<![CDATA[Flood disasters consistently cause massive damage every year, making rapid mapping of affected areas crucial for coordinating emergency aid. The use of unmanned aerial vehicles (UAVs) offers a practical solution to obtain high-resolution aerial imagery, but manually identifying flood areas from hundreds of images remains time-consuming. This study analyzes and compares two deep learning segmentation architectures, U-Net and Attention U-Net, for automatic flood area detection from UAV RGB images. Both models were trained using 290 image-mask pairs from a public dataset, with a split of 70% for training, 10% for validation, and 20% for testing. Images were processed at a resolution of 256×256 pixels, normalized to the range [0,1], and augmented with horizontal flipping, brightness adjustment, and affine transformations. Attention U-Net enhances the standard U-Net structure by adding attention gates to all skip connections in the decoder to suppress irrelevant background features. Both models were evaluated across five independent training runs using different random seeds to assess result robustness. Across these runs, Attention U-Net achieved a marginally higher mean IoU (77.11% ± 0.76) and Dice/F1 (87.07% ± 0.49) compared to the U-Net baseline (IoU: 76.97% ± 0.54; Dice/F1: 86.98% ± 0.34), but a paired t-test revealed that these differences were not statistically significant (IoU: p = 0.77; Dice/F1: p = 0.77). These results suggest that, on this dataset, attention gates do not provide a measurable advantage over the standard U-Net architecture, establishing both as comparable practical baselines for future flood mapping research.]]>
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