Effective Post-Flood Water Level Classification from UAV Images using Deep Learning Techniques

Floods are considered the most significant disaster that occurs frequently worldwide. Floods result from heavy rainfall, snow melts, or storm surges caused due to climate change. Identifying custom UAV flood image datasets for classifying them into extreme or low floodwater level images would assist the rescue teams in prioritizing based on floodwater levels and deploying the resources to perform effective rescue operations. This work uses a Convolutional Block Attention Module (CBAM) to enhance water-level classification performance using pre-trained CNN models, Vision (ViT), and Shifted window (Swin) transformers. This article proposed a hybrid transformer that combines best-performing pre-trained CNN models with Vision transformers for improved classification performance. Experimental results demonstrate the model’s effectiveness in classifying the images into extreme floodwater or low floodwater levels by leveraging features from attention-based transformer models.