Graph Attention Convolutional U-NET: A Semantic Segmentation Model for Identifying Flooded Areas

TL;DR

This paper introduces GAC-UNET, a novel graph neural network-based model with attention mechanisms for accurately segmenting flooded areas from aerial imagery, improving disaster response and urban planning.

Contribution

The paper presents the GAC-UNET model, integrating graph attention and Chebyshev layers into U-Net, and explores transfer learning to enhance flood segmentation accuracy.

Findings

Achieved 91% mAP in flood segmentation

Attained 94% dice score, outperforming existing methods

Reached 89% IoU, demonstrating high accuracy

Abstract

The increasing impact of human-induced climate change and unplanned urban constructions has increased flooding incidents in recent years. Accurate identification of flooded areas is crucial for effective disaster management and urban planning. While few works have utilized convolutional neural networks and transformer-based semantic segmentation techniques for identifying flooded areas from aerial footage, recent developments in graph neural networks have created improvement opportunities. This paper proposes an innovative approach, the Graph Attention Convolutional U-NET (GAC-UNET) model, based on graph neural networks for automated identification of flooded areas. The model incorporates a graph attention mechanism and Chebyshev layers into the U-Net architecture. Furthermore, this paper explores the applicability of transfer learning and model reprogramming to enhance the accuracy of flood area segmentation models. Empirical results demonstrate that the proposed GAC-UNET model, outperforms other approaches with 91\% mAP, 94\% dice score, and 89\% IoU, providing valuable insights for informed decision-making and better planning of future infrastructures in flood-prone areas.