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---
license: mit
language:
- en
---
<div align="center">
# SkyScenes: A Synthetic Dataset for Aerial Scene Understanding
[Sahil Khose](https://sahilkhose.github.io/)\*, [Anisha Pal](https://anipal.github.io/)\*, [Aayushi Agarwal](https://www.linkedin.com/in/aayushiag/)\*, [Deepanshi Deepanshi](https://www.linkedin.com/in/deepanshi-d/)\*, [Judy Hoffman](https://faculty.cc.gatech.edu/~judy/), [Prithvijit Chattopadhyay](https://prithv1.xyz/)
</div>
<!-- This repository is the official Pytorch implementation for [SkyScenes](). -->
[](https://huggingface.co/datasets/hoffman-lab/SkyScenes) []() []()
<!-- [](./assets/robust_aerial_videos.mp4) -->
<img src="./assets/skyscene_intro_teaser.png" width="100%"/>
## 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.
<details>
<summary>Click to view the detailed list of all variations</summary>
- **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°
</details>
<details>
<summary>Click to view class definitions, color palette and class IDs for Semantic Segmentation</summary>
**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 | <span style="color:rgb(0, 0, 0)"> (0, 0, 0) </span> | unlabeled | Elements/objects in the scene that have not been categorized |
| 1 | 2 | <span style="color:rgb(70, 70, 70)"> (70, 70, 70) </span> | building | Includes houses, skyscrapers, and the elements attached to them |
| 2 | 4 | <span style="color:rgb(190, 153, 153)"> (190, 153, 153) </span> | fence | Wood or wire assemblies that enclose an area of ground |
| 3 | -1 | <span style="color:rgb(55, 90, 80)"> (55, 90, 80) </span> | other | Uncategorized elements |
| 4 | 11 | <span style="color:rgb(220, 20, 60)"> (220, 20, 60) </span> | pedestrian | Humans that walk |
| 5 | 5 | <span style="color:rgb(153, 153, 153)"> (153, 153, 153) </span> | pole | Vertically oriented pole and its horizontal components if any |
| 6 | 16 | <span style="color:rgb(157, 234, 50)"> (157, 234, 50) </span> | roadline | Markings on road |
| 7 | 0 | <span style="color:rgb(128, 64, 128)"> (128, 64, 128) </span> | road | Lanes, streets, paved areas on which cars drive |
| 8 | 1 | <span style="color:rgb(244, 35, 232)"> (244, 35, 232) </span> | sidewalk | Parts of ground designated for pedestrians or cyclists |
| 9 | 8 | <span style="color:rgb(107, 142, 35)"> (107, 142, 35) </span> | vegetation | Trees, hedges, all kinds of vertical vegetation (ground-level vegetation is not included here) |
| 10 | 13 | <span style="color:rgb(0, 0, 142)"> (0, 0, 142) </span> | cars | Cars in scene |
| 11 | 3 | <span style="color:rgb(102, 102, 156)"> (102, 102, 156) </span> | wall | Individual standing walls, not part of buildings |
| 12 | 7 | <span style="color:rgb(220, 220, 0)"> (220, 220, 0) </span> | traffic sign | Signs installed by the state/city authority, usually for traffic regulation |
| 13 | 10 | <span style="color:rgb(70, 130, 180)"> (70, 130, 180) </span> | sky | Open sky, including clouds and sun |
| 14 | -1 | <span style="color:rgb(81, 0, 81)"> (81, 0, 81) </span> | ground | Any horizontal ground-level structures that do not match any other category |
| 15 | -1 | <span style="color:rgb(150, 100, 100)"> (150, 100, 100) </span> | bridge | The structure of the bridge |
| 16 | -1 | <span style="color:rgb(230, 150, 140)"> (230, 150, 140) </span> | railtrack | Rail tracks that are non-drivable by cars |
| 17 | -1 | <span style="color:rgb(180, 165, 180)"> (180, 165, 180) </span> | guardrail | Guard rails / crash barriers |
| 18 | 6 | <span style="color:rgb(250, 170, 30)"> (250, 170, 30) </span> | traffic light | Traffic light boxes without their poles |
| 19 | -1 | <span style="color:rgb(110, 190, 160)"> (110, 190, 160) </span> | static | Elements in the scene and props that are immovable |
| 20 | -1 | <span style="color:rgb(170, 120, 50)"> (170, 120, 50) </span> | dynamic | Elements whose position is susceptible to change over time |
| 21 | 19 | <span style="color:rgb(45, 60, 150)"> (45, 60, 150) </span> | water | Horizontal water surfaces |
| 22 | 9 | <span style="color:rgb(152, 251, 152)"> (152, 251, 152) </span> | terrain | Grass, ground-level vegetation, soil, or sand |
| 23 | 12 | <span style="color:rgb(255, 0, 0)"> (255, 0, 0) </span> | rider | Humans that ride/drive any kind of vehicle or mobility system |
| 24 | 18 | <span style="color:rgb(119, 11, 32)"> (119, 11, 32) </span> | bicycle | Bicycles in scenes |
| 25 | 17 | <span style="color:rgb(0, 0, 230)"> (0, 0, 230) </span> | motorcycle | Motorcycles in scene |
| 26 | 15 | <span style="color:rgb(0, 60, 100)"> (0, 60, 100) </span> | bus | Buses in scenes |
| 27 | 14 | <span style="color:rgb(0, 0, 70)"> (0, 0, 70) </span> | truck | Trucks in scenes |
|
</details>
## Dataset Structure
The dataset is organized in the following structure:
<!--<details>
<summary><strong>Images (RGB Images)</strong></summary>
- ***H_15_P_0***
- *ClearNoon*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- *ClearSunset*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- *ClearNight*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- *CloudyNoon*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- *MidRainyNoon*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- ***H_15_P_45***
- ...
- ...
- ***H_60_P_90***
- ...
</details>
<details>
<summary><strong>Instance (Instance Segmentation Annotations)</strong></summary>
- ***H_35_P_45***
- *ClearNoon*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
</details>
<details>
<summary><strong>Segment (Semantic Segmentation Annotations)</strong></summary>
- ***H_15_P_0***
- *ClearNoon*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- ***H_15_P_45***
- ...
- ...
- ***H_60_P_90***
</details>
<details>
<summary><strong>Depth (Depth Annotations)</strong></summary>
- ***H_35_P_45***
- *ClearNoon*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
</details>
-->
```
├── Images (RGB Images)
├── H_15_P_0
│ ├── ClearNoon
│ │ ├── Town01.tar.gz
│ │ ├── Town02.tar.gz
│ │ ├── ...
│ │ └── Town10HD.tar.gz
│ ├── ClearSunset
│ │ ├── Town01.tar.gz
│ │ ├── Town02.tar.gz
│ │ ├── ...
│ │ └── Town10HD.tar.gz
│ ├── ClearNight
│ │ ├── Town01.tar.gz
│ │ ├── Town02.tar.gz
│ │ ├── ...
│ │ └── Town10HD.tar.gz
│ ├── CloudyNoon
│ │ ├── Town01.tar.gz
│ │ ├── Town02.tar.gz
│ │ ├── ...
│ │ └── Town10HD.tar.gz
│ └── MidRainyNoon
│ ├── Town01.tar.gz
│ ├── Town02.tar.gz
│ ├── ...
│ └── Town10HD.tar.gz
├── H_15_P_45
│ └── ...
├── ...
└── H_60_P_90
└── ...
└── Instance (Instance Segmentation Annotations)
├── H_35_P_45
│ └── ClearNoon
│ ├── Town01.tar.gz
│ ├── Town02.tar.gz
│ ├── ...
│ └── Town10HD.tar.gz
└── ...
└── Segment (Semantic Segmentation Annotations)
├── H_15_P_0
│ ├── ClearNoon
│ │ ├── Town01.tar.gz
│ │ ├── Town02.tar.gz
│ │ ├── ...
│ │ └── Town10HD.tar.gz
│ ├── H_15_P_45
│ │ └── ...
│ ├── ...
│ └── H_60_P_90
│ └── ...
└── ...
└── Depth (Depth Annotations)
├── H_35_P_45
│ └── ClearNoon
│ ├── Town01.tar.gz
│ ├── Town02.tar.gz
│ ├── ...
│ └── 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.
|