Spatio-Temporal Consistency as a Means to Identify Unlabeled Objects in a Continuous Data Field
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Mesoscale ocean eddies are a critical component of the Earth System as they dominate the ocean's kinetic energy and impact the global distribution of oceanic heat, salinity, momentum, and nutrients. Therefore, accurately representing these dynamic features is critical for our planet's sustainability. The majority of methods that identify eddies from satellite observations analyze the data in a frame-by-frame basis despite the fact that eddies are dynamic objects that propagate across space and time. We introduce the notion of spatio-temporal consistency to identify eddies in a continuous spatio-temporal field, to simultaneously ensure that the features detected are both spatially and temporally consistent. Our spatio-temporal consistency approach allows us to remove most of the expert criteria used in traditional methods to reduce false negatives. The removal of arbitrary heuristics enables us to render more complete eddy dynamics by identifying smaller and longer lived eddies compared to existing methods.
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The use of physical principles to constrain spatiotemporal pattern mining algorithms has been explored for finding ocean eddies from satellite data.
“For example, the use of physical principles to constrain spatiotemporal pattern mining algorithms has been explored in [81], [82] for finding ocean eddies from satellite data.”
From Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2017) · cites this paper as [82]
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EddyScan: A physically consistent ocean eddy monitoring application
Faghmous, J. H., Styles, L., Mithal, V., Boriah, S., Liess, S., Kumar, V., Vikebo, F., & Mesquita, M. D. S. (2012) · 2012 Conference on Intelligent Data Understanding · doi:10.1109/cidu.2012.6382189 · Cited by 29 · see citing works
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Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2720168 · Cited by 1,752 · see citing works
Self-citationTheory-Guided Data Science for Climate Change
Faghmous, J. H., Banerjee, A., Shekhar, S., Steinbach, M., Kumar, V., Ganguly, A. R., & Samatova, N. (2014) · Computer · doi:10.1109/mc.2014.335 · Cited by 42 · see citing works
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- Authors
- Faghmous, J., Nguyen, H., Le, M., & Kumar, V.
- Affiliations
- University of Minnesota (US)
- Rochester Institute of Technology (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2014-06-20
- Volume
- 28
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2159287270
- Cited by
- 7
- References
- 1
- Citations by year
- 2020: 2 · 2019: 2 · 2017: 1 · 2015: 1 · 2014: 1
- FWCI
- 5.75
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Science Foundation (1355072, 1029711)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Arctic and Antarctic ice dynamics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Keywords
- ocean eddies, eddy detection, spatiotemporal consistency, mesoscale ocean dynamics, false negative reduction, eddy propagation
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