OMB Individually Reported
RSCC and TCA projects [2024 INV#WO0000000108017]
Low riskExact public inventory row
Description
Automated identification of coastal features in remote sensing data, reduced analyst time
Detailed example
Elevation and position of dune crest and toe, position of shoreline
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
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
Increased efficiency in identifying coastal features in remote sensing data compared to existing methods that are human analyst time intensive. Increased efficiency was tested by timing traditional methods versus AI/ML methods
Controls / human review
ATO: No; PIA: Not published
Data needed
Data used to train and evaluate the model came from various USGS data releases of dune morphology and shorelines.