🚁 FlyAwareV2

Multi-Modal UAV Dataset for Urban Semantic Segmentationin Adverse Weather Conditions

Francesco Barbato*, Matteo Caligiuri*, Pietro Zanuttigh
Department of Information Engineering, University of Padova
*Indicates Equal Contribution
Code arXiv Dataset

Abstract

The development of computer vision algorithms for Unmanned Aerial Vehicle (UAV) applications in urban environments heavily relies on the availability of large-scale datasets with accurate annotations. However, collecting and annotating real-world UAV data is extremely challenging and costly. To address this limitation, we present FlyAwareV2, a novel multimodal dataset encompassing both real and synthetic UAV imagery tailored for urban scene understanding tasks. Building upon the recently introduced SynDrone and FlyAware datasets, FlyAwareV2 introduces several new key contributions: 1) Multimodal data (RGB, depth, semantic labels) across diverse environmental conditions including varying weather and daytime; 2) Depth maps for real samples computed via state-of-the-art monocular depth estimation; 3) Benchmarks for RGB and multimodal semantic segmentation on standard architectures; 4) Studies on synthetic-to-real domain adaptation to assess the generalization capabilities of models trained on the synthetic data. With its rich set of annotations and environmental diversity, FlyAwareV2 provides a valuable resource for research on UAV-based 3D urban scene understanding.

experience

Key Features

  • 🏙️ Multi-Environment: Multiple urban towns and scenarios
  • 📏 Multi-Altitude: Different recording heights (20m, 50m, 120m)
  • 🎯 Multi-Modal: RGB, Depth, and Semantic annotations
  • 🌦️ Adverse Weather: Sunny, Rainy, Foggy, and Night conditions
  • 🔄 Synthetic + Real: CARLA-generated synthetic data + augmented real imagery
  • 📊 Comprehensive Benchmarks: Complete evaluation suite with domain adaptation

Applications

  • 🔍 Semantic Segmentation: Urban scene understanding from aerial perspectives
  • 🌦️ Adverse Weather Analysis: Robust perception in challenging conditions
  • 🔄 Domain Adaptation: Bridging synthetic-to-real domain gaps
  • 🚁 UAV Navigation: Autonomous drone navigation in urban environments
  • 📊 Benchmark Studies: Standardized evaluation of aerial perception models

Synthetic images examples

Real images examples

BibTeX

@misc{barbato2025flyawarev2multimodalcrossdomainuav,
      title={FlyAwareV2: A Multimodal Cross-Domain UAV Dataset for Urban Scene Understanding},
      author={Francesco Barbato and Matteo Caligiuri and Pietro Zanuttigh},
      year={2025},
      eprint={2510.13243},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2510.13243},
}