ViVa-SAFELAND: a New Freeware for Safe Validation of Vision-based Navigation in Aerial Vehicles
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Abstract (arXiv)
ViVa-SAFELAND is an open source software library, aimed to test and evaluate vision-based navigation strategies for aerial vehicles, with special interest in autonomous landing, while complying with legal regulations and people's safety. It consists of a collection of high definition aerial videos, focusing on real unstructured urban scenarios, recording moving obstacles of interest, such as cars and people. Then, an Emulated Aerial Vehicle (EAV) with a virtual moving camera is implemented in order to ``navigate" inside the video, according to high-order commands. ViVa-SAFELAND provides a new, safe, simple and fair comparison baseline to evaluate and compare different visual navigation solutions under the same conditions, and to randomize variables along several trials. It also facilitates the development of autonomous landing and navigation strategies, as well as the generation of image datasets for different training tasks. Moreover, it is useful for training either human of autonomous pilots using deep learning. The effectiveness of the framework for validating vision algorithms is demonstrated through two case studies, detection of moving objects and risk assessment segmentation. To our knowledge, this is the first safe validation framework of its kind, to test and compare visual navigation solution for aerial vehicles, which is a crucial aspect for urban deployment in complex real scenarios.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 27 references that are in the map. First posted on arXiv 18 Mar 2025.
Builds on
7 of 27 references.
References · 7
- 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
- 2018Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challengescited by 49
- 2021On the Visual-based Safe Landing of UAVs in Populated Areas: a Crucial Aspect for Urban Deploymentcited by 7
- 2021Certifying Emergency Landing for Safe Urban UAVcited by 6
- 2020Dense Crowds Detection and Counting with a Lightweight Architecturecited by 4
- 2022Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environmentscited by 2
- 2024Risk Assessment for Autonomous Landing in Urban Environments using Semantic Segmentationcited by 2
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Citing papers per year: 2025: 1.
1 paper.