# Deep Learning based Systems for Crater Detection: A Review

arXiv: https://arxiv.org/abs/2310.07727 · HTML: https://mlsanity.com/paper/2310.07727/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 140 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [RepPoints: Point Set Representation for Object Detection](https://mlsanity.com/paper/1904.11490/) (2019)
- [Imbalance Problems in Object Detection: A Review](https://mlsanity.com/paper/1909.00169/) (2019)
- [Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost](https://mlsanity.com/paper/1910.07153/) (2019)
- [CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement](https://mlsanity.com/paper/2005.02551/) (2020)
- [End-to-End Object Detection with Fully Convolutional Network](https://mlsanity.com/paper/2012.03544/) (2020)
- [Proposal Learning for Semi-Supervised Object Detection](https://mlsanity.com/paper/2001.05086/) (2020)
- [Automated crater shape retrieval using weakly-supervised deep learning](https://mlsanity.com/paper/1906.08826/) (2019)
- [Automated Crater Detection from Co-registered Optical Images, Elevation Maps and Slope Maps using Deep Learning](https://mlsanity.com/paper/2012.15281/) (2020)
- [Mask-guided sample selection for Semi-Supervised Instance Segmentation](https://mlsanity.com/paper/2008.11073/) (2020)
- [Automated crater detection with human level performance](https://mlsanity.com/paper/2010.12520/) (2020)
- [Automatic Crater Shape Retrieval using Unsupervised and Semi-Supervised Systems](https://mlsanity.com/paper/2211.01933/) (2022)

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

- [Vision-Language Model for Accurate Crater Detection](https://mlsanity.com/paper/2601.07795/) (2026)
- [Evaluation of Resource-Efficient Crater Detectors on Embedded Systems](https://mlsanity.com/paper/2405.16953/) (2024)
- [Deep learning framework for crater detection and identification on the Moon and Mars](https://mlsanity.com/paper/2508.03920/) (2025)
- [Adaptive Quantized Planetary Crater Detection System for Autonomous Space Exploration](https://mlsanity.com/paper/2508.18025/) (2025)
- [Adapting a Foundation Model for Lunar Surface Height Estimation](https://mlsanity.com/paper/2609.02448/) (2026)

## Abstract (arXiv)

Craters are one of the most prominent features on planetary surfaces, used in applications such as age estimation, hazard detection, and spacecraft navigation. Crater detection is a challenging problem due to various aspects, including complex crater characteristics such as varying sizes and shapes, data resolution, and planetary data types. Similar to other computer vision tasks, deep learning-based approaches have significantly impacted research on crater detection in recent years. This survey aims to assist researchers in this field by examining the development of deep learning-based crater detection algorithms (CDAs). The review includes over 140 research works covering diverse crater detection approaches, including planetary data, craters database, and evaluation metrics. To be specific, we discuss the challenges in crater detection due to the complex properties of the craters and survey the DL-based CDAs by categorizing them into three parts: (a) semantic segmentation-based, (b) object detection-based, and (c) classification-based. Additionally, we have conducted training and testing of all the semantic segmentation-based CDAs on a common dataset to evaluate the effectiveness of each architecture for crater detection and its potential applications. Finally, we have provided recommendations for potential future works.