Elements: Data: HDR: Collaborative Research: Developing an On-Demand Service Module for Mining Geophysical Properties of Sea Ice from High Spatial Resolution Imagery

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Sea ice acts as both an indicator and an amplifier of climate change. At present, there are multiple sources of sea ice observations which are obtained from a variety of networks of sensors (in situ, airborne, and space-borne). By developing a smart cyberinfrastructure element for the analysis of high spatial resolution (HSR) remote sensing images over sea ice, the science community is better able to extract important geophysical parameters for climate modeling. The project contributes new domain knowledge to the sea ice community. This is accomplished by integrating HSR images that are spatiotemporally discrete to produce a more rapid and reliable identification of ice types, and by a standardized image processing that allows creating compatible sea ice products. The cyberinfrastructure module is a value-added on-demand web service that can be naturally integrated with existing infrastructure.The key objective is to develop a reliable and efficient on-demand Open Geospatial Consortium-compliant web service, which is capable of extracting accurate geographic knowledge of water, submerged ice, bare ice, melt ponds, deformed 'ridging' ice, ridge shadows, and other information from HSR images with limited human intervention. The embedded spatial-temporal analysis framework provides functions to search, explore, visualize, organize, and analyze the discrete HSR images and other related remote sensing data and field data. The project creates a data and knowledge web service for the Arctic sea ice community by integrating computer vision and machine learning algorithms, computing resources, and HSR image data and other useful datasets. The conceptual model improves data flow, so users would query data, download value-added data, and have more consistent results across various sources of information. This creates new opportunities for scientific analysis that minimizes the investment of time in processing complex and spatiotemporally-discrete HSR imagery. The project includes a strong emphasis on teaching and development of the next-generation workforce through course curricula development, involvement of graduate and undergraduate students in research, and the offering of summer workshops for K-12 teachers (funded by other agencies). The collected images and results of the image analyses will be shared with the public in a timely manner through the NSF Arctic Data Center.This award by the Office of Advanced Cyberinfrastructure is jointly supported by EarthCube and the Office of the Polar Programs Arctic Natural Sciences Program, within the NSF Directorate for Geosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.