September 10, 2025Hydrology ResearchOpen Access

Improved lightweight water body recognition model using Mobile-Former network

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The Mobile-Former model reduces computational overhead significantly while enhancing feature extraction for water body recognition.
By using dilated convolution, the model improved accuracy on flood inundation tasks, achieving IoU scores as high as 91.81%.
The new architecture is proficient in real-time processing, making it suitable for resource-limited situations.
Validation with Chaohu flood data confirms its effectiveness in disaster response applications, showcasing practical utility.

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Proposed model improves flood inundation extraction accuracy to 98.79% in challenging environments, suggesting effective real-time solutions.

Shi et al. (2025) studied this question.

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Proposed model improves flood inundation extraction accuracy to 98.79% in challenging environments, suggesting effective real-time solutions.