Flood or Non-Flooded: A Comparative Study of State-of-the-Art Models for Flood Image Classification Using the FloodNet Dataset with Uncertainty Offset Analysis

Abstract

Natural disasters, such as floods, can cause significant damage to both the environment and human life. Rapid and accurate identification of affected areas is crucial for effective disaster response and recovery efforts. 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. To achieve this, we trained son 11 state-of-the-art (SOTA) models and modified them to suit the classification task at hand. Furthermore, we also introduced a technique of varying the uncertainty offset λ in the models to analyze its impact on the performance. The models were evaluated using standard classification metrics such as Loss, Accuracy, F1 Score, Precision, Recall, and ROC-AUC. 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. 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.