# Detection and Initial Assessment of Lunar Landing Sites Using Neural Networks

arXiv: https://arxiv.org/abs/2207.11413 · HTML: https://mlsanity.com/paper/2207.11413/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 23 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images](https://mlsanity.com/paper/1710.02726/) (2017)
- [A Review of Object Detection Models based on Convolutional Neural Network](https://mlsanity.com/paper/1905.01614/) (2019)
- [Hazard Detection And Avoidance For The Nova-C Lander](https://mlsanity.com/paper/2204.00660/) (2022)
- [Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers](https://mlsanity.com/paper/2102.12432/) (2021)

## Abstract (arXiv)

Robotic and human lunar landings are a focus of future NASA missions. Precision landing capabilities are vital to guarantee the success of the mission, and the safety of the lander and crew. During the approach to the surface there are multiple challenges associated with Hazard Relative Navigation to ensure safe landings. This paper will focus on a passive autonomous hazard detection and avoidance sub-system to generate an initial assessment of possible landing regions for the guidance system. The system uses a single camera and the MobileNetV2 neural network architecture to detect and discern between safe landing sites and hazards such as rocks, shadows, and craters. Then a monocular structure from motion will recreate the surface to provide slope and roughness analysis.