Aerial Image Classification in Post Flood Scenarios Using Robust Deep Learning and Explainable Artificial Intelligence

Efficiently delivering timely assistance to flooded regions is a critical imperative, and leveraging deep-learning methodologies has demonstrated significant efficacy in addressing environmental challenges. Several authors have collected data from specific regions and presented the methodologies for classifying flooded images. However, there are two main limitations of existing methodologies and benchmark datasets. Firstly, the models are trained on the images collected from specific geographical regions, which limits their ability to generalize when encountering images with diverse features or from varied regions. Secondly, the models are trained on high-resolution images and lack classification for the low-resolution images. In this study, we curated a dataset by merging benchmark datasets and acquiring images from web repositories. The main objective of this study is to address resolution issues and enhance model accuracy across diverse regions. We conducted a comparative analysis of the curated dataset using various deep-learning models based on CNN architecture. The experimental findings revealed that MobileNet and Xception outperformed ResNet-50, VGG-16, and Inception(v3) models, achieving an impressive accuracy rate of approximately 98% and an f1-score of 92% for flood class. Additionally, we have also used XAI Lime to interpret model results.