Cross-Scale-Guided Fusion Transformer for Disaster Assessment Using Satellite Imagery
Author:
Affiliation:
1. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
2. Department of Geography, University of Colorado Boulder, Boulder, CO, USA
Funder
Basic and Applied Basic Research Foundation of Guangdong Province
GuangDong Natural Science Foundation
Shenzhen Science and Technology Innovation Program
National Natural Science Foundation of China
U.S. Department of Commerce National Institute of Standards and Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
https://ieeexplore.ieee.org/ielam/36/10006360/10197573-aam.pdf
Reference50 articles.
1. Assessment of the Degree of Building Damage Caused by Disaster Using Convolutional Neural Networks in Combination with Ordinal Regression
2. Building-Based Damage Detection From Postquake Image Using Multiple-Feature Analysis
3. Building disaster damage assessment in satellite imagery with multi-temporal fusion;weber;arXiv 2004 05525,2020
4. Coarse-to-Fine Satellite Images Change Detection Framework via Boundary-Aware Attentive Network
5. Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images
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