Training Data for Streamflow Estimation
Description
This work uses convolutional neural networks to refine training data for streamflow estimation. The project will implement, test, and operationalize a system to derive effective stream width data using data from ESA's Sentinel-1 C-band radar satellite constellation, archive the data produced, and distribute the data for free and open use to train machine learning models relating to stream flow and effective stream width.
Detailed example
To create training data for machine learning using the European Space Agency's (ESA) Sentinel-1 C-band SAR data from ASF DAAC's growing cloud-based SAR data archive and new Sentinel-1 data as it is received.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Training Data for Streamflow Estimation
Controls / human review
ATO: Not reported; PIA: Not published