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Tuesday, January 7
 

11:00am EST

Interoperability of geospatial data with STAC
SpatioTemporal Asset Catalogs is an emerging specification of a common metadata model for geospatial data, and a way to make data catalogs indexable and searchable. We have already seen STAC being adopted for both public data and commercial data. Catalogs exist for several AWS Public Datasets, Landsat Collection 2 data will be published along with STAC metadata, and communities like Pangeo are using STAC to organize data repositories in a scalable way. Commercial companies like Planet and Digital Globe are starting to publish STAC metadata for some of their catalogs. Session talks may cover overviews of the STAC, software projects utilizing STAC, and use cases of STAC in organizations. How to Prepare for this Session: See https://stacspec.org/.

View Recording:https://youtu.be/BdZbJLQSNFE.

Takeaways


Speakers
avatar for Dan Pilone

Dan Pilone

Chief Technologist, Element 84
Dan Pilone is CEO/CTO of Element 84 and oversees the architecture, design, and development of Element 84's projects including supporting NASA, the USGS, Stanford University School of Medicine, and commercial clients. He has supported NASA's Earth Observing System for nearly 13 years... Read More →
avatar for Aimee Barciauskas

Aimee Barciauskas

Data engineer, Development Seed
MH

Matthew Hanson

Element 84
STAC


Tuesday January 7, 2020 11:00am - 12:30pm EST
White Flint
  White Flint, Breakout

4:00pm EST

Defining the Bull's Eye of Sample Metadata
In recent years, the integration of physical collections and samples into digital data infrastructure has received increased attention in the context of Open Science and FAIR research results. In order to support open, transparent, and reproducible science, physical samples need to be uniquely identified, findable in online catalogues, well documented, and linked to related data, publications, people, and other relevant digital information. Substantial progress has been made through wide-spread implementation of the IGSN as a persistent unique identifier. What is missing is the development and implementation of protocols and best practices for sample metadata. Effort to do this have shown that it is impossible to develop a common vocabulary that describes all samples collected: one size does not fit all and each domain e.g. soil scientists, volcanologists, cosmochemists, paleoclimate scientists, and granite researchers – to name a few examples - all have their own vocabularies. Yet there is a minimum set of attributes that are common to all samples, the ‘Bull’s Eye of sample metadata’. This session invites participants from all walks of earth and environmental science to help define what is the minimum set of attributes needed to describe physical samples that are at the heart of much of Earth and environmental research.

How to Prepare for this Session:
Participations should come with a list of the mimimum metadata requirements for their institutions or domains.  They should be prepared to give a brief introduction to their needs.

Session Agenda:
  1. Introduction to the issue
  2. Review of existing examples and discussion of the limitations
  3. Discuss minimal requirements; propose changes/addition
  4. Summarize outcomes and discuss next steps
Google doc with the current metadata list and proposed changes

Presentations: ​​​​

View Recording: https://youtu.be/bxhTmrNqkCA

Takeaways

Speakers
avatar for Lesley Wyborn

Lesley Wyborn

Adjunct Fellow, Australian National University
avatar for Kerstin Lehnert

Kerstin Lehnert

President, IGSN e.V.
Kerstin Lehnert is Senior Research Scientist at the Lamont-Doherty Earth Observatory of Columbia University and Director of EarthChem, the System for Earth Sample Registration, and the Astromaterials Data System. Kerstin holds a Ph.D in Petrology from the University of Freiburg in... Read More →


Tuesday January 7, 2020 4:00pm - 5:30pm EST
Linden Oak
  Linden Oak, Working Session
 
Wednesday, January 8
 

2:00pm EST

AI for Augmenting Geospatial Information Discovery
Thanks to the rapid developments of hardware and computer science, we have seen a lot of exciting breakthroughs in self driving, voice recognition, street view recognition, cancer detection, check deposit, etc. Sooner or later the fire of AI will burn in Earth science field. Scientists need high-level automation to discover in-time accurate geospatial information from big amount of Earth observations, but few of the existing algorithms can ideally solve the sophisticated problems within automation. However, nowadays the transition from manual to automatic is actually undergoing gradually, a bit by a bit. Many early-bird researchers have started to transplant the AI theory and algorithms from computer science to GIScience, and a number of promising results have been achieved. In this session, we will invite speakers to talk about their experiences of using AI in geospatial information (GI) discovery. We will discuss all aspects of "AI for GI" such as the algorithms, technical frameworks, used tools & libraries, and model evaluation in various individual use case scenarios. How to Prepare for this Session: https://esip.figshare.com/articles/Geoweaver_for_Better_Deep_Learning_A_Review_of_Cyberinfrastructure/9037091
https://esip.figshare.com/articles/Some_Basics_of_Deep_Learning_in_Agriculture/7631615

Presentations:
https://doi.org/10.6084/m9.figshare.11626299.v1

View Recording: https://youtu.be/W0q8WiMw9Hs

Takeaways
  • There is a significant uptake of machine learning/artificial intelligence for earth science applications in the recent decade;
  • The challenge of machine learning applications for earth science domain includes:
    • the quality and availability of training data sets;
    • Requires a team with diverse skill background to implement the application
    • Need better understanding of the underlying mechanism of ML/AI models
  • There are many promising applications/ developments on streamlining the process and application of machine learning applications for different sectors of the society (weather monitoring, emergency responses, social good)



Speakers
avatar for Yuhan (Douglas) Rao

Yuhan (Douglas) Rao

Postdoctoral Research Scholar, CISESS/NCICS/NCSU
avatar for Aimee Barciauskas

Aimee Barciauskas

Data engineer, Development Seed
avatar for Annie Burgess

Annie Burgess

ESIP Lab Director, ESIP
avatar for Rahul Ramachandran

Rahul Ramachandran

Project Manager, Sr. Research Scientist, NASA
avatar for Ziheng Sun

Ziheng Sun

Research Assistant Professor, George Mason University
My research interests are mainly on geospatial cyberinfrastructure and agricultural remote sensing.


Wednesday January 8, 2020 2:00pm - 3:30pm EST
Salon A-C
  Salon A-C, Breakout

4:00pm EST

Citizen Science Data in Earth Science: Challenges and Opportunities
Citizen science is scientific data collection and research performed primarily or in part by non-professional and amateur scientists. Citizen science data has been used in a variety of the physical sciences, including physics, ecology, biology, and water quality. As volunteer-contributed datasets continue to grow, they represent a unique opportunity to collect and analyze earth-science data on spatial and temporal scales impossible to achieve by individual researchers. This session will explore the ways open citizen science data sets can be used in earth science research and some of the associated challenges and opportunities for the ESIP community to use and partner with citizen science organizations.

Speakers:View Recording: https://youtu.be/jTNgWZI6Cik

Takeaways


How to Prepare for this Session: https://www.nationalgeographic.org/encyclopedia/citizen-science/
http://www.earthsciweek.org/citizen-science

Speakers
avatar for Alexis Garretson

Alexis Garretson

Community Fellow, ESIP
avatar for Kelsey Breseman

Kelsey Breseman

Archiving Program Lead, Environmental Data & Governance Initiative
Governmental accountability around public data & the environment. Decentralized web. Intersection of tech & ethics & civics.


Wednesday January 8, 2020 4:00pm - 5:30pm EST
Linden Oak
  Linden Oak, Breakout

4:00pm EST

Planning for new Agriculture and Climate Cluster focus area on automated agriculture with AI
The Agriculture and Climate (ACC) Cluster will host a planning session for a new focus area on automated agriculture and AI (""Agro-AI""). Some initial ideas on possible activities in this space were presented at the ACC October 2019 telecon, including those related to the “Data-to-Decisions” ESIP Lab project (https://www.esipfed.org/wp-content/uploads/2018/07/Wee.pdf). Currently, there are many initiatives and funding opportunities for automated agriculture with AI. The National Science Foundation, e.g., recently announced a program aimed at significantly advancing research in AI (https://www.nsf.gov/news/news_summ.jsp?cntn_id=299329&org=NSF&from=news), including, in its initial set of high-priority areas, “AI-Driven Innovation in Agriculture and the Food System.”
Among the topics for discussion in this planning session will be related proposal opportunities and sponsoring an ACC breakout session on agriculture and AI at the ESIP 2020 Summer Meeting. How to Prepare for this Session: TBD; there will be an intro presentation, prior to the group discussion. This presentation may be made available ahead of the meeting in the scheduled session page.

Presentations:

View Recording: https://youtu.be/GhnSINRFNBg

Takeaways
  • Next step 1: Conduct a survey of available dashboards, existing data, ML use cases, existing APIs
  • Next step 2: Decide on an example question for a use case
  • Next step 3: Define and survey potential users



Speakers
AA

Arif Albayrak

Senior Software Engineer, ADNET (GESDISC)
avatar for Bill Teng

Bill Teng

NASA GES DISC (ADNET)


Wednesday January 8, 2020 4:00pm - 5:30pm EST
Salon A-C
  Salon A-C, Business Meeting
 
Thursday, January 9
 

10:15am EST

Mapping Data & Operational Readiness Levels (ORLs) to Community Lifelines
Approach: The Disaster Lifecycle Cluster has seen great success in its efforts to put Federated arms around “trusted data for decision makers” as a way to accelerate situational awareness and decision-making. By identifying trust levels for data. This session will build upon the Summer meeting and align perfectly with the overall ESIP theme of: Data to Action: Increasing the Use and Value of Earth Science Data and Information.

The ESIP Disaster Lifecycle Cluster has evolved into one of the most operationally active clusters in the Federation with a thirst for applying datasets to decision-making environments while building trust levels that manifest themselves as ORLs. Duke Energy, All Hazards Consortium’s Sensitive Information Sharing environment (SISE), DHS and FEMA are all increasing their interest in ORLs with their sights set on implementing them in the near future. Data is available everywhere and more of it is on the way. Trusted data is available some places and can help decision makers such as utilities make 30-second decisions that can save lives, property and get the lights back on sooner, saving millions of dollars.

This session will provide the venue to discuss emerging projects from NASA’s Applied Sciences Division (A.37), Initiatives at JPL and Federal Agency data portal access that can accelerate decision making today and in the future. We will also discuss drone data and European satellite data that is available for access and use when disasters threaten. Come and join us, the data you have may just save a life.

Agenda:
  1. Greg McShane, DHS CISA - The Critical Nature of the Public-Private Trusted Information Sharing Paradigm (10 min) Presented by Tom Moran, All Hazards Consortium Executive Director
  2. Dave Jones, StormCenter/GeoCollaborate - The status of ORLs, where we are, ESIP Announcement at GEO in Australia, AHC SISE, Next Steps (10 min)
  3. Maggi Glassco, NASA Disasters Program, JPL - New Applied Sciences Disasters Projects, Possible Lifeline Support Information Sources in the Future (10 min)
  4. Bob Chen/Bob Downs, Columbia Univ./SEDAC/CIESIN - Specific Global and Local Population Data for Community Lifeline Decision Making (10 min)
  5. Discussion/Q&A Period (40 min)

Presentations

View Recording: https://youtu.be/gJ93R6SlMkM

Key Takeaways for this Session: 
  1. Through the All Hazards Consortium, a new research institute will begin to help bring candidate research products into operations. An imagery committee, consisting of private and research members under SISE, will identify and evaluate use-case driven candidate imagery data within the ORL context using Geo-Collaborate.
  2. NASA grant opportunities within the disasters program requires co-funding by end user partners to guide usage needs and adoption (using ARL success criteria). This should increase adoption of NASA funded ASP project data and/or services. The cluster would like to work with NASA ASP as a testbed for funded projects to connect to additional user communities.
  3. We discussed the need / value of population data (current and predictions on affected populations) for preparedness activities and emergency response. We would like to leverage additional data services from SEDAC to test with operational decision makers. 


Speakers
avatar for Dave Jones

Dave Jones

StormCenter Communications, StormCenter Communications
Real-time data access, sharing and collaboration across multiple platforms. Collaborative Common Operating Pictures, Decision Making, Situational Awareness, connecting disparate mapping systems to share data, cross-product data sharing and collaboration. SBIR Phase III status with... Read More →
avatar for Karen Moe

Karen Moe

NASA Goddard Emeritus
ESIP Disasters Lifecycle cluster co-chair with Dave Jones/StormCenter IncManaging an air quality monitoring project for my town just outside of Washington DC and looking for free software!! Enjoying citizen science roles in environmental monitoring and sustainable practices in my... Read More →


Thursday January 9, 2020 10:15am - 11:45am EST
Salon A-C
  Salon A-C, Breakout

12:00pm EST

Fire effects on soil morphology across time scales: Data needs for near- and long-term land and hazard management
Fire impacts soil hydrology and biogeochemistry at both near (hours to days) and long (decades to centuries) time scales. Burns, especially in soils with high organic carbon stocks like peatlands, induce a loss of absolute soil carbon stock. Additionally, fire can alter the chemical makeup of the organic matter, potentially making it more resistant to decomposition. On the shorter timescales, fire can also change the water repellent properties or hydrophobicity of the soil, leading to an increased risk of debris flows and floods.

In this session, we will focus on the varying data needs for assessing the effects of burns across time scales, from informing emergency response managers in the immediate post-burn days, to monitoring post-burn recovery, to managing carbon in a landscape decades out.

Speaker abstracts (in order of presentation):

James MacKinnon (NASA GSFC)
Machine learning methods for detecting wildfires 

This talk shows the innovative use of deep neural networks, a type of machine learning, to detect wildfires in MODIS multispectral data. This effort attained a very high classification accuracy showing that neural networks could be useful in a scientific context, especially when dealing with sparse events such as fire anomalies. Furthermore, we laid the groundwork to continue beyond binary fire classification towards being able to detect the "state," or intensity of the fire, eventually allowing for more accurate fire modeling. With this knowledge, we developed software to enable neural networks to run on even the typically compute-limited spaceflight-rated computers, and tested it by building a drone payload equipped with a flight computer analog and flew it over controlled burns to prove its efficacy.

Kathe Todd-Brown (U. FL Gainesville)
An overview of effects of fire on ecosystems

Fire is a defining characteristic of many ecosystems worldwide, and, as the climate warms, both fire frequency and severity are expected to increase. In addition to the effects of smoke on the climate and human health, there are less apparent effects of fire on the terrestrial ecosystem. From alterations in the local soil properties to changes in the carbon budget as organic carbon is combusted into CO2 and pyrogenic carbon, fire is deeply impactful to the local landscape. The long-term climate implication of fire on the terrestrial carbon budget is a tension between carbon lost to the atmosphere as carbon dioxide and sequestered in the soil as recalcitrant pyrogenic carbon. Here we present a new model to simulate the interaction between ecosystem growth, decomposition, and fire on carbon dynamics. We find that the carbon lost to burned carbon dioxide will always be recovered, if there is any recalcitrant pyrogenic carbon generated by the fires. The time scale of this recovery, however, is highly variable and often not relevant to land managers. This model highlights key data gaps at the annual and decadal time scales. Quantifying and predicting the loss of soil, litter, and vegetation carbon in an individual fire event is a key unknown. Relatedly, the amount of pyrogenic carbon generated by fire events is another near-term data needed to better constrain this model. Finally, on the longer time scales, the degree of recalcitrancy of pyrogenic carbon is a critical unknown.

Daniel Fuka (VA Tech)

Rapidly improving the spatial representation of soil properties using topographically derived initialization with a proposed workflow for new data integration
Topography exerts critical controls on many hydrologic, geomorphologic, biophysical, and forest fire processes. However, in modeling these systems, the current use of topographic data neglects opportunities to account for topographic controls on processes such as soil genesis, soil moisture distributions, and hydrological response; all factors that significantly characterize the post-fire effects and potential risks of the new landscape. In this presentation, we demonstrate a workflow that takes advantage of data brokering to combine the most recent topographic data and best available soil maps to increase the resolution and representational accuracy of spatial soil morphologic and hydrologic attributes: texture, depth, saturated conductivity, bulk density, porosity, and the water capacities at field and wilting point tensions. We show several proofs of concept and initial performance test the values of the topographically adjusted soil parameters against those from the NRCS SSURGO (Soil Survey Geographic database). Finally, we pose the potential for a quickly configurable opensource data brokering system (NSF BALTO) to be used to make available the most recently updated topographic and soils characteristics, so this workflow can rapidly re-characterize and increase the resolution of post-fire landscapes.

Dalia Kirschbaum (NASA GSFC)
Towards characterization of global post-fire debris flow hazard

Post-fire debris flows commonly occur in the western United States, but the extent of this hazard is little known in other regions. These events occur when rain falls on the ground with little vegetative cover and hydrophobic soils—two common side effects of wildfire. The storms that trigger post-fire debris flows are typically high-intensity, short-duration events. Thus, a first step towards global modeling of this hazard is to evaluate the ability of GPM IMERG and other global precipitation data to detect these storms. The second step is to determine the effectiveness of MCD64 and other globally available predictors in identifying locations susceptible to debris flows. Finally, rainfall and other variables can be combined into a single global model of post-fire debris flow occurrence. This research can show both where post-fire debris flows are currently most probable, as well as where the historical impact has been greatest.

How to Prepare for this Session:

Presentations

View Recording: https://youtu.be/I89om-kBYB0

Takeaways
  • Modeling and detecting fires and fire impacts is changing (e.g. neural networks, carbon modeling) and needs to continue to improve
  • There are many data needs to be able to operationalize post-fire debris flow and soil modeling
  • Fires severely change ecosystems and soils and we do not really understand the exact changes yet, need more research in this area


Speakers
KT

Kathe Todd-Brown

University of Florida Gainesville
DF

Dan Fuka

Virginia Tech
avatar for Bill Teng

Bill Teng

NASA GES DISC (ADNET)


Thursday January 9, 2020 12:00pm - 1:30pm EST
Salon A-C
  Salon A-C, Breakout

12:00pm EST

Datacubes for Analysis-Ready Data: Standards & State of the Art
This workshop session will follow up on the OGC Coverage Analytics sprint, focusing specifically on advanced services for spatio-temporal datacubes. In the Earth sciences datacubes are accepted as an enabling paradigm for offering massive spatio-temporal Earth data analysis-ready, more generally: easing access, extraction, analysis, and fusion. Also, datacubes homogenizes APIs across dimensions, allowing unified wrangling of 1-D sensor data, 2-D imagery, 3-D x/y/t image timeseries and x/y/z geophysics voxel data, and 4-D x/y/z/t climate and weather data.
Based on the OGC datacube reference implementation we introduce datacube concepts, state of standardization, and real-life 2D, 3D, and 4D examples utilizing services from three continents. Ample time will be available for discussion, and Internet-connected participants will be able to replay and modify many of the examples shown. Further, key datacube activities worldwide, within and beyond Earth sciences, will be related to.
Session outcomes could take a number of forms: ideas and issues for OGC, ISO, or ESIP to consider; example use cases; challenges not yet addressed sufficiently, and entirely novel use cases; work and collaboration plans for future ESIP work. Outcomes of the session will be reported at the next OGC TC meeting's Big Data and Coverage sessions. How to Prepare for this Session: Introductory and advanced material is available from http://myogc.org/go/coveragesDWG

Presentations
https://doi.org/10.6084/m9.figshare.11562552.v1

View Recording: https://youtu.be/82WG7soc5bk

Takeaways
  • Abstract coverage construct defines the base which can be filled up with a coverage implementation schema. Important as previously implementation wasn’t interoperable with different servers and clients. 
  • Have embedded the coordinate system retrieved from sensors reporting in real time into their xml schema to be able to integrate the sensor data into the broader system. Can deliver the data in addition to GML but JSON, and RDF which could be used to link into semantic web tech. 
  • Principle is send HTTP url-encoded query to server and get some results that are extracted from datacube, e.g., sources from many hyperspectral images.

Speakers

Thursday January 9, 2020 12:00pm - 1:30pm EST
White Flint