Session Leveraging Unsupervised Machine Learning in Environmental Science


Description:

We invite abstract submissions for projects that have programmed unsupervised machine learning models for use in environmental science research and project pipelines. Examples include but are not limited to pattern recognition and object detection, classification, etc.


Papers:
Cloud and Aerosol Variability Across Different Synoptic Regimes Identified with a Deep Learning-based Unsupervised Clustering Model
Xue Zheng, LLNL, Livermore, CA; and S. Qiu

Vegetation mapping of Africa using machine learning
Ismail Adewale Olumegbon, Univ. of Maryland, Baltimore County, Greenbelt, MD; and H. M. Barbosa

The Utility of Domain Knowledge When Developing Deep Learning Models to Predict Coastal Fog
Waylon G. Collins, NOAA/National Weather Service, Corpus Christi, TX; and B. Colburn, P. E. Tissot, S. A. King, E. Krell, and J. K. Williams

Submission Deadline: Thursday, 31 August 2023



See more of: 23rd Conference on Artificial Intelligence for Environmental Science
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