Transformer-based Flood Scene Segmentation for Developing Countries Ahan M R * BITS Pilani Goa Campus ahanmr98@gmail.com Roshan Roy * BITS Pilani rroshanroy@gmail.com Shreyas Sunil Kulkarni BITS Pilani Hyderabad Campus sskshreyas@gmail.com Vaibhav Soni MANIT Bhopal vaibsoni@gmail.com Ashish Chittora BITS Pilani Goa Campus ashishc@goa.bits-pilani.ac.in 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 coun- tries. 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 neighbor- hoods. 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. 1 Introduction and Context The Center for Research on the Epidemiology of Disasters, in affiliation with the World Health Organization (WHO), reported that natural disasters accounted for 1.3 million deaths and over USD 2 trillion in economic damage — all between 1998 and 2017 [19]. Flooding related damage is a factor in most of them [4] and frequent the list of most expensive disasters [17]. Developing economies of Asia are disproportionately affected and are the worst-hit by floods, accounting for 44% of all flood disasters from 1987-1997 [18]. India alone registers 1/5th of global deaths from floods [11]. Rapid urbanization, global climate change, and rising sea water levels will expose 1.47 billion more people to flood risk, with 89% of them living in low-middle income countries [5]. Flood Segmentation technology is instrumental for Disaster Prediction and Response is critical to save lives and livelihoods. Flood Response: Typically, disaster management teams complete a Post Disaster Needs Assessment (PDSA) and rapidly develop infrastructure based on this report on the collected data [6]. Unmanned Aerial Vehicles (UAVs) are deployed to collect large volumes of image and video data in affected * Equal Contribution 35th Conference on Neural Information Processing Systems (NeurIPS 2021), virtual. arXiv:2210.04218v1 [cs.CV] 9 Oct 2022