Event-driven nearshore and shoreline coastline detection on SpiNNaker neuromorphic hardware

Abstract Coastline detection is vital for coastal management, involving frequent observation and assessment to understand coastal dynamics and inform decisions on environmental protection. Continuous streaming of high-resolution images demands robust data processing and storage solutions to manage large datasets efficiently, posing challenges that require innovative solutions for real-time analysis and meaningful insights extraction. This work leverages low-latency event-based vision sensors coupled with neuromorphic hardware in an attempt to decrease a two-fold challenge, reducing the computational burden to ∼0.375 mW whilst obtaining a coastline detection map in as little as 20 ms. The proposed Spiking Neural Network runs on the SpiNNaker neuromorphic platform using a total of 18 040 neurons reaching 98.33% accuracy. The model has been characterised and evaluated by computing the accuracy of Intersection over Union scores over the ground truth of a real-world coastline dataset across different time windows. The system’s robustness was further assessed by evaluating its ability to avoid coastline detection in non-coastline profiles and funny shapes, achieving a success rate of 97.3%.