# Attentive Dual Stream Siamese U-net for Flood Detection on Multi-temporal Sentinel-1 Data

arXiv: https://arxiv.org/abs/2204.09387 · HTML: https://mlsanity.com/paper/2204.09387/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 20 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://mlsanity.com/paper/1803.02579/) (2018)
- [H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement](https://mlsanity.com/paper/2010.05309/) (2020)

## Built on by (most cited first, 7 of 7)

- [Unsupervised Flood Detection on SAR Time Series](https://mlsanity.com/paper/2212.03675/) (2022)
- [Cross Modal Distillation for Flood Extent Mapping](https://mlsanity.com/paper/2302.08180/) (2023)
- [Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery](https://mlsanity.com/paper/2404.19043/) (2024)
- [DAM-Net: Global Flood Detection from SAR Imagery Using Differential Attention Metric-Based Vision Transformers](https://mlsanity.com/paper/2306.00704/) (2023)
- [On the Importance of Feature Representation for Flood Mapping using Classical Machine Learning Approaches](https://mlsanity.com/paper/2303.00691/) (2023)
- [ZAYAN: Disentangled Contrastive Transformer for Tabular Remote Sensing Data](https://mlsanity.com/paper/2604.27606/) (2026)
- [Beyond Backscatter: AlphaEarth Land-Cover Priors for Rapid SAR Flood Segmentation Across Foundation Backbones](https://mlsanity.com/paper/2606.29134/) (2026)

## Abstract (arXiv)

Due to climate and land-use change, natural disasters such as flooding have been increasing in recent years. Timely and reliable flood detection and mapping can help emergency response and disaster management. In this work, we propose a flood detection network using bi-temporal SAR acquisitions. The proposed segmentation network has an encoder-decoder architecture with two Siamese encoders for pre and post-flood images. The network's feature maps are fused and enhanced using attention blocks to achieve more accurate detection of the flooded areas. Our proposed network is evaluated on publicly available Sen1Flood11 benchmark dataset. The network outperformed the existing state-of-the-art (uni-temporal) flood detection method by 6% IOU. The experiments highlight that the combination of bi-temporal SAR data with an effective network architecture achieves more accurate flood detection than uni-temporal methods.