Efficient Classification of Imbalanced Natural Disasters Data Using Generative Adversarial Networks for Data Augmentation

Efficient Classification of Imbalanced Natural Disasters Data Using Generative Adversarial Networks for Data Augmentation

Rapid damage identification and classification in disastrous situations and natural disasters are crucial for efficiently directing aid and resources. With the development of deep learning techniques and the availability of imagery content on social media platforms, extensive research has focused on...

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Bibliographic Details
Main Authors: Rokaya Eltehewy, Ahmed Abouelfarag, Sherine Nagy Saleh
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:ISPRS International Journal of Geo-Information
Subjects:
data augmentation
deep neural network architectures
disaster classification
ensemble classifiers
generative adversarial networks
Online Access:https://www.mdpi.com/2220-9964/12/6/245

Internet

https://www.mdpi.com/2220-9964/12/6/245

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