# Vision-Based Risk Aware Emergency Landing for UAVs in Complex Urban Environments

arXiv: https://arxiv.org/abs/2505.20423 · HTML: https://mlsanity.com/paper/2505.20423/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 6 of 6)

- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (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)
- [Visual Environment Assessment for Safe Autonomous Quadrotor Landing](https://mlsanity.com/paper/2311.10065/) (2023)
- [Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environments](https://mlsanity.com/paper/2203.13792/) (2022)
- [ViVa-SAFELAND: a New Freeware for Safe Validation of Vision-based Navigation in Aerial Vehicles](https://mlsanity.com/paper/2503.14719/) (2025)

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

- [Drones that Think on their Feet: Sudden Landing Decisions with Embodied AI](https://mlsanity.com/paper/2510.00167/) (2025)
- [See&Say: Vision Language Guided Safe Zone Detection for Autonomous Package Delivery Drones](https://mlsanity.com/paper/2604.13292/) (2026)

## Abstract (arXiv)

Landing safely in crowded urban environments remains an essential yet challenging endeavor for Unmanned Aerial Vehicles (UAVs), especially in emergency situations. In this work, we propose a risk-aware approach that harnesses semantic segmentation to continuously evaluate potential hazards in the drone's field of view. By using a specialized deep neural network to assign pixel-level risk values and applying an algorithm based on risk maps, our method adaptively identifies a stable Safe Landing Zone (SLZ) despite moving critical obstacles such as vehicles, people, etc., and other visual challenges like shifting illumination. A control system then guides the UAV toward this low-risk region, employing altitude-dependent safety thresholds and temporal landing point stabilization to ensure robust descent trajectories. Experimental validation in diverse urban environments demonstrates the effectiveness of our approach, achieving over 90% landing success rates in very challenging real scenarios, showing significant improvements in various risk metrics. Our findings suggest that risk-oriented vision methods can effectively help reduce the risk of accidents in emergency landing situations, particularly in complex, unstructured, urban scenarios, densely populated with moving risky obstacles, while potentiating the true capabilities of UAVs in complex urban operations.