Meeting summaries and links in reverse chronological order.
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Abstract: This presentation provides an overview of how we use a recurrent autoencoder neural network to encode sequential Californiagolden eagle telemetry data. The encoding is followed by an unsupervisedclustering technique, Deep Embedded Clustering (DEC), to iteratively clusterthe data into a chosen number of behavior classes. We apply the method tosimulated movement data sets and telemetry data for a Golden Eagle. The DECachieves better unsupervised clustering accuracy scores for the simulated datasets as compared to the baseline K-means clustering result.
Speaker Bio: Natalya Rapstine is a Computer Scientist at Advanced ResearchComputing group, specializing in computational data science, statistics, andmachine learning applications for advancement of science at the U.S. GeologicalSurvey. She received a M.S. in Statistics from Colorado School of Mines.
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.
Abstract: The expense and logistics of monitoring streamflow (e.g. stage and discharge) and nearshore waves (e.g. height and period) using in situ instrumentation such as current meters, bubblers, pressure transducers, etc, limits the extent to which such important basic information can be acquired. Machine learning might offer a solution, if such information can be obtained remotely from time-lapse imagery using inexpensive consumable camera installations. To that end, I describe a proof-of-concept study into designing and implementing a single deep learning framework that can be used for both stream gaging and wave gauging from appropriate time-series of imagery. I show that it is possible to train the framework to estimate 1) stage and/or discharge from oblique imagery of streams at USGS gaging stations, using existing time-lapse camera infrastructure; and 2) nearshore wave height and period from oblique and rectified imagery from USGS Argus systems. This proof-of-concept technique is based on deep convolutional neural networks (CNNs), which are deep learning models for regression tasks based on automated image feature extraction. The stream/wave gauge model framework consists of an existing generic CNN model to extract features from imagery - called a ‘base model', with additional layers to distill the feature information into lower dimensional spaces, prevent overfitting, and a final layer of dense neurons to predict continuously varying quantities. Given enough training data, the model can generalize well to a site despite variation in, for example, lighting, weather, snow cover, vegetation, and any transient objects in the scene. This development might offer the potential to train models for imagery at sites based on short deployments of in situ instrumentation, especially useful for sites where instrumentation is difficult or expensive to maintain for long periods. This entirely data-driven technique, at least for now, must be trained separately for each site and quantity, so would be suitable for very long-term, site-specific estimation of wave or hydraulic parameters from stationary camera installations, subsequent to a training period. Further development might promote low-cost (or even hobbyist) hydrodynamic and hydraulic monitoring anywhere.
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