Comparison study on the deep-learning-based detection of Mars craters
Bibliographic record
Abstract
Deep-learning methods are of interest for the analysis of imagery and digital elevation models from Mars orbiting satellites. They detect various atmosphere and surface characteristics. For instance, these include dust storms and craters [1,2]. We approach this topic by using the deep-learning-based crater detection algorithm DeepMars2 [3,4]. The algorithm is applied to two digital elevation models (DEMs) of the Mars surface. The DEMs are based on the satellite instruments MOLA/MGS (Mars Orbiter Laser Altimeter/Mars Global Surveyor) and HRSC/MEX (High Resolution Stereo Camera/Mars Express) and have different resolution. Crater detection statistics are compared between both DEMs.[1] Alshehhi, R., Gebhardt, C. Detection of Martian dust storms using mask regional convolutional neural networks. Prog Earth Planet Sci 9, 4 (2022). https://doi.org/10.1186/s40645-021-00464-1[2] R. Alshehhi and C. Gebhardt, "Automated Geological Landmarks Detection on Mars Using Deep Domain Adaptation From Lunar High-Resolution Satellite Images," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 2274-2283, 2022, doi: 10.1109/JSTARS.2022.3156371.[3] Lee, C. (2019). Automated crater detection on Mars using deep learning. Planetary and Space Science, 170, 16-28. https://doi.org/10.1016/j.pss.2019.03.008[4] Lee, C. & Hogan, J. (2021). Automated crater detection with human level performance. Computers & Geosciences, 147, 104645. https://doi.org/10.1016/j.cageo.2020.104645
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".