# Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

arXiv: https://arxiv.org/abs/2102.10545 · HTML: https://mlsanity.com/paper/2102.10545/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 21 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://mlsanity.com/paper/1511.02680/) (2015)
- [Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision](https://mlsanity.com/paper/1906.01620/) (2019)
- [Evaluating Bayesian Deep Learning Methods for Semantic Segmentation](https://mlsanity.com/paper/1811.12709/) (2018)
- [Crater Detection via Convolutional Neural Networks](https://mlsanity.com/paper/1601.00978/) (2016)

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

- [Visual Environment Assessment for Safe Autonomous Quadrotor Landing](https://mlsanity.com/paper/2311.10065/) (2023)
- [Deep Monocular Hazard Detection for Safe Small Body Landing](https://mlsanity.com/paper/2301.13254/) (2023)
- [HALO: Hazard-Aware Landing Optimization for Autonomous Systems](https://mlsanity.com/paper/2304.01583/) (2023)
- [Explainable Convolutional Networks for Crater Detection and Lunar Landing Navigation](https://mlsanity.com/paper/2408.13587/) (2024)
- [Efficient Feature Description for Small Body Relative Navigation using Binary Convolutional Neural Networks](https://mlsanity.com/paper/2304.04985/) (2023)
- [Real-Time Stochastic Terrain Mapping and Processing for Autonomous Safe Landing](https://mlsanity.com/paper/2409.09309/) (2024)

## Abstract (arXiv)

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs' reliability. In response to these limitations, this paper proposes an application of the Bayesian deep-learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by: (i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation; and (ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.