Natural Disaster Damage Assessment using Semantic Segmentation of UAV Imagery
Author:
Affiliation:
1. University of Engineering and Technology,Computer Engineering Department,Taxila,Pakistan
2. National Center of Robotics and Automation, University of Engineering and Technology,Swarm Robotics Lab,Taxila,Pakistan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10089530/10089531/10089539.pdf?arnumber=10089539
Reference34 articles.
1. Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks
2. Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning
3. Self Attention Based Semantic Segmentation on a Natural Disaster Dataset
4. Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
5. Change Capsule Network for Optical Remote Sensing Image Change Detection
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1. Semantic Segmentation in Aerial Imagery: A Novel Approach for Urban Planning and Development;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02
2. Deep artificial intelligence applications for natural disaster management systems: A methodological review;Ecological Indicators;2024-06
3. Scalable approach to create annotated disaster image database supporting AI-driven damage assessment;Natural Hazards;2024-05-19
4. Methods and datasets on semantic segmentation for Unmanned Aerial Vehicle remote sensing images: A review;ISPRS Journal of Photogrammetry and Remote Sensing;2024-05
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