SatStreaks: Towards Supervised Learning for Delineating Satellite Streaks from Astronomical Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Delineation of satellite streaks in astronomical images is an important aspect of ground based space studies. While deep learning algorithms show promise, training and validation of deep learning models for satellite streak segmentation is challenging due to the limited availability of large-scale, annotated datasets. We introduce SatStreaks, a dataset comprising of 3,130 densely annotated, real images of satellite streaks captured through ongoing citizen science projects. We utilize SatStreaks to develop a U-Net based model for the streak segmentation and conduct an experimental evaluation of data-driven image segmentation algorithms. The satellite streak segmentation codebase consisting of various deep learning models, and the SatStreak dataset has been made publicly available (https://github.com/jijup/SatStreaks <https://github.com/jijup/SatStreaks> ) to facilitate the advancement of computer vision algorithms for space studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it