Beyond the Algorithm Challenge

Floods cause $100B+ in annual damages globally, yet critical response decisions are made with 6-72 hour delays due to ground-based satellite processing bottlenecks. This “intelligence gap” renders high-quality Earth observation data operationally useless during the critical “Golden Hour” when rapid intervention saves lives. This paper introduces NEO-FLOOD (Neuromorphic Onboard Flood-mapping), a satellite architecture that eliminates this latency by deploying autonomous AI directly in orbit. NEO-FLOOD integrates space-validated neuromorphic processors (Intel Loihi 2, BrainChip Akida) consuming just 2-5W with our novel Spike2Former-Flood algorithm—a spiking neural network optimized for real-time optical-SAR fusion onboard small satellites. Validated against IEEE GRSS 2024 datasets, our approach targets high accuracy while reducing intelligence delivery from hours to minutes. This order of magnitude latency improvement transforms satellites from passive data collectors into autonomous decision-support systems, enabling immediate flood response coordination for emergency agencies, insurers, and humanitarian organizations.