license: mit
language:
- en
SkyScenes: A Synthetic Dataset for Aerial Scene Understanding
Sahil Khose*, Anisha Pal*, Aayushi Agarwal*, Deepanshi 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.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
- ClearNoon
- 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
- ClearNoon
- H_35_P_45
- Segment (Semantic Segmentation Annotations)
- H_15_P_0
- ClearNoon
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- ClearNoon
- H_15_P_45
- ...
- ...
- H_60_P_90
- H_15_P_0
- Depth (Depth Annotations)
- H_35_P_45
- ClearNoon
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- ClearNoon
- H_35_P_45
- H_15_P_0
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