--- license: mit language: - en ---
# 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/)
[![HuggingFace Dataset](https://img.shields.io/badge/🤗-HuggingFace%20Dataset-cyan.svg)](https://huggingface.co/datasets/hoffman-lab/SkyScenes) [![Project Page](https://img.shields.io/badge/Project-Website-orange)]() [![arXiv](https://img.shields.io/badge/arXiv-SkyScenes-b31b1b.svg)]() ## 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.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.