Transformer-based Flood Scene Segmentation for Developing Countries

TL;DR

This paper introduces FloodTransformer, a novel visual transformer-based model for flood segmentation in aerial images, achieving high accuracy and robustness to aid early warning and disaster response in developing countries.

Contribution

It presents the first transformer-based model for flood segmentation and a new metric, Flood Capacity, to quantify flood extent from aerial imagery.

Findings

Achieved 0.93 mIoU on SWOC Flood dataset.

Outperformed existing methods in flood segmentation accuracy.

Validated robustness across unseen flood images.

Abstract

Floods are large-scale natural disasters that often induce a massive number of deaths, extensive material damage, and economic turmoil. The effects are more extensive and longer-lasting in high-population and low-resource developing countries. Early Warning Systems (EWS) constantly assess water levels and other factors to forecast floods, to help minimize damage. Post-disaster, disaster response teams undertake a Post Disaster Needs Assessment (PDSA) to assess structural damage and determine optimal strategies to respond to highly affected neighbourhoods. However, even today in developing countries, EWS and PDSA analysis of large volumes of image and video data is largely a manual process undertaken by first responders and volunteers. We propose FloodTransformer, which to the best of our knowledge, is the first visual transformer-based model to detect and segment flooded areas from aerial images at disaster sites. We also propose a custom metric, Flood Capacity (FC) to measure the spatial extent of water coverage and quantify the segmented flooded area for EWS and PDSA analyses. We use the SWOC Flood segmentation dataset and achieve 0.93 mIoU, outperforming all other methods. We further show the robustness of this approach by validating across unseen flood images from other flood data sources.