Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
Problem
Aircraft icing remains a critical aviation threat, and no robust model existed to detect potential icing areas over East Asia from geostationary satellite data.
Why it matters
Icing threatens aircraft safety, so reliable detection of icing areas can help protect flights.
What the AI did
Random forest and multinomial log-linear models were trained on COMS MI and Himawari-8 AHI satellite data using quality-controlled pilot reports (PIREPs) as reference, and compared to existing models via five-fold cross-validation.
Results
not stated in source (abstract truncated mid-sentence before the detection metric value)
Impact
Provides ML-based icing detection models for East Asia validated against pilot reports, per abstract.
Record
| Venue | Remote Sensing |
|---|---|
| Published | 2018 |
| Work type | article |
| Authors | Seongmun Sim, Jungho Im, Sumin Park, Haemi Park, Myoung‐Hwan Ahn, Pak Wai Chan |
| Citations | 29 (OpenAlex, at import time) |
| References | 54 |
| Model or method | Random forest (RF) and multinomial log-linear (MLL) models |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/rs10040631 |
| Open access | gold |
| Keywords | aircraft icing, geostationary satellite, random forest, remote sensing, aviation safety |
Importance rating
significant — thousands to millions affected
Abstract states aircraft icing is a critical threat to aviation and no robust East Asia detection model existed; safety benefit across flights in a major region.
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