This talk (delivered by Dan Pilone) gives a quick overview of the results of our disaster response user needs study and demonstrate a prototype disaster response pipeline for field data management. The serverless, cloud-based pipeline combines public and private data sources with open source software.
This talk (delivered by Joe Hamman) discusses Pangeo a coordinated community effort with support from NASA, NSF, AWS, Microsoft Azure and Google Cloud, to develop interactive and reproducible open source workflows for discovery, visualization, and quantitative analysis of large datasets used for research in the Earth Sciences.
This talk demonstrates the ability to acquire, process, and analyze open broadcast science data entirely in the cloud. Leveraging the new AWS Elastic Ground Station service, we demonstrate selecting and scheduling satellite acquisitions for open science data then leveraging the scalable nature of the cloud to execute a full processing stack. This talk demonstrates the ability to acquire L0 data, leveraging open data processing algorithms from agencies like NASA and NOAA, and a highly scalable, cost effective, and secure processing architecture to produce near real-time data. By taking advantage of direct satellite acquisition and cloud scale processing, we demonstrate reduced latency from acquisition to data availability for scenarios like disaster response, tipping and cueing, and location monitoring.
This talk shares the current status of work under a NASA ACCESS grant to expand the Pangeo ecosystem's access to EOSDIS data holdings. The talk includes background information on Pangeo, STAC, Intake, and kuburnetes.
This talk discusses the mapping of existing cybersecurity controls (namely, NIST 800-53) to the cloud. Its core thesis is that the complexity and pace of iteration in the cloud necessitates a more adaptive security model. It discusses some capabilities that should be developed, and helpful services in those contexts.
This talk provides an overview on the Pangeo project and community. It discussed what Pangeo is, what its motivations are, how to deploy its stack, and how to contribute back to the community. I tried to contextualize it along a question asked by a USGS staff scientist (Aaron Friesz) regarding practical use, so focused on the details of three core packages: xarray, dask, and jupyter. Please note that many of these slides were sourced from other Pangeo talks; I attribute them to their authors on the slides themselves.
This talk discussed the arbitrary transformation and reprojection of remote sensing data -- up to and including entire product corpuses -- based on context-specific needs. The thesis was that instead of worrying about gold data formats (e.g. netCDF, HDF, etc), we should develop transformation graphs which can produce ephemeral "archives of convenience" for given use-cases. The talk also included a demo of SatCat, a proof of concept of this idea using GOES-16 full disk ABI data.
This talk surveyed the open source geospatial software ecosystem in the context of small satellite lifecycles, specifically in the context of a SaaS offering. The thesis was that, with the rapidly decreasing barrier to entry for satellite launches, universities, NGOs, and other small satellite operators would be better suited to use SaaS providers for their data lifecycles rather than investing in in-house development/operational costs. The talk includes a discussion of tools we've built and contributed to at NASA (as well as those developed by others) around data product generation, data discovery, and data visualization.
This talk (delivered by Dan Pilone) discussed our research on archives of convenience -- real-time arbitrary subsetting, transformation, and format conversion of scientific data based on use-cases. We present a proof of concept based on full disk transformations from a geostationary satellite.
This talk provided a brief overview of NASA's Earth data assets available freely to the public for scientific and commercial uses. The audience was general data science professionals without remote sensing experience.
This talk discusses our experience using AWS for Earth science web applications.