Dataset Card for SkyScenes

This is a FiftyOne dataset with 280 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/SkyScenes")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

SkyScenes is a comprehensive synthetic dataset for aerial scene understanding that was recently accepted to ECCV 2024. The dataset contains 33,600 aerial images captured from UAV perspectives using the CARLA simulator.

The original repo on the Hub can be found here.

• Curated by: Sahil Khose, Anisha Pal, Aayushi Agarwal, Deepanshi, Judy Hoffman, Prithvijit Chattopadhyay
• Funded by: Georgia Institute of Technology
• Shared by: Harpreet Sahota, Hacker-in-Residence at Voxel51
• Language(s) (NLP): en
• License: MIT License

Dataset Structure

• Images: RGB images captured across multiple variations:

  • 8 different town layouts (7 urban + 1 rural)

  • 5 weather/time conditions (ClearNoon, ClearSunset, ClearNight, CloudyNoon, MidRainyNoon)

  • 12 viewpoint combinations (3 heights × 4 pitch angles)

Annotations

Each image comes with dense pixel-level annotations for:

• Semantic segmentation (28 classes)

• Instance segmentation

• Depth information

Key Variations

• Heights: 15m, 35m, 60m

• Pitch Angles: 0°, 45°, 60°, 90°

• Weather/Time: Various conditions to test robustness

• Layouts: Different urban and rural environments

NOTE: This repo contains only a subset of the full dataset:

• Heights & Pitch Angles:

  • H_15_P_0 (15m height, 0° pitch)

  • H_35_P_0 (35m height, 0° pitch)

  • H_60_P_0 (60m height, 0° pitch)

• Weather Condition: ClearNoon only

• Town Layouts: Town01, Town02, Town05, Town07

• Data Modalities:

  • RGB Images
  • Depth Maps
  • Semantic Segmentation

If you wish to work with the full dataset in FiftyOne format, you can use the following repo.

Dataset Sources

• Repository: https://github.com/hoffman-group/SkyScenes
• Paper: https://arxiv.org/abs/2312.06719
• Demo: https://hoffman-group.github.io/SkyScenes/

Uses

The dataset contains 33.6k densely annotated synthetic aerial images with comprehensive metadata and annotations, making it suitable for both training and systematic evaluation of aerial scene understanding models.

Training and Pre-training

• Functions as a pre-training dataset for real-world aerial scene understanding models
• Models trained on SkyScenes demonstrate strong generalization to real-world scenarios
• Can effectively augment real-world training data to improve overall model performance

Model Evaluation and Testing

Diagnostic Testing

• Serves as a test bed for assessing model sensitivity to various conditions including:
  • Weather changes
  • Time of day variations
  • Different pitch angles
  • Various altitudes
  • Different layout types

Multi-modal Development

• Enables development of multi-modal segmentation models by incorporating depth information alongside visual data
• Supports testing how additional sensor modalities can improve aerial scene recognition capabilities

Research Applications

• Enables studying synthetic-to-real domain adaptation for aerial imagery
• Provides controlled variations for analyzing model behavior under different viewing conditions
• Supports development of models for:
  • Semantic segmentation
  • Instance segmentation
  • Depth estimation

References

• SkyScenes Dataset on HuggingFace
• SkyScenes Official Website

Citation

@misc{khose2023skyscenes,
      title={SkyScenes: A Synthetic Dataset for Aerial Scene Understanding}, 
      author={Sahil Khose and Anisha Pal and Aayushi Agarwal and Deepanshi and Judy Hoffman and Prithvijit Chattopadhyay},
      year={2023},
      eprint={2312.06719},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}