-Based Multimodal Remote Sensing Data Analysis;2024

4. Seismic Risk Regularization for Urban Changes Due to Earthquakes: A Case of Study of the 2023 Turkey Earthquake Sequence;Remote Sensing;2023-05-25

5. Using remote sensing for exposure and seismic vulnerability evaluation: is it reliable?;GIScience & Remote Sensing;2023-04-12

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