# Risk Assessment for Autonomous Landing in Urban Environments using Semantic Segmentation

arXiv: https://arxiv.org/abs/2410.12988 · HTML: https://mlsanity.com/paper/2410.12988/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://mlsanity.com/paper/1802.10062/) (2018)
- [On the Visual-based Safe Landing of UAVs in Populated Areas: a Crucial Aspect for Urban Deployment](https://mlsanity.com/paper/2102.13253/) (2021)
- [Dense Crowds Detection and Counting with a Lightweight Architecture](https://mlsanity.com/paper/2007.06630/) (2020)
- [Dense Crowds Detection and Surveillance with Drones using Density Maps](https://mlsanity.com/paper/2003.08766/) (2020)

## Built on by (most cited first, 2 of 2)

- [VisLanding: Monocular 3D Perception for UAV Safe Landing via Depth-Normal Synergy](https://mlsanity.com/paper/2506.14525/) (2025)
- [ViVa-SAFELAND: a New Freeware for Safe Validation of Vision-based Navigation in Aerial Vehicles](https://mlsanity.com/paper/2503.14719/) (2025)

## Abstract (arXiv)

In this paper, we address the vision-based autonomous landing problem in complex urban environments using deep neural networks for semantic segmentation and risk assessment. We propose employing the SegFormer, a state-of-the-art visual transformer network, for the semantic segmentation of complex, unstructured urban environments. This approach yields valuable information that can be utilized in smart autonomous landing missions, particularly in emergency landing scenarios resulting from system failures or human errors. The assessment is done in real-time flight, when images of an RGB camera at the Unmanned Aerial Vehicle (UAV) are segmented with the SegFormer into the most common classes found in urban environments. These classes are then mapped into a level of risk, considering in general, potential material damage, damaging the drone itself and endanger people. The proposed strategy is validated through several case studies, demonstrating the huge potential of semantic segmentation-based strategies to determining the safest landing areas for autonomous emergency landing, which we believe will help unleash the full potential of UAVs on civil applications within urban areas.