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Tuesday, January 7
 

11:00am EST

Analytic Centers for Air Quality
The Analytic Center Framework (ACF) is a concept to support scientific investigations with a harmonized collection of data from a wide range of sources and vantage points, tools and computational resources. Four recent NASA AIST competitive awards are focused on either ACFs or components which could feed into AQ ACF's. Previous projects have developed tools and improved the accessibility and usability of data for Air Quality analysis, and have tried to address issues related to inconsistent metadata, uncertainty quantification, interoperability among tools and computing resources and visualization to aid scientific investigation or applications. The format for this meeting will be a series of brief presentati.ons by invited speakers followed by a discussion. This generally follows the panel model How to Prepare for this Session: A link to a set of pre-read materials will be provided.

View Recording: https://youtu.be/fy4eoOfSbpo.

Takeaways
  • Is there enough interest to start an Air Quality cluster? Yes!
  • Technologists and scientists should both be involved in the cluster to ensure usability through stakeholder engagement


Speakers
ML

Mike Little

ESTO, NASA
Computational Technology to support scientific investigations


Tuesday January 7, 2020 11:00am - 12:30pm EST
Glen Echo
  Glen Echo, Working Session
 
Wednesday, January 8
 

11:00am EST

Earth Observation Process and Application Discovery, Machine Learning, and Federated Cloud Analytics: Putting data to work using OGC Standards
This session provides an overview of the results from the recent OGC Research & Development initiative Testbed-15. The 9-months 5M USD initiative addressed six different topics, Earth Observation Process and Application Discovery, Machine Learning, Federated Cloud Analytics, Open Portrayal Framework, Delta Updates, and Data Centric Security. This session focuses on the results produced by the first three.

Earth Observation Process and Application Discovery developed draft specifications and models for discovery of cloud-provided process and applications. This was achieved by extending existing standards with process and application specific extensions. Now, data processing software can be made available as a service, discovered using catalog interfaces, and executed on demand by customers. This allows to locate the process execution physically close to the data and reduces data transport overheads.

The Machine Learning research developed models in the areas of earth observation data processing, image classification, feature extraction and segmentation, vector attribution, discovery and cataloguing, forest inventory management & optimization, and semantic web-link building and triple generation. Both model discovery and access took place through standardized interfaces.

The Federated Cloud Analytics research analysed how to handle data and processing capacities that are provided by individual cloud environments transparently to the user. The research included how federated membership, resource, and access policy management can be provided within a security environment, while also providing portability and interoperability to all stakeholders. Additionally, the initiative conducted a study of the application of Distributed Ledger Technologies (DLTs), and more specifically Blockchains, for managing provenance information in Federated Cloud.

The other three topics will be briefly introduced in addition. The Open Portrayal Framework provides a fully interoperable portrayal and styling suite of standards. Here, the initiative developed new OGC APIs for styles, maps, images, and tiles. Delta updates explored incremental updates and thus reduced communication payloads between clients and servers, whereas the Data Centric Security thread examined the use of encrypted container formats on standard metadata bindings. How to Prepare for this Session: Al results will be made available as public Engineering Reports that provide full details. These become stepwise available at http://docs.opengeospatial.org/per/

Presentations:
https://doi.org/10.6084/m9.figshare.11551563.v1

View Recording: https://youtu.be/ojMrcIE-SgE

Takeaways
  • OGC innovation program: Test fitness for purpose of geospatial community initiatives. TESTBED-15 concluded last November results available soon from document repository. End to end cloud pipeline for data processing and analytics. Call for TESTBED-16 due Feb 9th 2020! 1.6M in funding available. Three major threads: earth observation clouds, data integration and analytics, and modeling and packaging. 
  • Way to synergize between needs of user communities competing and collaborating projects, contributing to a more interoperable world. Provides applications, process and catalogues for data processing. 
  • Testbeds center around an exploitation/processing platform (for data with relevant applications) like an application market with cloud services. Having some trouble finding application developers. Finding web services with relevant data can be problematic.



Speakers
avatar for Ingo Simonis

Ingo Simonis

Director Innovation Programs & Science, OGC
Dr. Ingo Simonis is director of interoperability programs and science at the Open Geospatial Consortium (OGC), an international consortium of more than 525 companies, government agencies, research organizations, and universities participating in a consensus process to develop publicly... Read More →


Wednesday January 8, 2020 11:00am - 12:30pm EST
White Flint
  White Flint, Breakout

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

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

Do you have a labeling problem? Three tools for labeling data
The ESIP community and others in machine learning regularly lament the lack of labeled datasets, needed for certain classes of training algorithms. Generating accurate, useful labels is a hard problem, with no general automated solution in sight. Thus, labeling generally involves human effort, which is challenging because the volume of data needed for training can be very large.

Tools exist to help in labeling data. This session will demonstrate three labeling tools and associated processes:
  • Image Labeler, a fast, scalable cloud-based tool to facilitate the rapid development of Earth science event databases, to aid in automated ML-based image classification, Rahul Ramachandran
  • Labelimg, an open source graphical image annotation tool, https://github.com/tzutalin/labelImg, Ziheng Sun
  • Bokeh, a Python based plotting and annotation tool set for building arbitrary labeling workflows, https://bokeh.org/, Jim Bednar
Time permitting, the session will conclude with a short discussion of thoughts and tradeoffs about the tools.

This session is followed by a hands-on workshop for using Labelimg and Bokeh. Please see the session abstract for "Hands on Labeling Workshop" for information on preparing for that workshop if you are interested in participating.

Presentations
https://doi.org/10.6084/m9.figshare.11629110.v1
https://doi.org/10.6084/m9.figshare.11591739.v1

View Recording: https://youtu.be/3ufBOoD3M1E

Takeaways
  • Machine learning based classification applications require high-quality labelled data sets for both model training and evaluation. There are many existing tools for labeling images (including earth science data), but labeling tasks are very labor and time intensive.
  • If the pre-built labeling tools don’t work for your problem, Anaconda provides a general-purpose labeler-building toolkit based on Bokeh for Python users; see https://examples.pyviz.org/ml_annotators/ml_annotators.html
  • There is opportunity in combining partly automated, partly human labeling, to automate the easy cases while leaving the final call to a person. Currently not much tool support or good practices, hard to integrate.The art of avoiding extra work!

Speakers
avatar for Ziheng Sun

Ziheng Sun

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

Anne Wilson

Senior Software Engineer, Laboratory for Atmospheric and Space Physics
avatar for Yuhan (Douglas) Rao

Yuhan (Douglas) Rao

Postdoctoral Research Scholar, CISESS/NCICS/NCSU


Thursday January 9, 2020 10:15am - 11:45am EST
Glen Echo
  Glen Echo, Breakout

12:00pm EST

Hands-on labeling workshop
Intended as a follow on to the "Do You Have a Labeling Problem?" session and to get your feet wet, this working session is for people to experiment with two of the tools presented in that session, Labelimg and Bokeh. Presenters will provide some sample data for participants to work with. Attendees can also bring some of their own data to work with in the time remaining after the planned activities.

It would be best for workshop participants to preinstall Labelimg before coming to the session.   Regarding Bokeh, Anaconda is providing 25 accounts for workshop participants. (Thank you, Jim and Anaconda!).  Installing Bokeh is also an option.  Links for getting these tools are:
  • Labelimg via https://github.com/tzutalin/labelImg#installation
  • Bokeh as part of the HoloViz suite via http://holoviz.org/installation.html

Presentations

View Recording: https://youtu.be/y8NqTLgT8Ao

Takeaways


Speakers
avatar for Ziheng Sun

Ziheng Sun

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

Anne Wilson

Senior Software Engineer, Laboratory for Atmospheric and Space Physics
avatar for Yuhan (Douglas) Rao

Yuhan (Douglas) Rao

Postdoctoral Research Scholar, CISESS/NCICS/NCSU


Thursday January 9, 2020 12:00pm - 1:30pm EST
Glen Echo
  Glen Echo, Workshop

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