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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%.