2026-09-20T02:50:25Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1446342022-08-30T03:18:22Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Cahoy, Kerri L.
Felt, Violet C.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-08-29T16:01:06Z
2022-08-29T16:01:06Z
2022-05
2022-05-27T16:19:34.847Z
https://hdl.handle.net/1721.1/144634
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 are of inadequate spatial and temporal coverage to globally map small-scale ocean fronts, and existing algorithms to generate ocean front maps are computationally intensive. We propose machine learning (ML) models to detect temperature and chlorophyll ocean fronts from unprocessed satellite imagery, significantly reducing the standard resources and computational times needed for detecting ocean fronts. These models are developed with resource-constrained satellite imaging platforms like CubeSats in mind, as such platforms are able to address the spatial and temporal coverage challenges. The highest performing models achieve accuracies of 96% and make predictions in milliseconds using less than 100 MB of storage; these capabilities are well-suited for CubeSat deployment.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Machine Learning Models for On-Orbit Detection of Temperature and Chlorophyll Ocean Fronts
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
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unknown
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<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 are of inadequate spatial and temporal coverage to globally map small-scale ocean fronts, and existing algorithms to generate ocean front maps are computationally intensive. We propose machine learning (ML) models to detect temperature and chlorophyll ocean fronts from unprocessed satellite imagery, significantly reducing the standard resources and computational times needed for detecting ocean fronts. These models are developed with resource-constrained satellite imaging platforms like CubeSats in mind, as such platforms are able to address the spatial and temporal coverage challenges. The highest performing models achieve accuracies of 96% and make predictions in milliseconds using less than 100 MB of storage; these capabilities are well-suited for CubeSat deployment.</Abstract>
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