sunspot-single-V-cluster-detection Computer Vision Project
Updated 7 days ago
Metrics
Sunspot detector using SDO/HMI
This model is being developed to classify suspots using the Zurich McIntosh classification.
Current Usage
Initial attempts to classifify the full 3 charachter McIntosh classification were deemed to difficult and therfore the approach to date is to limit the class to Zurich only.
Once a higher rate of success is achived this model will for a stacked layer to re-classifiy for the later McIntish parameters. The model success is improving and by setting the prediction rate to 20% the model is highly effective classification.
Therfore this is still very much in-progress.
Labelling
The labbelling is currently now at the stage where it's using the current model as the predictive first attempt. What has been cosnistent to date is the bounding boxes need to understand the seperate between sunspots and therefore the boxes look initial like they're including a lot of non-sunspot activity.
This however is useful how when the second stacked layer will coming into play, as this will re-classifiy within the p (Penumbra) and c (degreee of compatness)
Copyright
The images used in this project are downloaded from the NASA SDO website. See https://sdo.gsfc.nasa.gov/data/rules.php.
Therefore all images provided by SDO NASA are "Courtesy of NASA/SDO and the AIA, EVE, and HMI science teams."
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
sunspot-single-v-cluster-detection_dataset,
title = { sunspot-single-V-cluster-detection Dataset },
type = { Open Source Dataset },
author = { solarflarelabelling },
howpublished = { \url{ https://universe.roboflow.com/solarflarelabelling/sunspot-single-v-cluster-detection } },
url = { https://universe.roboflow.com/solarflarelabelling/sunspot-single-v-cluster-detection },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { sep },
note = { visited on 2024-09-25 },
}