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Artificial intelligence-Enabled Lightweight Flood Segmentation Model with Polarization Fusion and Attention

Year: 2026

Type: article

Abstract: Rapid and reliable flood maps are essential for emergency response and disaster management. However, many deep learning models for flood segmentation are computationally demanding, limiting real-time use and deployment on modest hardware. This paper presents PFALNet, a reduced-parameter segmentation network that integrates dual-polarization synthetic aperture radar image backscatter and a polarization-ratio channel. The model employs concurrent spatial and channel squeeze-and-excitation attention to enhance multi-scale feature fusion for flood delineation. Experiments on a public Sentinel-1 flood-mapping benchmark show that PFALNet achieves strong performance on the Florence test region (F1 = 90.53%, IoU = 82.70%, κ = 88.80%) while remaining lightweight (0.31M parameters, 5.88 GFLOPs) and enabling real-time inference (6.23 ms per 256 × 256 tile, ∼ 160 tiles/s) on a single GPU. These results indicate that carefully designed lightweight synthetic aperture radar (SAR) segmentation models can deliver competitive performance with substantially reduced computational cost.

Source: EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

DOI: 10.4108/eetinis.133.12738

Authors: Premisha Premananthan, Brad McNiven, Muhammad Fahim, Quang Le, Hang Le

Institutions: Memorial University of Newfoundland, Queen's University Belfast, Duy Tan University

Language: English

Topic: Flood Risk Assessment and Management

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W7202015400

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