Presenters-0589: Difference between revisions
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|CSDMS meeting webinar registration URL= | |CSDMS meeting webinar registration URL=REGISTRATION NOW CLOSED | ||
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We will provide example datasets and models, but participants will also be encouraged to bring their own imagery sets. That way, participants will have time to familiarize themselves with the burgeoning Doodleverse tools (https://github.com/Doodleverse) in between classes on their own data. | We will provide example datasets and models, but participants will also be encouraged to bring their own imagery sets. That way, participants will have time to familiarize themselves with the burgeoning Doodleverse tools (https://github.com/Doodleverse) in between classes on their own data. | ||
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|Working group member=Marine Working Group, Terrestrial Working Group, Coastal Working Group, Education and Knowledge Transfer (EKT) Working Group, Cyberinformatics and Numerics Working Group, Hydrology | |Working group member=Marine Working Group, Terrestrial Working Group, Coastal Working Group, Education and Knowledge Transfer (EKT) Working Group, Cyberinformatics and Numerics Working Group, Hydrology Focus Research Group, Chesapeake Focus Research Group, Critical Zone Focus Research Group, Human Dimensions Focus Research Group, Geodynamics Focus Research Group, Ecosystem Dynamics Focus Research Group, Coastal Vulnerability Initiative, Continental Margin Initiative, Artificial Intelligence & Machine Learning Initiative, Modeling Platform Interoperability Initiative, River Network Modeling Initiative | ||
|CSDMS meeting additional files1=Pre Webinar Preparations.pdf | |||
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Latest revision as of 15:08, 20 June 2023
CSDMS 2022 Webinars
Machine Learning - Part 1
Abstract
Part 1 will focus on the use of Doodler (https://github.com/Doodleverse/dash_doodler), a 'human-in-the-loop' labeling tool for image segmentation (described in this paper: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021EA002085). We'll cover the two primary uses of Doodler; a) for relatively rapid image segmentation of a small set of images, and b) for making libraries of labeled imagery for training Machine Learning models to automate the process of image segmentation on larger datasets. We'd ideally like participants to label the same imagery in-class so we can discuss image interpretation and label agreement. This may even result in a publishable dataset; participants would receive co-authorship and could opt-in/out. We will provide example datasets and models, but participants will also be encouraged to bring their own imagery sets. That way, participants will have time to familiarize themselves with the burgeoning Doodleverse tools (https://github.com/Doodleverse) in between classes on their own data.
Please acknowledge the original contributors when you are using this material. If there are any copyright issues, please let us know (CSDMSweb@colorado.edu) and we will respond as soon as possible.
Of interest for:
