dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
- name: antenna
dtype: string
- name: datetime
dtype: string
splits:
- name: train
num_bytes: 1833634770.762
num_examples: 113409
- name: validation
num_bytes: 228715568.866
num_examples: 14253
- name: test
num_bytes: 230174837.398
num_examples: 14271
download_size: 2352964096
dataset_size: 2292525177.026
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
e-Callisto Solar Flare Detection Dataset
Institute of Data Science i4Ds, FHNW
Compiled by Vincenzo Timmel | Kenfus
Overview
This dataset comprises radio spectra from the e-Callisto solar spectrometer network, annotated according to the e-Callisto Label List by Christian Monstein. It is designed for training machine learning models to automatically detect and classify solar flares, using data collected via the ecallisto_ng Package.
Non Radio-Sunburst Images
For every Radio-Sunburst image, five Non-Sunburst images are included (Label 0).
Data Collection
The dataset encompasses observations from several antennas, each documenting specific periods of data collection. Below is the updated list of stations with their respective data collection ranges:
| Antenna | Min Date | Max Date |
|---|---|---|
| ALASKA-COHOE_63 | 2022-04-09 | 2024-02-22 |
| ALASKA-HAARP_62 | 2021-11-18 | 2024-02-23 |
| ALGERIA-CRAAG_59 | 2021-04-23 | 2024-02-22 |
| ALMATY_58 | 2021-02-28 | 2024-02-23 |
| AUSTRIA-UNIGRAZ_01 | 2021-01-20 | 2024-02-23 |
| Australia-ASSA_02 | 2021-02-13 | 2021-12-09 |
| Australia-ASSA_62 | 2021-12-10 | 2024-02-22 |
| BIR_01 | 2021-04-17 | 2024-02-14 |
| EGYPT-Alexandria_02 | 2021-08-20 | 2024-02-21 |
| GERMANY-DLR_63 | 2022-11-11 | 2024-02-22 |
| GLASGOW_01 | 2022-01-07 | 2024-02-22 |
| HUMAIN_59 | 2021-01-20 | 2024-02-23 |
| INDIA-GAURI_01 | 2022-04-20 | 2024-02-21 |
| INDIA-OOTY_02 | 2021-12-09 | 2024-02-23 |
| KASI_59 | 2021-04-22 | 2024-02-23 |
| MEXART_59 | 2021-02-18 | 2024-02-21 |
| MEXICO-FCFM-UANL_01 | 2023-09-02 | 2024-02-21 |
| MEXICO-LANCE-B_62 | 2022-03-30 | 2022-08-02 |
| MONGOLIA-UB_01 | 2021-03-01 | 2024-02-16 |
| MRO_59 | 2021-03-01 | 2024-02-16 |
| MRO_61 | 2021-02-28 | 2024-02-16 |
| NORWAY-EGERSUND_01 | 2022-10-16 | 2024-02-23 |
| SSRT_59 | 2022-10-30 | 2024-02-23 |
| SWISS-Landschlacht_62 | 2021-10-05 | 2024-02-23 |
| TRIEST_57 | 2021-01-20 | 2024-02-23 |
| USA-ARIZONA-ERAU_01 | 2022-05-15 | 2024-02-22 |
Data Augmentation
Data augmentation is applied by subtracting random minutes before the start of a detected radio sunburst, thereby generating 15-minute images that include the onset of a radio-sunburst.
Caution
Preprocessing, including label cleanup based on specific assumptions, has been applied to the dataset. Users should note that the labels might not be entirely accurate, reflecting potential inaccuracies
Distribution
Train
Validation
Test
