# On the Visual-based Safe Landing of UAVs in Populated Areas: a Crucial Aspect for Urban Deployment

arXiv: https://arxiv.org/abs/2102.13253 · HTML: https://mlsanity.com/paper/2102.13253/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2021.

## Built on by (most cited first, 7 of 7)

- [Tracker Meets Night: A Transformer Enhancer for UAV Tracking](https://mlsanity.com/paper/2303.10951/) (2023)
- [Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environments](https://mlsanity.com/paper/2203.13792/) (2022)
- [Risk Assessment for Autonomous Landing in Urban Environments using Semantic Segmentation](https://mlsanity.com/paper/2410.12988/) (2024)
- [MambaNUT: Nighttime UAV Tracking via Mamba-based Adaptive Curriculum Learning](https://mlsanity.com/paper/2412.00626/) (2024)
- [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)
- [Dual Prompt-Driven Feature Encoding for Nighttime UAV Tracking](https://mlsanity.com/paper/2603.19628/) (2026)

## Abstract (arXiv)

Autonomous landing of Unmanned Aerial Vehicles (UAVs) in crowded scenarios is crucial for successful deployment of UAVs in populated areas, particularly in emergency landing situations where the highest priority is to avoid hurting people. In this work, a new visual-based algorithm for identifying Safe Landing Zones (SLZ) in crowded scenarios is proposed, considering a camera mounted on an UAV, where the people in the scene move with unknown dynamics. To do so, a density map is generated for each image frame using a Deep Neural Network, from where a binary occupancy map is obtained aiming to overestimate the people's location for security reasons. Then, the occupancy map is projected to the head's plane, and the SLZ candidates are obtained as circular regions in the head's plane with a minimum security radius. Finally, to keep track of the SLZ candidates, a multiple instance tracking algorithm is implemented using Kalman Filters along with the Hungarian algorithm for data association. Several scenarios were studied to prove the validity of the proposed strategy, including public datasets and real uncontrolled scenarios with people moving in public squares, taken from an UAV in flight. The study showed promising results in the search of preventing the UAV from hurting people during emergency landing.