Datasets:

dronefreak
/

Aeroscapes

---
language:
- en

license: cc-by-sa-4.0

pretty_name: Aeroscapes Semantic Segmentation Dataset

task_categories:
- image-segmentation

task_ids:
- semantic-segmentation

tags:
- semantic-segmentation
- aerial-imagery
- uav
- drone
- remote-sensing
- computer-vision
- ultralytics
- yolo

size_categories:
- 1K<n<10K

configs:
- config_name: default
  data_files:
  - split: train
    path: "train_metadata.jsonl"
  - split: validation
    path: "val_metadata.jsonl"
---

# Aeroscapes: Aerial Semantic Segmentation Dataset

<p align="center">
  <img src="aeroscapes_banner.jpg" alt="AeroScapes Dataset Banner — RGB (left) and colorized ground truth (right) for two sample scenes"/>
</p>

![Task](https://img.shields.io/badge/Task-Semantic%20Segmentation-blue?style=flat-square)
![Dataset](https://img.shields.io/badge/Dataset-AeroScapes-0aa1a7?style=flat-square)
![Format](https://img.shields.io/badge/Format-YOLO%20Compatible-orange?style=flat-square)
![Classes](https://img.shields.io/badge/Classes-12-success?style=flat-square)
![Splits](https://img.shields.io/badge/Splits-Train%20%7C%20Val-blueviolet?style=flat-square)
![License](https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey?style=flat-square)

> **Unofficial redistribution of the AeroScapes dataset under the original CC BY-SA 4.0 license.**

## Disclaimer

This repository is **not** an official release of the AeroScapes dataset.

The AeroScapes dataset was created by Ishan Nigam, Chen Huang, and Deva Ramanan, with dataset
collection and manual annotation supported by Autel Robotics. They retain all copyright and
intellectual property rights. This repository does **not** claim ownership of any images,
annotations, or metadata.

This repository exists for two purposes:

1. To reorganize the original dataset into a standardized YOLO/Ultralytics-compatible directory
   structure (also consumable by [CABiNet](https://github.com/dronefreak/CABiNet)) that can be used
   directly by many modern semantic segmentation training pipelines.
2. To provide a more convenient download source, as the original is distributed as a single
   Google Drive archive.

---

# Dataset Description

AeroScapes is a semantic segmentation benchmark captured from low-altitude unmanned aerial
vehicles (UAVs), acquired at altitudes between 5 and 50 meters. The dataset contains 3,269
720p (1280x720) images with pixel-level semantic annotations across 12 classes (11 foreground
classes plus background), covering people, vehicles, and both urban and natural terrain.

This repository preserves the original dataset while packaging it in a standardized directory
layout for improved compatibility with modern deep learning frameworks.

---

# Changes from the Official Release

This repository **does not modify the dataset contents.**

The following changes have been made:

- Reorganized the directory structure into a YOLO/Ultralytics-compatible `images/`+`masks/` layout.
- Added a `data.yaml` configuration file for easier integration with Ultralytics-based projects.
- Preserved the original train and validation splits exactly as released (`ImageSets/trn.txt` and
  `val.txt`) — the official AeroScapes release does not define a separate test split.
- Preserved all original filenames.
- Preserved all original images.
- Preserved all original segmentation masks — the official `SegmentationClass/` masks are already
  single-channel, class-ID-encoded PNGs, so this is a lossless copy/re-layout, not a re-annotation
  or re-encoding.
- No labels were changed.
- No samples were added or removed.

Apart from the directory organization and configuration file, the dataset contents are identical
to the official release.

---

# Dataset Structure

```text
dataset/
├── README.md
├── data.yaml
├── images/
│   ├── train/    # 2,621 images
│   └── val/      # 648 images
└── masks/
    ├── train/
    └── val/
```

where:

* `images/` contains the original RGB UAV images organized by dataset split.
* `masks/` contains the corresponding single-channel semantic segmentation masks (pixel value =
  class ID, 0-11) for each split.
* `data.yaml` is a configuration file added in this repository to simplify loading the dataset in
  Ultralytics-compatible training pipelines.
* The original train/validation splits have been preserved exactly as released by the AeroScapes
  authors. There is no official test split.

Each image in `images/<split>/` has a corresponding segmentation mask with the same filename
(different extension) in `masks/<split>/`.

## Classes

| ID | Class        | ID | Class      |
| -- | ------------ | -- | ---------- |
| 0  | Background   | 6  | Animal     |
| 1  | Person       | 7  | Obstacle   |
| 2  | Bike         | 8  | Construction |
| 3  | Car          | 9  | Vegetation |
| 4  | Drone        | 10 | Road       |
| 5  | Boat         | 11 | Sky        |

All 12 classes are valid and used in both training and evaluation; none are mapped to an ignore
label. Pixel value `255` is reserved for genuinely unrecognized values (none are expected in a
clean copy of this dataset).

---

# Dataset Sources

## Original Paper

**Ensemble Knowledge Transfer for Semantic Segmentation**

Ishan Nigam, Chen Huang, Deva Ramanan

2018 IEEE Winter Conference on Applications of Computer Vision (WACV)

---

## Official Resources

- **Official Repository:** https://github.com/ishann/aeroscapes
- **Dataset Download (Google Drive):** https://drive.google.com/file/d/1WmXcm0IamIA0QPpyxRfWKnicxZByA60v/view?usp=sharing

---

# Attribution

**All credit for the dataset belongs entirely to the original AeroScapes authors and Autel
Robotics** (dataset collection and manual annotation support).

This repository only redistributes the original dataset under the same license while reorganizing
the directory structure for improved usability and accessibility.

If you use this dataset in your research, **please cite the original publication below.**

---

# License

The original AeroScapes dataset is distributed under the **Creative Commons
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)** license.

Accordingly:

- Attribution to the original authors is required.
- Commercial use is permitted.
- Any derivative work must be distributed under the same license.

This repository is distributed under the same license.

---

# Citation

If you use this dataset, please cite:

```bibtex
@inproceedings{nigam2018ensemble,
  author    = {Ishan Nigam and Chen Huang and Deva Ramanan},
  title     = {Ensemble Knowledge Transfer for Semantic Segmentation},
  booktitle = {2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year      = {2018}
}
```

---

# Acknowledgements

We sincerely thank Ishan Nigam, Chen Huang, and Deva Ramanan for creating and publicly releasing
this valuable benchmark, and Autel Robotics for supporting its collection and annotation — this
work has significantly contributed to research in semantic segmentation for UAV imagery.