Flood Area Segmentation using Using U-Net: A Deep Learning Approach with UAV and Satellite Imagery

Flood area segmentation plays a critical role in disaster management and mitigation, providing valuable insights for timely interventions and resource allocation during flood events. This study explores the application of the U-Net model, a deep learning architecture known for its accuracy in image segmentation tasks, to segment flood-affected areas using a dataset comprising UAV and satellite imagery. The U-Net model's encoder-decoder structure is utilized to capture both high-level contextual and fine-grained spatial information, enabling precise flood area identification. Key performance metrics, including Intersection over Union (IoU) and Dice coefficient, are employed to evaluate the model's effectiveness. The model achieves high accuracy in segmenting complex flood regions, particularly in challenging environmental conditions where traditional methods fail. By leveraging advanced deep learning techniques, the U-Net model facilitates real-time flood monitoring, offering a robust tool for disaster management systems. The results demonstrate that the U-Net model is highly suitable for flood detection tasks, enhancing the efficiency of flood response strategies in the face of increasing flood frequency due to climate change.