Datasets:
Tasks:
Image Segmentation
Formats:
json
Sub-tasks:
semantic-segmentation
Languages:
English
Size:
1K - 10K
License:
| 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> | |
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| > **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. | |