# Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environments

arXiv: https://arxiv.org/abs/2203.13792 · HTML: https://mlsanity.com/paper/2203.13792/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 12 of 12)

- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Bayesian Loss for Crowd Count Estimation with Point Supervision](https://mlsanity.com/paper/1908.03684/) (2019)
- [Context-Aware Crowd Counting](https://mlsanity.com/paper/1811.10452/) (2018)
- [FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation](https://mlsanity.com/paper/1905.11377/) (2019)
- [Vision-Based Autonomous UAV Navigation and Landing for Urban Search and Rescue](https://mlsanity.com/paper/1906.01304/) (2019)
- [On the Visual-based Safe Landing of UAVs in Populated Areas: a Crucial Aspect for Urban Deployment](https://mlsanity.com/paper/2102.13253/) (2021)
- [Certifying Emergency Landing for Safe Urban UAV](https://mlsanity.com/paper/2104.14928/) (2021)
- [Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and Robotics Together](https://mlsanity.com/paper/2202.01493/) (2022)
- [Dense Crowds Detection and Counting with a Lightweight Architecture](https://mlsanity.com/paper/2007.06630/) (2020)
- [Package Delivery Using Autonomous Drones in Skyways](https://mlsanity.com/paper/2108.06056/) (2021)
- [Image Segmentation to Identify Safe Landing Zones for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2111.14557/) (2021)

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

- [Vision-Based Risk Aware Emergency Landing for UAVs in Complex Urban Environments](https://mlsanity.com/paper/2505.20423/) (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)

Safe autonomous landing for Unmanned Aerial Vehicles (UAVs) in populated areas is a crucial aspect for successful urban deployment, particularly in emergency landing situations. Nonetheless, validating autonomous landing in real scenarios is a challenging task involving a high risk of injuring people. In this work, we propose a framework for real-time safe and thorough evaluation of vision-based autonomous landing in populated scenarios, using photo-realistic virtual environments. We propose to use the Unreal graphics engine coupled with the AirSim plugin for drone's simulation, and evaluate autonomous landing strategies based on visual detection of Safe Landing Zones (SLZ) in populated scenarios. Then, we study two different criteria for selecting the "best" SLZ, and evaluate them during autonomous landing of a virtual drone in different scenarios and conditions, under different distributions of people in urban scenes, including moving people. We evaluate different metrics to quantify the performance of the landing strategies, establishing a baseline for comparison with future works in this challenging task, and analyze them through an important number of randomized iterations. The study suggests that the use of the autonomous landing algorithms considerably helps to prevent accidents involving humans, which may allow to unleash the full potential of drones in urban environments near to people.