Meeting summaries and links in reverse chronological order.
Abstract: Satellite observations provide invaluable data across different spatio-temporal scales. These data enable us to build models for applications such as land cover classification, agricultural monitoring, surface water mapping, biodiversity monitoring, among others. Meanwhile, machine learning (ML) techniques can be utilized to advance these applications, and develop faster, more efficient and scalable models. These techniques learn from training datasets that are generated from image annotation or ground reference observations. However, to develop accurate ML-based models, and be able to validate their accuracy, we need to use benchmark training datasets that are representative of the diversity of the target variable, and openly accessible to all researchers and developers.
To address this requirement, Radiant Earth Foundation has established Radiant MLHub to foster sharing of geospatial training data for different thematic applications. Radiant MLHub is hosted on the cloud and users will be able to search for different training datasets, and quickly ingest them into their pipelines using an API. To increase interoperability of training datasets generated by different institutions, Radiant MLHub has adopted the SpatioTemporal Asset Catalog (STAC) as the standard for data cataloging.
In this presentation, I will review the architecture of Radiant MLHub, its API access and the STAC definition for training data. Next, I will present two applications on using ML models for LC classification from multi-spectral data and surface water detection from Synthetic Aperture (SAR) data.
Speaker bio: Hamed Alemohammad is the Chief Data Scientist at Radiant Earth Foundation, leading the development of Radiant MLHub as an opensource cloud native commons for Machine Learning applications using Earth Observations. He has extensive expertise in machine learning, remote sensing and imagery techniques particularly in developing new algorithms for multi-spectral satellite and airborne based observations. He also serves as an elected member of the American Geophysical Union’s technical committee on remote sensing. Prior to joining Radiant Earth, he was a Research Scientist at Columbia University. Hamed received his PhD in Civil and Environmental Engineering from MIT in 2014.
Jeff Falgout presented on Tallgrass, a new machine for AI at the USGS
Ken Bagstad presented on AI for integrated environmental modeling & forecasting (+ overview of AI for Ecosystem Services)
JC pointed out some activity on the AI/ML forum and encouraged members to post
Group leads reminded members to contribute to a spreadsheet for collecting USGS AI/ML project descriptions to communicate to USGS leadership
John Stock talked about opportunities at the USGS Innovation Center related to AI/ML, including postdoctoral positions
Pete Doucette gave a presentation “Ruminations on AI and Land Imaging,” covering some background to artificial intelligence and machine learning, relevant Landsat and Analysis Ready Data activities at the USGS, and the importance of team science
See February CDI collaboration area blog post that summarizes the call
Group leads asked members to contribute to a spreadsheet for collecting USGS AI/ML project descriptions to communicate to USGS leadership
Introduction to the group by Tim Quinn, Chief, Office of Enterprise Information
Introduction to the wiki space and AI/ML forum, JC Nelson
Comments from attendees, including mention of dl_tools toolbox for deep learning, an output from a recent CDI funded project