README.md

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# Dataset Card for Dataset Name

<!-- Provide a quick summary of the dataset. -->

We provide here datasets to help in building classification for quality of astronomical images. It is inspired from the publication 
[Assessment of Astronomical Images Using Combined Machine-learning Models](https://doi.org/10.3847/1538-3881/ab7938).
Authors of the publication did not provide access to the datasets used.
We provide 2 different datasets:
- raw dataset: astronomical images with LDAC files containing features extracted with the tool SExtractor,
- processed dataset: catalogs of features usable as inputs for modeling.

These datasets are part of the project [astro_iqa](https://github.com/mfournigault/astro_iqa).

# Support

Your support will enable to enrich this dataset with new data captured with new sources:
[buymeacoffee.com/selfmaker](https://buymeacoffee.com/selfmaker)

Support the Github project, where you can find models built thanks to this dataset: [astro_iqa](https://github.com/mfournigault/astro_iqa).

## Raw Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

Raw data sources are a compilation of images captured by the MegaCam camera at the Canada-France-Hawaii Telescope, and captured with my personal telescope/camera.
Features are extracted from images by using the software SExtractor.
For each FITS file present in the directory "./data/raw", the software will produce a LDAC file in the format "FITS_1.0".
Each image is associated to one LDAC file.

- **Curated by:** [selfmaker]
- **Funded by [selfmaker]:** 
- **Shared by [selfmaker]:** 
- **License:** [CC-BY-NC-SA-4.0]

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** [./raw]
- **Demo:** [For usage details, see the notebooks SOM_datasets_preparation and dnn_datasets_preparation in the github project repo]

## Processed Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
The dataset is composed of feature catalogs and image annotations.

**The catalogs** are built by combining all the LDAC files produced by the SExtractor software. For each object, SExtractor is used to output the following features:
- X and Y coordinates of the object in the image,
- ISO0,
- ELONGATION,
- ELLIPTICITY,
- CLASS_STAR,
- BACKGROUND.
The exposure time of the image is also added to the catalog as it can significantly affect the quality and characteristics of the detected sources.

- **Curated by:** [selfmaker]
- **Funded by [selfmaker]:** 
- **Shared by [selfmaker]:** 
- **License:** [CC-BY-NC-SA-4.0]

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** [./for_modeling]
- **Demo:** [For usage details, see the notebooks SOM_datasets_preparation, dnn_datasets_preparation and datasets_verifiation in the github project repo]


Annotations follow the COCO format:
"info": {
        ...
    },
    "images": [
        filenames, ...
    ],
    "categories": [
        "GOOD",
        "B_SEEING",
        "BGP",
        "BT",
        "RBT"
    ],
    "annotations": { ... }
Annotations files are located in the fold "data/for_modeling". The ones used to compose the current dataset are:
- map_images_labels_cadc2.json
- map_images_labels_ngc0869.json
- map_images_labels_ngc0896.json
- 8595 map_images_labels_ngc7000.json

Annotations are already reported in the parquet files.

To built tensorflow datasets from the parquet catalogs see the github project repo: [https://github.com/mfournigault/astro_iqa](https://github.com/mfournigault/astro_iqa).

## Uses

<!-- Address questions around how the dataset is intended to be used. -->
These datasets aim to develop a quality assessment tool for astronomical images. 
Given an astronomical image, to classify the image between categories: good, bad tracking, very bad tracking, bad seeing, or background issues.
This classification can then be used:
- during image capturing with a telescope to warn the user of potential issues,
- during image stacking to discard bad images of bad quality, and so enable a stacking pipeline completely automated.
For usage details, see the notebooks SOM_datasets_preparation, dnn_datasets_preparation and datasets_verification in the github project repo

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
See the project documentation for a complete description of dataset structures..


## More Information

For more information, see the documentation of the project [astro_iqa](https://github.com/mfournigault/astro_iqa).