A Deep Learning Ensemble Model for Flood Image Classification

Abstract

Flood is a type of natural disaster that leads to a widespread devastation. The increasing amount of rain specifically in the Urban regions of Sindh province causes several issues, whereas the drainage system is not very efficient to handle the large amount of water in a short period of time. Identification of floods is essential for disaster response, as it helps locate areas which need immediate help. Recently, the deep learning-based models have shown the best performance for image classification tasks. In this paper, a deep learning-based ensemble model has been developed where four state-of-the-art deep learning models are combined to classify flood from the images either captured with the mobile camera or other image capturing devices. The deep learning ensemble model has been trained and tested on the two publicly available datasets labelled with flood and non-flood images. To enhance the efficacy of the deep learning-based ensemble model, the hyper-parameters of the four models are fine-tuned. The results obtained show that the deep learning-based ensemble model outperforms than the individual models.