# Automated Crater Detection from Co-registered Optical Images, Elevation Maps and Slope Maps using Deep Learning

arXiv: https://arxiv.org/abs/2012.15281 · HTML: https://mlsanity.com/paper/2012.15281/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Deep Learning based Systems for Crater Detection: A Review](https://mlsanity.com/paper/2310.07727/) (2023)
- [Automatic Crater Shape Retrieval using Unsupervised and Semi-Supervised Systems](https://mlsanity.com/paper/2211.01933/) (2022)
- [Vision-Language Model for Accurate Crater Detection](https://mlsanity.com/paper/2601.07795/) (2026)
- [Explainable Convolutional Networks for Crater Detection and Lunar Landing Navigation](https://mlsanity.com/paper/2408.13587/) (2024)
- [Adapting a Foundation Model for Lunar Surface Height Estimation](https://mlsanity.com/paper/2609.02448/) (2026)

## Abstract (arXiv)

Impact craters are formed as a result of continuous impacts on the surface of planetary bodies. This paper proposes a novel way of simultaneously utilizing optical images, digital elevation maps (DEMs), and slope maps for automatic crater detection on the lunar surface. Mask R-CNN, tuned for the crater detection task, is utilized in this paper. Two catalogs, namely, Head-LROC and Robbins, are used for the performance evaluation. Exhaustive analysis of the detection results on the lunar surface has been performed with respect to both Head-LROC and Robbins catalog. With the Head-LROC catalog, which has relatively strict crater markings and larger possibility of missing craters, recall value of 94.28% has been obtained as compared to 88.03% for the baseline method. However, with respect to a manually marked exhaustive crater catalog based on relatively liberal marking, significant precision and recall values are obtained for different crater size ranges. The generalization capability of the proposed method in terms of crater detection on a different terrain with different input data type is also evaluated. We show that the proposed model trained on the lunar surface with optical images, DEMs and corresponding slope maps can be used to detect craters on the Martian surface even with entirely different input data type, such as thermal IR images from the Martian surface.