Giulia D´Angelo presents Event-driven nearshore and shoreline coastline detection on SpiNNaker neuromorphic hardware
On 2025-02-06 11:00:00 at G205, Karlovo náměstí 13, Praha 2
In this seminar, Giulia D'Angelo will present this article:
Fatahi, M., Boulet, P., & D’angelo, G. (2024). Event-driven nearshore and
shoreline coastline detection on SpiNNaker neuromorphic hardware. Neuromorphic
Computing and Engineering, 4(3), 034012.
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%.
Fatahi, M., Boulet, P., & D’angelo, G. (2024). Event-driven nearshore and
shoreline coastline detection on SpiNNaker neuromorphic hardware. Neuromorphic
Computing and Engineering, 4(3), 034012.
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%.