Deep Learning Based Flood Severity Detection Using UAV Images

Flood severity detection enables rapid, accurate, and automated assessment of flood impact for effective disaster response and resource allocation. Aerial imagery obtained from Unmanned Aerial Vehicles (UAVs) is utilized to assess flood severity by categorizing the impacted regions into two classes: “High Floods” and “Low Floods”. This research introduces a hybrid deep learning methodology for post-flood severity assessment utilizing two advanced models: EfficientNetB2 and Vision Transformer (ViT). Data augmentation methods are utilized to enhance the model's generalization skills. The proposed technique attains 93 % accuracy and an $\mathbf{F 1}$-score of $\mathbf{0. 9 3}$, signifying strong classification efficacy. This methodology can assist emergency responders and planners in making informed decisions on catastrophe management and recovery.