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This study introduces a robust deep learning framework to analyze the relationship between population density changes and flood inundation, which is crucial for improving disaster management and urban resilience strategies. As the environment changes, population grows rapidly, and infrastructure advances, it is vital to ensure urban areas can withstand floods while maintaining functionality and safety. Preparedness through anticipation and resource allocation in high-risk areas enhances risk assessment. Using high-resolution satellite imagery, socio-economic datasets, and a modified U-Net architecture to process multispectral and Synthetic Aperture Radar (SAR) data, the study generates detailed maps of population shifts and flood extents. The Intersection over Union (IoU) metric rigorously validates the model's accuracy in predicting and mapping flood and population data. The findings show significant correlations between flood events and population distribution changes, offering empirical insights into infrastructure development and resilience planning. These insights help develop effective policies to manage urbanization and population in flood-prone areas. Additionally, the results provide data-driven insights for infrastructure and resilience planning in response to climate change challenges. This information aids policymakers in addressing the impacts of climate change on vulnerable communities, supporting community empowerment, and facilitating early restoration post crisis.

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  • U-Net architecture is applied in advanced deep learning applications with SAR.

  • A robust deep learning framework to analyze the relationship between population density changes and flood inundation.

  • The Intersection over Union (IoU) metric proves to be effective for accurate model evaluation.

  • A significant correlation between flood events and population movements, offering critical, data-driven insights.

climate-induced migration, disaster management and planning, dual-encoder U-Net, flood resilience, geospatial data integration, population displacement, Sundarbans
© 2025 The Authors
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-ND 4.0), which permits copying and redistribution with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/by-nd/4.0/).