FLOOD SEGMENTATION USING AERIAL IMAGE

Floods are one of the most common and devastating natural disasters, causing widespread damage to lives,property, and the environment. Identifying flooded areas quickly is crucial for effective disaster response and recovery.Traditional methods of flood detection are often slow, labor-intensive, and may not cover large or remote regions effectively. Thisproject aims to provide a smart and efficient solution by using aerial images taken from drones, satellites, or aircraft. Theseimages are analyzed using advanced computer programs, which apply machine learning and deep learning techniques toautomatically detect and mark flood-affected regions. These intelligent algorithms can scan and process large amounts of imagedata much faster than humans and with greater accuracy. The proposed system not only saves time but also offers real-time ornear-real-time flood mapping, helping emergency response teams, government authorities, and planners to take quick andinformed decisions. By providing accurate flood maps, this approach improves disaster management, reduces the impact of floods,and supports better planning for future emergencies.