TopAir: Light-Weight Aerial dataset

This dataset was collected with a nadir view (top view) using AirSim simulator in various outdoor environments (both rural and urban). It contains video frames collected in differet trajectories along with annotation of depth maps and semantic segmentation maps. The extrinsic camera locations corresponding to each frame are also provided. TopAir was collected at low, mid, and high altitudes varying from 10 to 100 meters above the ground. This is a light-weight dataset with an image resolution of 384x384 and ~7K total number of frames.

Dataset Details

The dataset was collected using different environments in UnrealEngine4 integrated with AirSim simulator. These environments are:

• Africa (3 trajectories)
• City Park (7 trajectories)
• Oak Forest (3 trajectories)
• Rural Australia (4 trajectories)
• Assets Ville Town (5 trajectories)
• Accustic Cities (3 trajectories)
• Downtown City (5 trajectories)
• Edith Finch (2 trajectories)
• Landscape Mountains (1 trajectory)
• Nature Pack (1 trajectory)

The semantic segmentation maps of TopAir contain 8 classes:

Class ID	RGB Color Palette	Class Name	Definition
1	(75, 163, 185)	Water	Includes all water surfaces
2	(50, 128, 0)	Trees	all trees and vegetation above the ground
3	(232, 250, 80)	Land	all ground types (dirt ground, rocky ground, ground vegetation, sand, etc)
4	(237, 125, 49)	Vehicle	all types of ground vehicles (car, trucks, ..)
5	(70, 70, 70)	Rocks	boulders and rocks
6	(142, 1, 246)	Road	Lanes, streets, paved areas on which cars drive
7	(255, 128, 128)	Building	buildings, residential houses, and constructions
8	(128, 64, 64)	Others	any other objects not incuded in the above classes

Per-class pixel distribution: .....

To convert depth maps to their corresponding meter values: depth value (meter) = pixel value*100.0/255.0

The reference frames convention used in TopAir:

Dataset Description

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Uses

This dataset can be used in monocular depth estimation and semantic segmentation tasks concrened with aerial applications in outdoor environments.

Direct Use

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Dataset Structure

[More Information Needed] The dataset is organized as follows:

├── AccuCities_1 (environmentName_trajectoryNum)
│   ├── depth (depth maps annotation)
│   ├── images (RGB frames)
│   ├── seg_colored (segmentation maps in colors)
│   ├── seg_id (segmentation maps represented with class ids)
│   └── camera_loc.txt (camera locations and orientations)
│   
├── AccuCities_2
│   ├── depth
│   ├── images
│   ├── seg_colored
│   ├── seg_id
│   └── camera_loc.txt
│
├── AccuCities_2
│   ├── depth
│   ├── images
│   ├── seg_colored
│   ├── seg_id
│   └── camera_loc.txt
│
├── ....
│
└── RuralAust3_2
    ├── depth
    ├── images
    ├── seg_colored
    ├── seg_id
    └── camera_loc.txt

Dataset Creation

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Source Data

Data Collection and Processing

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Annotation process

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Bias, Risks, and Limitations

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Recommendations

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