The ability to quickly and accurately interpret images and videos captured in the aftermath of a disaster, using techniques like deep learning and CNNs can provide valuable information for decision-making in disaster management and recovery efforts. These statistics highlight the necessity for effective and precise disaster response and recovery efforts, particularly in regions that are most affected. Additionally, the report states that Asia was the most affected region, with 50 % of all-natural disasters recorded in 2019 occurring in Asia, followed by Africa ( 20 ) % and the Americas ( 19 ) % respectively. The report also highlights that floods were the most frequent type of natural disaster, accounting for 42 % of all-natural disasters recorded in 2019, followed by storms ( 26 ) % and heatwaves ( 10 ) %. The report states that a total of 9974 natural disasters were recorded in 2019, affecting over 208 million people and causing over 29 billion economic losses. The Natural Disasters DataBook 2019, a report published by the Centre for Research on the Epidemiology of Disasters (CRED) provides a comprehensive overview of natural disasters that occurred worldwide in 2019. In addition to the importance of visual scene understanding in disaster response and recovery, it’s also important to understand the scope and frequency of natural disasters. These techniques allow for the efficient and accurate identification of affected areas, which can aid in disaster response and recovery efforts. In recent years, the use of satellite imagery and convolutional neural networks (CNNs) has become an increasingly popular approach for monitoring and responding to natural disasters. Traditional methods of collecting information about the extent of damage caused by natural disasters, such as ground surveys, can be time-consuming, costly, and may not always provide accurate or comprehensive data. These findings can aid in the development of more accurate and efficient disaster response and recovery systems, which could help in minimizing the impact of natural disasters. The results of this study provide a quantitative comparison of the performance of different CNN architectures for flood image classification, as well as the impact of different uncertainty offset λ. The models were evaluated using standard classification metrics such as Loss, Accuracy, F1 Score, Precision, Recall, and ROC-AUC. Furthermore, we also introduced a technique of varying the uncertainty offset λ in the models to analyze its impact on the performance. To achieve this, we trained son 11 state-of-the-art (SOTA) models and modified them to suit the classification task at hand. In this paper, we aimed to evaluate the performance of state-of-the-art (SOTA) computer vision models for flood image classification, by utilizing a semi-supervised learning approach on a dataset named FloodNet. Rapid and accurate identification of affected areas is crucial for effective disaster response and recovery efforts. Natural disasters, such as floods, can cause significant damage to both the environment and human life.
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