Remote sensing building damage assessment with a multihead neighbourhood attention transformer-Reference-Cited by-同舟云学术

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Remote sensing building damage assessment with a multihead neighbourhood attention transformer

Published:2023-08-11 Issue:16 Volume:44 Page:5069-5100
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Yu Chen¹,Hu Bin²,Cheng Xiuchuan¹,Yin Guangqiang¹,Wang Zhiguo¹³

Affiliation:

1. Shool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. Chengdu Institute of the Tibetan Plateau Earthquake Research, China Earthquake Administration, Chengdu, China

3. Kashi Institute of Electronics and Information Industry, Kashi, P.R. China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2242590

Reference55 articles.

1. Damage Characterization in Urban Environments from Multitemporal Remote Sensing Datasets Built from Previous Events

2. Pyramid Pooling Module-Based Semi-Siamese Network: A Benchmark Model for Assessing Building Damage from xBD Satellite Imagery Datasets

3. A Transformer-Based Siamese Network for Change Detection

4. Crowdsourcing earthquake damage assessment using remote sensing imagery

5. Deep Vision Transformers for Remote Sensing Scene Classification

Cited by 3 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. Mbda-net: a building damage assessment model based on a multi-scale fusion network;Signal, Image and Video Processing;2024-09-10

2. An Improved Instance Segmentation Method for Fast Assessment of Damaged Buildings Based on Post-Earthquake UAV Images;Sensors;2024-07-05

3. Efficient building damage assessment from post-disaster aerial video using lightweight deep learning models;International Journal of Remote Sensing;2023-11-13

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