OMB Individually Reported
Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning
Low riskExact public inventory row
Description
Applies convolutional neural networks (supervised learning) to automatically identify the presence of different aerosol species (e.g., dust and smoke) in CALIPSO lidar backscatter measurements.
Detailed example
presence of different aerosol species (e.g., dust and smoke)
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
Applies convolutional neural networks (supervised learning) to automatically identify the presence of different aerosol species (e.g., dust and smoke) in CALIPSO lidar backscatter measurements.
Controls / human review
ATO: Not reported; PIA: Not published