# Automated crater shape retrieval using weakly-supervised deep learning

arXiv: https://arxiv.org/abs/1906.08826 · HTML: https://mlsanity.com/paper/1906.08826/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [Deep Learning based Systems for Crater Detection: A Review](https://mlsanity.com/paper/2310.07727/) (2023)
- [Autonomous crater detection on asteroids using a fully-convolutional neural network](https://mlsanity.com/paper/2204.00477/) (2022)
- [Automatic Crater Shape Retrieval using Unsupervised and Semi-Supervised Systems](https://mlsanity.com/paper/2211.01933/) (2022)
- [Camera-Pose Robust Crater Detection from Chang'e 5](https://mlsanity.com/paper/2406.04569/) (2024)
- [A machine-generated catalogue of Charon's craters and implications for the Kuiper belt](https://mlsanity.com/paper/2206.08277/) (2022)
- [CraterBench-R: Instance-Level Crater Retrieval for Planetary Scale](https://mlsanity.com/paper/2604.06245/) (2026)

## Abstract (arXiv)

Crater ellipticity determination is a complex and time consuming task that so far has evaded successful automation. We train a state of the art computer vision algorithm to identify craters in Lunar digital elevation maps and retrieve their sizes and 2D shapes. The computational backbone of the model is MaskRCNN, an "instance segmentation" general framework that detects craters in an image while simultaneously producing a mask for each crater that traces its outer rim. Our post-processing pipeline then finds the closest fitting ellipse to these masks, allowing us to retrieve the crater ellipticities. Our model is able to correctly identify 87% of known craters in the longitude range we hid from the network during training and validation (test set), while predicting thousands of additional craters not present in our training data. Manual validation of a subset of these "new" craters indicates that a majority of them are real, which we take as an indicator of the strength of our model in learning to identify craters, despite incomplete training data. The crater size, ellipticity, and depth distributions predicted by our model are consistent with human-generated results. The model allows us to perform a large scale search for differences in crater diameter and shape distributions between the lunar highlands and maria, and we exclude any such differences with a high statistical significance. The predicted test set catalogue and trained model are available here: https://github.com/malidib/CratersMaskRCNN/.