Projects per year
Abstract
Contrails, formed by jet flights, alter Earth's energy balance, prompting research into monitoring contrails and developing satellite-based automated contrail detection. This demand has advanced deep learning (DL)-based techniques. However, training DL algorithms to detect contrails has limitations: class imbalance, labeling difficulty, and a lack of reliable labeled datasets. We propose a probabilistic DL approach using P-UNet to alleviate label bias in contrail detection. By observing model outputs based on two labeled datasets, OpenContrails and MIT-Contrails, we found the probabilistic approach robust against potentially biased labels.
Original language | English |
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Title of host publication | SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems |
Subtitle of host publication | Hamburg, Germany, November 13-16, 2023 |
Editors | Maria Luisa Damiani, Matthias Renz, Ahmed Eldawy, Peer Kröger, Mario A. Nascimento |
Place of Publication | New-York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 1-2 |
Number of pages | 2 |
ISBN (Print) | 979-8-4007-0168-9 |
DOIs | |
Publication status | Published - 22 Dec 2023 |
Keywords
- Computing methodologies
- Artificial intelligence
- Mathematics of computing
- Probability and statistics
- Neural networks
Projects
- 1 Finished
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COVID-Contrail: Changes in US Contrail Outbreaks before and during the COVID-19 Pandemic
Kim, E.-K. (PI) & Lee, Y. (CoI)
1/09/22 → 31/08/24
Project: Research