Fast Ocean Front Detection Using Deep Learning Edge Detection Models

Small-scale ocean fronts play a significant role in absorbing the excess heat and CO2 generated by climate change, yet their dynamics are not well understood. The existing in situ and remote sensing measurements of the ocean have inadequate spatial and temporal coverage to map small-scale ocean fronts globally. In addition, conventional algorithms to generate ocean front maps are computationally intensive and require data with long lead times. We propose machine learning (ML) models to detect temperature and chlorophyll ocean fronts from unprocessed and radiometrically uncorrected satellite imagery by transfer learning from the existing models for edge detection. We use two separate datasets: one based on conventional approaches to ocean front detection, and a second based on human-annotated ground truth. The deep learning front detection approach significantly reduces the resources and overall lead times needed for detecting ocean fronts. The deep learning models are developed with resource-constrained edge compute platforms, such as CubeSats in mind, as such platforms can address the spatial and temporal coverage challenges. The highest performing models achieve the accuracies of 96% and make predictions in milliseconds using unoptimized desktop CPUs and less than 100 MB of storage; these capabilities are well suited for CubeSat deployment.