Abstract
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. Existing in-situ and remote sensing measurements of the
ocean have inadequate spatial and temporal coverage to map small-scale
ocean fronts globally. Additionally, 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 im- agery by transfer learning
from 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 truth1. 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 like CubeSats
in mind, as such platforms can address the spatial and temporal coverage
challenges. The highest performing models achieve accuracies of 96% and
make predictions in milliseconds using unoptimized desktop CPUs and
using less than 100 MB of storage; these capabilities are well- suited
for CubeSat deployment.