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Abstract
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Flooding is one of the most serious natural hazards worldwide, causing significant and often
irreversible damage to economies, infrastructure, and human health in urban areas. The rising
frequency of flood events and concerns about the likelihood of future occurrences underscore the
urgent need for thorough investigations into flood dynamics. Creating a flood hazard area map is the
first critical step in evaluating flood-related damage. In this work, a new flood mapping methodology
is presented, which integrates multiple datasets. In this methodology, optical and radar remote sensing
data are fused, utilizing a deep learning capsule network. Thanks to the multi-dimensional/multiscale
kernels and feature extraction in vector form within the suggested capsule network, notable
outcomes are achieved. The proposed flood mapping methodology is implemented in three main
steps: (1) fusing radar and optical data, (2) utilizing a capsule deep learning network to preserve the
relationships among features extracted from the convolutional neural network (CNN), and (3)
applying a mechanism to enhance the accuracy of flood zoning. The suggested method is utilized for
flood mapping in the Gorganrood watershed in northern Iran, employing Sentinel-1 and Sentinel-2
imagery. The results were rigorously compared to established methods, includingCNNand weighted
CNNensemble (WCNNE). The results demonstrated that the suggested method outperformedCNN
by 6.96% andWCNNEby 3.72% in overall accuracy and provided a remarkable area under the
receiver operating curve of 98.34%. It was also observed that incorporating the attention mechanism
slightly increased the accuracy of the proposed network by 0.23%. The findings suggest that the
proposed methodology has considerable potential for accurate floodwater mapping globally.
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