The use of UAV for the Identification of Flooded and Non-Flooded Region in Post Flood Scenarios using Deep Learning Techniques

Floods are a form of unforeseen disaster which may seriously compromise human health and safety and also damage infrastructure, property, and ecosystems. Post flood management is process which involves better flood image segmentation, the mapping process, assistance with relief, and evacuation these can be enhanced by the adoption of technology. In computer vision and image processing, the segmentation of image is crucial task, it is a method which determine different types of objects in an image through defining labels to each and every pixel in a target category according to its semantics. Use of Unmanned Aerial Vehicle (UAV) is more reliable way to capturing images and video of the flood regions. This study presents a comparative analysis of the achievement of DeepLabv3+ in segmentation flooded and non-flooded regions in post-flood UAV images. The models are assessed based on key performance metrics: Accuracy, Precision, Recall, Intersection over Union (IoU) and Dice Loss. Among these, DeepLabv3+ emerged as the most effective, achieving the highest accuracy (0.9494), precision (0.9565) and IoU (0.7686), indicating its superior ability to correctly segment flood images and minimize false positives. Although its recall (0.8781) was slightly lower than that of DeepLabv3, it still demonstrated robust flood area identification. The lower Dice Loss (0.1332) further corroborates its superior segmentation quality with better segmentation.