README.md

metadata

license: mit
language:
  - en
task_categories:
  - object-detection
  - depth-estimation
  - image-segmentation
tags:
  - dataset
  - aerial
  - synthetic
  - domain adaptation
  - sim2real

SkyScenes: A Synthetic Dataset for Aerial Scene Understanding

Sahil Khose*, Anisha Pal*, Aayushi Agarwal*, Deepanshi*, Judy Hoffman, Prithvijit Chattopadhyay

Dataset Summary

Real-world aerial scene understanding is limited by a lack of datasets that contain densely annotated images curated under a diverse set of conditions. Due to inherent challenges in obtaining such images in controlled real-world settings, we present SkyScenes, a synthetic dataset of densely annotated aerial images captured from Unmanned Aerial Vehicle (UAV) perspectives. SkyScenes images are carefully curated from CARLA to comprehensively capture diversity across layout (urban and rural maps), weather conditions, times of day, pitch angles and altitudes with corresponding semantic, instance and depth annotations. SkyScenes features 33,600 images in total, which are spread across 8 towns, 5 weather and daytime conditions and 12 height and pitch variations.

Click to view the detailed list of all variations

• Layout Variations(Total 8)::
  • Town01
  • Town02
  • Town03
  • Town04
  • Town05
  • Town06
  • Town07
  • Town10HD

Town07 features Rural Scenes, whereas the rest of the towns feature Urban scenes

• Weather & Daytime Variations(Total 5):

  • ClearNoon
  • ClearSunset
  • ClearNight
  • CloudyNoon
  • MidRainyNoon

• Height and Pitch Variations of UAV Flight(Total 12):

  • Height = 15m, Pitch = 0°
  • Height = 15m, Pitch = 45°
  • Height = 15m, Pitch = 60°
  • Height = 15m, Pitch = 90°
  • Height = 35m, Pitch = 0°
  • Height = 35m, Pitch = 45°
  • Height = 35m, Pitch = 60°
  • Height = 35m, Pitch = 90°
  • Height = 60m, Pitch = 0°
  • Height = 60m, Pitch = 45°
  • Height = 60m, Pitch = 60°
  • Height = 60m, Pitch = 90°

Click to view class definitions, color palette and class IDs for Semantic Segmentation

SkyScenes semantic segmentation labels span 28 classes which can be further collapsed to 20 classes.

Class ID	Class ID (collapsed)	RGB Color Palette	Class Name	Definition
0	-1	(0, 0, 0)	unlabeled	Elements/objects in the scene that have not been categorized
1	2	(70, 70, 70)	building	Includes houses, skyscrapers, and the elements attached to them
2	4	(190, 153, 153)	fence	Wood or wire assemblies that enclose an area of ground
3	-1	(55, 90, 80)	other	Uncategorized elements
4	11	(220, 20, 60)	pedestrian	Humans that walk
5	5	(153, 153, 153)	pole	Vertically oriented pole and its horizontal components if any
6	16	(157, 234, 50)	roadline	Markings on road
7	0	(128, 64, 128)	road	Lanes, streets, paved areas on which cars drive
8	1	(244, 35, 232)	sidewalk	Parts of ground designated for pedestrians or cyclists
9	8	(107, 142, 35)	vegetation	Trees, hedges, all kinds of vertical vegetation (ground-level vegetation is not included here)
10	13	(0, 0, 142)	cars	Cars in scene
11	3	(102, 102, 156)	wall	Individual standing walls, not part of buildings
12	7	(220, 220, 0)	traffic sign	Signs installed by the state/city authority, usually for traffic regulation
13	10	(70, 130, 180)	sky	Open sky, including clouds and sun
14	-1	(81, 0, 81)	ground	Any horizontal ground-level structures that do not match any other category
15	-1	(150, 100, 100)	bridge	The structure of the bridge
16	-1	(230, 150, 140)	railtrack	Rail tracks that are non-drivable by cars
17	-1	(180, 165, 180)	guardrail	Guard rails / crash barriers
18	6	(250, 170, 30)	traffic light	Traffic light boxes without their poles
19	-1	(110, 190, 160)	static	Elements in the scene and props that are immovable
20	-1	(170, 120, 50)	dynamic	Elements whose position is susceptible to change over time
21	19	(45, 60, 150)	water	Horizontal water surfaces
22	9	(152, 251, 152)	terrain	Grass, ground-level vegetation, soil, or sand
23	12	(255, 0, 0)	rider	Humans that ride/drive any kind of vehicle or mobility system
24	18	(119, 11, 32)	bicycle	Bicycles in scenes
25	17	(0, 0, 230)	motorcycle	Motorcycles in scene
26	15	(0, 60, 100)	bus	Buses in scenes
27	14	(0, 0, 70)	truck	Trucks in scenes

                                                                            |

Dataset Structure

The dataset is organized in the following structure:

├── Images (RGB Images)
│   ├── H_15_P_0
│   │   ├── ClearNoon
│   │   │   ├── Town01
│   │   │   │   └── Town01.tar.gz
│   │   │   ├── Town02
│   │   │   │   └── Town02.tar.gz
│   │   │   ├── ...
│   │   │   └── Town10HD
│   │   │       └── Town10HD.tar.gz
│   │   ├── ClearSunset
│   │   │   ├── Town01
│   │   │   │   └── Town01.tar.gz
│   │   │   ├── Town02
│   │   │   │   └── Town02.tar.gz
│   │   │   ├── ...
│   │   │   └── Town10HD
│   │   │       └── Town10HD.tar.gz
│   │   ├── ClearNight
│   │   │   ├── Town01
│   │   │   │   └── Town01.tar.gz
│   │   │   ├── Town02
│   │   │   │   └── Town02.tar.gz
│   │   │   ├── ...
│   │   │   └── Town10HD
│   │   │       └── Town10HD.tar.gz
│   │   ├── CloudyNoon
│   │   │   ├── Town01
│   │   │   │   └── Town01.tar.gz
│   │   │   ├── Town02
│   │   │   │   └── Town02.tar.gz
│   │   │   ├── ...
│   │   │   └── Town10HD
│   │   │       └── Town10HD.tar.gz
│   │   └── MidRainyNoon
│   │       ├── Town01
│   │       │   └── Town01.tar.gz
│   │       ├── Town02
│   │       │   └── Town02.tar.gz
│   │       ├── ...
│   │       └── Town10HD
│   │           └── Town10HD.tar.gz
│   ├── H_15_P_45
│   │   └── ...
│   ├── ...
│   └── H_60_P_90
│       └── ...
├── Instance (Instance Segmentation Annotations)
│   ├── H_35_P_45
│   │   └── ClearNoon
│   │       ├── Town01
│   │       │   └── Town01.tar.gz
│   │       ├── Town02
│   │       │   └── Town02.tar.gz
│   │       ├── ...
│   │       └── Town10HD
│   │           └── Town10HD.tar.gz
│   └── ...
├── Segment (Semantic Segmentation Annotations)
│   ├── H_15_P_0
│   │   ├── ClearNoon
│   │   │   ├── Town01
│   │   │   │   └── Town01.tar.gz
│   │   │   ├── Town02
│   │   │   │   └── Town02.tar.gz
│   │   │   ├── ...
│   │   │   └── Town10HD
│   │   │       └── Town10HD.tar.gz
│   │   ├── H_15_P_45
│   │   │   └── ...
│   │   ├── ...
│   │   └── H_60_P_90
│   │       └── ...
│   └── ...
└── Depth (Depth Annotations)
    ├── H_35_P_45
    │   └── ClearNoon 
    │       ├── Town01
    │       │   └── Town01.tar.gz
    │       ├── Town02
    │       │   └── Town02.tar.gz
    │       ├── ...
    │       └── Town10HD
    │           └── Town10HD.tar.gz
    └── ...

Note: Since the same viewpoint is reproduced across each weather variation, hence ClearNoon annotations can be used for all images pertaining to the different weather variations.

Dataset Download

The dataset can be downloaded using both datasets library by Hugging Face and wget. Since SkyScenes offers variations across different axes we enable different subsets for download that can aid in model sensitivity analysis across these axes.

Download instructions: wget

Example script for downloading different subsets of data using wget

#!/bin/bash
#Change here to download a specific Height and Pitch Variation, for example - H_15_P_0
# HP=('H_15_P_45' 'H_15_P_60' 'H_15_P_90')
HP=('H_15_P_0' 'H_15_P_45' 'H_15_P_60' 'H_15_P_90' 'H_35_P_0' 'H_35_P_45' 'H_35_P_60' 'H_35_P_90' 'H_60_P_0' 'H_60_P_45' 'H_60_P_60' 'H_60_P_90')

#Change here to download a specific weather subset, for example - ClearNoon
#Note - For Segment, Instance and Depth annotations this field should only have ClearNoon variation
# weather=('ClearNoon' 'ClearNight')
weather=('ClearNoon' 'ClearNight' 'ClearSunset' 'CloudyNoon' 'MidRainyNoon')

#Change here to download a specific Town subset, for example - Town07
layout=('Town01' 'Town02' 'Town03' 'Town04' 'Town05' 'Town06' 'Town07' 'Town10HD')

#Change here for any specific annotation, for example - https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Segment
base_url=('https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images')

#Change here for base download folder
base_download_folder='SkyScenes'


for hp in "${HP[@]}"; do
  for w in "${weather[@]}"; do
      for t in "${layout[@]}"; do
        folder=$(echo "$base_url" | awk -F '/' '{print $(NF)}')
        download_url="${base_url}/${hp}/${w}/${t}/${t}.tar.gz"
        download_folder="${base_download_folder}/${folder}/${hp}/${w}/${t}"
        mkdir -p "$download_folder"
        echo "Downloading: $download_url"
        wget -P "$download_folder" "$download_url"
      done
  done
done

Download instructions: datasets

Click to view all the available keys for downloading subsets of the data

• Layout Variations

  • Rural
  • Urban

• Weather Variations

  • ClearNoon
  • ClearNight (only images)
  • ClearSunset (only images)
  • CloudyNoon (only images)
  • MidRainyNoon (only images)

• Height Variations

  • H_15
  • H_35
  • H_60

• Pitch Variations

  • P_0
  • P_45
  • P_60
  • P_90

• Height and Pitch Variations

  • H_15_P_0
  • H_15_P_45
  • H_15_P_60
  • H_15_P_90
  • H_35_P_0
  • H_35_P_45
  • H_35_P_60
  • H_35_P_90
  • H_60_P_0
  • H_60_P_45
  • H_60_P_60
  • H_60_P_90

  Full dataset key: full

  💡Notes:

  • To download images append subset key with images, example - H_35_P_45 images
  • To download semantic segmentation maps append subset key with semseg, example - H_35_P_45 semseg
  • To download instance segmentation maps append subset key with instance, example - H_35_P_45 instance
  • To download depth maps append subset key with depth, example - H_35_P_45 depth

Example script for loading H_35_P_45 images

from datasets import load_dataset
dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 images")

Example script for loading H_35_P_45 semantic segmentation maps

from datasets import load_dataset
dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 semseg")

Example script for loading H_35_P_45 instance segmentation maps

from datasets import load_dataset
dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 instance")

Example script for loading H_35_P_45 depth maps

from datasets import load_dataset
dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 depth")

💡 Notes

• To prevent issues when loading datasets using datasets library, it is recommended to avoid downloading subsets that contain overlapping directories. If there are any overlapping directories between the existing downloads and new ones, it's essential to clear the .cache directory of any such overlaps before proceeding with the new downloads. This step will ensure a clean and conflict-free environment for handling datasets.

BibTex

If you find this work useful please like ❤️ our dataset repo and cite 📄 our paper. Thanks for your support!

  @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}
}