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Machine Learning [clear filter]
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

FAIRtool.org, Serverless workflows for cubesats, Geoweaver ML workflow management, 3D printed weather stations
Come hear what ESIP Lab PIs have built over the past year. Speakers include:

Abdullah Alowairdhi: FAIRTool Project Update
Ziheng Sun: Geoweaver Project
Amanda Tan: Serverless Workflow Project
Agbeli Ameko: 3D-Printed Weather Stations

Presentations:
https://doi.org/10.6084/m9.figshare.11626284.v1

View Recording: https://youtu.be/vrRwEQRAIZ4

Takeaways



Speakers
avatar for Amanda Tan

Amanda Tan

Data Scientist, University of Washington
Cloud computing, distributed systems
avatar for Abdullah Alowairdhi

Abdullah Alowairdhi

PhD Candedate, U of Idaho
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 Annie Burgess

Annie Burgess

ESIP Lab Director, ESIP


Wednesday January 8, 2020 11:00am - 12:30pm EST
Salon A-C
  Salon A-C, Breakout
  • Skill Level Skim the Surface, Jump In
  • Keywords Cloud Computing, Machine Learning
  • Collaboration Area Tags Science Software
  • Remote Participation Link: https://global.gotomeeting.com/join/195545333
  • Remote Participation Phone #: (571) 317-3129
  • Remote Participation Access Code 195-545-333
  • Additional Phone #'s: Australia: +61 2 8355 1050 Austria: +43 7 2081 5427 Belgium: +32 28 93 7018 Canada: +1 (647) 497-9391 Denmark: +45 32 72 03 82 Finland: +358 923 17 0568 France: +33 170 950 594 Germany: +49 692 5736 7317 Ireland: +353 15 360 728 Italy: +39 0 230 57 81 42 Netherlands: +31 207 941 377 New Zealand: +64 9 280 6302 Norway: +47 21 93 37 51 Spain: +34 912 71 8491 Sweden: +46 853 527 836 Switzerland: +41 225 4599 78 United Kingdom: +44 330 221 0088

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