Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 24
Articles | Volume 17, issue 24
https://doi.org/10.5194/amt-17-7109-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-7109-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 24
Research article
 | 
19 Dec 2024
Research article |  | 19 Dec 2024

IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme

Huiying Zhang, Xia Li, Fabiola Ramelli, Robert O. David, Julie Pasquier, and Jan Henneberger

Data sets

Data for publication “IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme” Huiying Zhang et al. https://doi.org/10.5281/zenodo.14288739

Model code and software

Models for publication “IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme” Huiying Zhang et al. https://doi.org/10.5281/zenodo.14288804

Code for publication “IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme” Huiying Zhang et al. https://doi.org/10.5281/zenodo.14288779

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Short summary
Our innovative IceDetectNet algorithm classifies each part of aggregated ice crystals, considering both their basic shape and physical processes. Trained on ice crystal images from the Arctic taken by a holographic camera, it correctly classifies over 92 % of the ice crystals. These more detailed insights into the components of aggregated ice crystals have the potential to improve our estimates of microphysical properties such as riming rate, aggregation rate, and ice water content.
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