.png){kind=icon}
.png){kind=icon}
Return
FlyAwareV2: A multimodal cross-domain UAV dataset for urban scene understanding
.png){kind=icon}
OA
AI
P
M
P
DOI：10.1016/j.image.2026.117483.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. Dataset link: https://medialab.dei.unipd.it/paper_data/FlyAwareV2
Keywords:
Semantic segmentation
Aerial imaging
Depth data
Multimodal scene understanding
Unsupervised domain adaptation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
S
IF:
2.7
Papers:
18
Citations:
0
Organization
Cited Papers