Fire Resilient Landscapes
Description
Assists in planning treatments to reduce wildfire risk and calculate treatment costs
Detailed example
Raster and vector surfaces, summary statistics, and graphics
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
Cost savings and improving efficiencies
Controls / human review
ATO: No; PIA: Not published
Data needed
Data is a mixture of tabular, text, and geospatial data, including: map data on roads, streams, and water bodies from OpenStreetMap webservice, existing fuels, TreeMap data, potential operational delineations (PODs), desired future conditions text documents, machine rates cost/calculator excel documents, US Forest Service engineering tables