Using Generative Adversarial Networks for Enhanced Augmentation on Natural Disaster

Shivam Pandey, Vijay Bhardwaj, Kavita Thukral, Mohit Yadav

2025

Abstract

Regarding effectively allocating relief as well as resources in tragic circumstances and catastrophic events, rapid damages recognition and categorization is essential. Numerous studies have been conducted as a result of the growth of methods for deep learning as well as the accessible nature of pictures on social networking sites. It has concentrated on assessing damages. Using geographic information from such instances, those pictures' visual qualities enable immediate assessment of the region's security condition. This study suggests a system for categorizing disasters; this includes a variety of catastrophic photos that have been synthesized using generating adversarial particular adjusting of a deep segmentation neuronal network, and adversarial generative networks using a model. In this research, a structure for categorizing disasters is proposed. It blends a collection of synthetic, different calamity photos produced by generative adversarial networks, with domain-dependent adjusting of a deep convolutional neural network -based system. Due to the fact that previous research in this field has mostly been hampered by a lack of data materials, an example dataset high- lighting the problem of the unbalanced categorization of several catastrophic events has been created and enhanced. Investigations, qualitative as well as quantitative information, demonstrate the effectiveness of the information enhancement technique used to create a data set with equilibrium. Additional tests con- ducted with different metrics to assess confirmed the suggested framework's precision and generalizability across multiple categories when compared with additional cutting-edge approaches for the objective of catastrophe categorization. The structure performed better than the remaining algorithms by an additional 11% annually in terms of validating reliability.

Paper Citation

in Harvard Style

Pandey S., Bhardwaj V., Thukral K. and Yadav M. (2025). Using Generative Adversarial Networks for Enhanced Augmentation on Natural Disaster. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 664-670. DOI: 10.5220/0013583400004664

in Bibtex Style

@conference{incoft25,
author={Shivam Pandey and Vijay Bhardwaj and Kavita Thukral and Mohit Yadav},
title={Using Generative Adversarial Networks for Enhanced Augmentation on Natural Disaster},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={664-670},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013583400004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Using Generative Adversarial Networks for Enhanced Augmentation on Natural Disaster
SN - 978-989-758-763-4
AU - Pandey S.
AU - Bhardwaj V.
AU - Thukral K.
AU - Yadav M.
PY - 2025
SP - 664
EP - 670
DO - 10.5220/0013583400004664
PB - SciTePress