Flood-Eye: Intelligent Segmentation and Area Estimation of Flooded Regions

In this paper, we present Flood-Eye, an advanced framework for segmenting images intended for precise flood area estimation and segmentation. With its dual-pathway encoder, Flood-Eye is able to record contextual information at low resolution in addition to high-resolution details. To successfully integrate these features and improve the segmentation’s consistency, our model makes use of a novel cross-attention fusion technique. Furthermore, adaptive refinement is provided via a dynamic convolutional layer that adjusts responses to different flood patterns. A reliable component for estimating flooded areas is also included in the model, providing a quantitative analysis of the area of the flooded zone. With a mean intersection over union (mIoU) of 83.26% on the FloodNet dataset, Flood-Eye outperforms other state-of-the-art models in terms of performance and offers comprehensive and useful insights for efficient flood management and disaster response. This URL provides segmentation prediction and area estimation, which is drone footage of flooding in South Florida on June 14, 2024, and it clearly demonstrates the flooding’s impact and the need for advanced flood detection models like Flood-Eye. https://youtu.be/zltuXwrGUZM.