# Image Segmentation to Identify Safe Landing Zones for Unmanned Aerial Vehicles

arXiv: https://arxiv.org/abs/2111.14557 · HTML: https://mlsanity.com/paper/2111.14557/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [SpaceNet 6: Multi-Sensor All Weather Mapping Dataset](https://mlsanity.com/paper/2004.06500/) (2020)
- [No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs](https://mlsanity.com/paper/1802.08755/) (2018)
- [Play and Learn: Using Video Games to Train Computer Vision Models](https://mlsanity.com/paper/1608.01745/) (2016)
- [Recurrent Multiresolution Convolutional Networks for VHR Image Classification](https://mlsanity.com/paper/1806.05793/) (2018)
- [Monocular and Stereo Cues for Landing Zone Evaluation for Micro UAVs](https://mlsanity.com/paper/1812.03539/) (2018)
- [Potential UAV Landing Sites Detection through Digital Elevation Models Analysis](https://mlsanity.com/paper/2107.06921/) (2021)

## Built on by (most cited first, 3 of 3)

- [An Experimental Study of Trojan Vulnerabilities in UAV Autonomous Landing](https://mlsanity.com/paper/2510.20932/) (2025)
- [Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environments](https://mlsanity.com/paper/2203.13792/) (2022)
- [See&Say: Vision Language Guided Safe Zone Detection for Autonomous Package Delivery Drones](https://mlsanity.com/paper/2604.13292/) (2026)

## Abstract (arXiv)

There is a marked increase in delivery services in urban areas, and with Jeff Bezos claiming that 86% of the orders that Amazon ships weigh less than 5 lbs, the time is ripe for investigation into economical methods of automating the final stage of the delivery process. With the advent of semi-autonomous drone delivery services, such as Irish startup `Manna', and Malta's `Skymax', the final step of the delivery journey remains the most difficult to automate. This paper investigates the use of simple images captured by a single RGB camera on a UAV to distinguish between safe and unsafe landing zones. We investigate semantic image segmentation frameworks as a way to identify safe landing zones and demonstrate the accuracy of lightweight models that minimise the number of sensors needed. By working with images rather than video we reduce the amount of energy needed to identify safe landing zones for a drone, without the need for human intervention.