Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the FIREX-AQ Datasets

By N. LaHaye, Anistasija Easley, Kyongsik Yun, Huikyo Lee, E. Linstead, M. Garay, Olga V. Kalashnikova. 2025

Subfields: Atmospheric Science, Representation & Self-Supervised Learning, Remote Sensing

Citations in the corpus

• Fusing Multi‐ and Hyperspectral Satellite Data for Harmful Algal Bloom Monitoring With Self‐Supervised and Hierarchical Deep Learning (2025, unknown): “In our latest work (LaHaye, Easley, Yun, et al., 2025; LaHaye, Munashinge, et al., 2025), we expanded our machine learning framework into the SIT-FUSE (Segmentation, Instance Tracking, and data Fusion Using multi-SEnsor imagery) library, an open-source system for segmenting, tracking, and analyzing…”