AI-Powered CubeSat System for Real-Time Flood Detection and Predictive Modeling

Disaster response is often delayed by communication failures and slow data relay, as seen in Hurricane Katrina (2005) and the 2010 Haiti earthquake. These events underscore the need for real-time disaster monitoring and reliable communication. CubeSats offer a scalable, cost-effective solution by rapidly collecting flood data. Integrated with AI, they enable autonomous flood detection, infrastructure assessment, and disaster prediction. However, challenges such as limited computational power, cybersecurity vulnerabilities, and environmental constraints must be addressed for optimal performance. This study presents three CubeSat prototypes: a commercial model with environmental sensors, an MIT CubeSat Challenge prototype using a Raspberry Pi 4B, and a custom AMSAT PCB CubeSat optimized for AI processing. A Convolutional Neural Network (CNN) enables real-time image classification and predictive analytics. Results show 92% accuracy in flood detection and significantly reduced data transmission delays compared to traditional satellites. Future CubeSats could enhance crisis monitoring, integrate blockchain-based cybersecurity measures, and refine AI models for improved efficiency.