# Transformer-based Flood Scene Segmentation for Developing Countries

arXiv: https://arxiv.org/abs/2210.04218 · HTML: https://mlsanity.com/paper/2210.04218/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 9 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [Understanding Robustness of Transformers for Image Classification](https://mlsanity.com/paper/2103.14586/) (2021)
- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://mlsanity.com/paper/1611.09326/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling](https://mlsanity.com/paper/1505.07293/) (2015)
- [ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach](https://mlsanity.com/paper/2012.00671/) (2020)

## Built on by (most cited first, 3 of 3)

- [DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition](https://mlsanity.com/paper/2410.13663/) (2024)
- [Graph Attention Convolutional U-NET: A Semantic Segmentation Model for Identifying Flooded Areas](https://mlsanity.com/paper/2502.15907/) (2025)
- [Aerial Flood Scene Classification Using Fine-Tuned Attention-based Architecture for Flood-Prone Countries in South Asia](https://mlsanity.com/paper/2411.00169/) (2024)

## Abstract (arXiv)

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.