Improving Prediction Capabilities for Barrier Island Landscape Change
Description
This research uses several AI/ML tools to observe and analyze coastal landscape change at critical habitats along barrier islands. The work employs traditional ML methods (random forest model), and more cutting-edge methods, like foundation models (e.g. Segment Anything). AI tools that leverage human-in-the-loop methods (e.g. Doodler) enable efficient and reliable landcover map creation from aerial and satellite imagery and enhanced historical imagery that provides important long-term perspectives of landcover change. This work aids land management decision-making and risk assessments and provides data for model validation.
Detailed example
Data products include landcover maps and change maps; Publications describe model validation for coastal natural hazards and landscape evolution; Continued development of AI/ML methods and workflows to inform landscape change assessments.
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
More accurate and longer-term information leads to more efficient and effective resource management, protecting property and national interests.
Controls / human review
ATO: No; PIA: Not published
Data needed
Coastal aerial imagery and ground-reference data (example data release with attributed information is located at https://www.sciencebase.gov/catalog/item/67927873d34e88f5864c49b0)