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Summer ESIP 2020


We’re passionate about Earth Science Data and helping to solve the challenges in that field. At this year’s ESIP Summer Meeting, we have four E84 team members speaking and moderating across multiple sessions.

Here’s where you can find us if you want to chat!

STAC and Sentinel-2 Cloud-Optimized GeoTIFFs

  • When? July 14 @ 2:00 PM – 3:30 PM EST
  • Who? Matthew Hanson (Moderator)
  • What? Panel

SpatioTemporal Asset Catalogs (STAC) is an emerging community standard that aims to improve geospatial interoperability via standardized search and discovery. A brief overview of STAC will be provided along with how STAC can be used to enable powerful scalable workflows that keep track of data provenance. Additionally, Element 84 worked on creating a new AWS Public Dataset for Sentinel-2 surface reflectance data available in Cloud-Optimized GeoTIFF format. Attendees will be shown how to use open-source tools to discover Sentinel-2 data for any location and date range and how chunks of data data can be efficiently read and fetched for the resulting scenes.


Cloud-Optimized Data

  • When? July 14 @ 4:00 PM – 5:30 PM EST
  • Who? Patrick Quinn, Trevor Skaggs
  • What? Panel

This session will seek to summarize our current understanding of best practices for creating, using and cataloging cloud-optimized data. Speakers are sought to address any part of this research space, including metadata handling, chunking, compression, filtering, sparse datasets, tooling, libraries, workflows, and cloud experiences with formats like Cloud-Optimized GeoTIFF (COG), Parquet, Zarr, TileDB, NetCDF4 and HDF5.


Public-Private Partnerships in the Age of the COVID-19 Global Pandemic – Part 2

  • When? July 16 @ 4:00 PM – 5:30 PM
  • Who? Dan Pilone
  • What? Panel

COVID-19 has turned our worlds upside down, impacting our lives, our businesses and the economy in significant ways. Emergency response has had to adapt rapidly to an unseen and deadly virus. Decisions are being driven, mostly by data and that data needs to be trusted. This session will focus on identifying how the disaster response community trusts data. The All Hazards Consortium and ESIP have come up with Operational Readiness Levels (ORL) for trusted data and this is being embraced by private and public sector organizations and agencies.

We will focus our meeting on how public-private partnerships accelerate decision making and how the identification of trusted data, and the sharing of that data, leads to rapid data-driven decision making across multiple sectors. This can save lives. We are building on the ESIP winter meeting theme of “Putting Data to Work” and our session mapping data ORLs to FEMA Community Lifelines. In this session we will continue to encourage data producers, providers and users to work with us in the Disaster Lifecyle Cluster so we can refine our definitions of trusted data and ORLs.

Part 1 invites presentations on COVID-19 trusted data access and sharing from people who can discuss the challenges and goals for data sharing.

Part 2 explores challenges in providing trusted data and technology throughout the COVID-19 response teams from decision makers to community engagement.


Organizational Strategies, Standards, and Policies for Machine Learning – Charting the Next Step of ESIP Machine Learning Cluster

  • When? July 21 @ 2:00 PM – 3:30 PM EST
  • Who? Dan Pilone
  • What? Panel

Since 2010s, the Earth science community has seen rapid growth in the interests and practice of adopting machine learning in research and discovery. The fast accumulating Earth science data and mature cloud technology have accelerated the growth of machine learning applications in Earth sciences in both academia and government agencies as well as industries.

Until recently, there is still a lack of systematic strategies and community-driven standards to steward and coordinate machine learning applications for Earth sciences. Recently, NOAA Research Council released its strategy for artificial intelligence outlining its vision to “dramatically expand the application of artificial intelligence in every NOAA mission area by improving the efficiency, effectiveness, and coordination of AI development and usage across the agency.” Similarly, other government agencies and private sectors have also formulated their own vision and practice for adopting ML/AI to advance their missions and put data to work.

In this session, we invite representatives from various government agencies and organizations to share their perspectives on adopting machine learning for Earth sciences (ML4ES). The session will have a set of brief presentations from speakers to outline the current landscape of ML4ES and followed by a panel discussion on how the ESIP community can contribute to and shape this landscape.

This stand-alone session will also serve to inform a follow on session, which is a conversation about possible Cluster activities and outputs.