Fast Ocean Front Detection Using Deep Learning Edge Detection Models

Abstract

<p>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. </p>

Open-Source Software in Space Operations

“…BeaverCube-2 is a mission jointly developed by the MIT Space Telecommunications, Astronomy, and Radiation (STAR) Lab and the Northrop Grumman Corporation which aims to demonstrate the use of an AI Computational Accelerator System-on-a-Chip (SoC) on a 3U CubeSat in LEO. Beaver-Cube-2 will leverage this AI accelerator to train ML models to perform in-orbit image processing to identify clouds and ocean fronts around the Cape Hatteras region of North Carolina (Felt, 2022;. Previous work has been conducted on producing a computer vision pipeline for this task, specifically on the cloud segmentation and front identification (Felt, 2022) models.…”

“…Beaver-Cube-2 will leverage this AI accelerator to train ML models to perform in-orbit image processing to identify clouds and ocean fronts around the Cape Hatteras region of North Carolina (Felt, 2022;. Previous work has been conducted on producing a computer vision pipeline for this task, specifically on the cloud segmentation and front identification (Felt, 2022) models. However, deploying software that has only been tested using ground-based computing and imaging is a risk.…”