Social Media Flood Image Classification Using Transfer Learning with EfficientNet Variants

Abstract

Social media posts consist of a large number of flood-related images and data. These images can be helpful for the flood image classification process and necessary to produce real-time information about road accessibility during rescue and disaster management. In this paper, social media images are classified into flooded and non-flooded categories using EfficientNet variants with MediaEval 2017 Disaster Image Retrieval from the social media dataset. The transfer learning technique is employed to achieve good performance and efficiency. Then different variants are compared using various metrics after and before freezing. The higher variants are intentionally avoided, as it does not perform with better efficiency and accuracy. The B3 variant outperforms all other variance when considering model validation accuracy and false positive rate after unfreezing top layers. But when analysing the classification report, all variants show almost equal performance and weighted average F1-score.