Multi-Contextual Learning : Analyzing Melt Over the Greenland Ice Sheet

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dc.contributor.authorKulkarni, Chhaya
dc.contributor.authorJaneja, Vandana
dc.contributor.authorSchlegel, Nicole-Jeanne
dc.date.accessioned2024-08-27T20:38:02Z
dc.date.available2024-08-27T20:38:02Z
dc.date.issued2023-07
dc.descriptionIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 16-21 July 2023, Pasadena, CA, USA
dc.description.abstractClimate data related to polar regions has been extensively studied through model simulations, remote sensed images, and statistical analysis. Knowledge discovery tasks, if applied to the Arctic data, can capture the associations, anomalies, and interesting patterns and trends that exist in the data to inform the polar scientists. This can help improve the certainty in the models with better local information. In this study, we propose a framework that develops multi-contextual spatiotemporal neighborhoods, addressing spatial autocorrelation and heterogeneity taking into account data from multiple views or contexts, to identify local homogeneous regions across the entire Greenland Ice Sheet. Using these neighborhoods, we study how local regions in the Greenland ice sheet evolve over time. This framework can expand to include additional contextual variables and help polar scientists study local ice surface melt phenomena across multiple contexts. Novelty of our approach lies in its ability to consider the Greenland data from spatial, temporal and semantic contexts that allows us to build a comprehensive understanding of a specific phenomenon such as snowmelt.
dc.description.sponsorshipThis work is funded by the National Science Foundation Award #2118285 (iHARP)
dc.description.urihttps://ieeexplore.ieee.org/document/10281954/
dc.format.extent4 pages
dc.genreconference papers and proceedings
dc.genrepostprints
dc.identifierdoi:10.13016/m2nuha-wywh
dc.identifier.citationKulkarni, Chhaya, Vandana Janeja, and Nicole-Jeanne Schlegel. “Multi-Contextual Learning : Analyzing Melt Over the Greenland Ice Sheet.” In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 6736–39, 2023. https://doi.org/10.1109/IGARSS52108.2023.10281954.
dc.identifier.urihttps://doi.org/10.1109/IGARSS52108.2023.10281954
dc.identifier.urihttp://hdl.handle.net/11603/35806
dc.language.isoen_US
dc.publisherIEEE
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Information Systems Department
dc.rights© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectAnalytical models
dc.subjectSpatial databases
dc.subjectSemantics
dc.subjectStatistical analysis
dc.subjectData models
dc.subjectMarket research
dc.subjectspatiotemporal data
dc.subjectmulti-contextual learning
dc.subjectSpatiotemporal phenomena
dc.subjectneighborhood discovery
dc.subjectGreenland ice sheet
dc.titleMulti-Contextual Learning : Analyzing Melt Over the Greenland Ice Sheet
dc.typeText
dcterms.creatorhttps://orcid.org/0009-0006-1164-003X
dcterms.creatorhttps://orcid.org/0000-0003-0130-6135

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