Session 11B Leveraging Unsupervised Machine Learning in Environmental Science

Wednesday, 31 January 2024: 1:45 PM-3:00 PM
338 (The Baltimore Convention Center)
Host: 23rd Conference on Artificial Intelligence for Environmental Science
Cochairs:
Kyle A. Hilburn and Kirsten J Mayer, Colorado State University, Department of Atmospheric Science, Fort Collins, CO

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:
1:45 PM
11B.1
Vegetation Mapping of Africa Using Machine Learning
Ismail Adewale Olumegbon, Univ. of Maryland, Baltimore County, Greenbelt, MD; and H. M. Barbosa

Handout (3.3 MB)

2:00 PM
11B.2
Three-Dimensional Convective Cell Database for Cloud Process Study – Segmentation and Validation
Xiaowen Li, Morgan State University, Baltimore, MD; Morgan State Univ., Baltimore, MD; and M. Rafsan Jani and C. Padilla

2:15 PM
11B.3
Classification of Cloud Particle Imagery Using Variational Autoencoders and Unsupervised Clustering
Joseph Ko, Columbia Univ., New York, NY; and K. J. Sulia, V. Przybylo, M. van Lier-Walqui, and K. D. Lamb

2:30 PM
11B.4
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

2:45 PM
11B.5
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, Faruque, and J. Wang

Recorded Presentation
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
See more of: 23rd Conference on Artificial Intelligence for Environmental Science
<< Previous Session | Next Session >>