Datasets:

yubaiscat
/

MARS20

pretty_name: MARS20
license: afl-3.0
task_categories:
  - object-detection
  - image-to-text
language:
  - en
tags:
  - remote-sensing
  - aerial-imagery
  - airport
  - aircraft
  - fine-grained-recognition
  - keypoint-detection
  - image-captioning
  - generative-data
annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
      - split: validation
        path: data/validation.parquet
      - split: test
        path: data/test.parquet

MARS20

MARS20 is a remote-sensing airport dataset for fine-grained aircraft understanding and controllable image generation. This Hugging Face release packages the official split subset as Parquet files with embedded RGB images, instance annotations, skeleton keypoints, and image-level captions.

It accompanies the paper AirplaneGen: Skeleton-Guided Generation of Remote Sensing Images with Multi-Instance Airplanes.

Authors: Lingxuan Zhu, Yanze Ma, Jiaji Wu, Yanbo Fan, Xiaobing Wang, Mingzhou Tan
Venue: Remote Sensing, 2026, 18(12):1940
DOI: https://doi.org/10.3390/rs18121940
Paper: https://www.mdpi.com/2072-4292/18/12/1940

At a Glance

2778 annotated images
16673 airplane instances
20 fine-grained aircraft categories
Bounding boxes in pixel coordinates
Skeleton keypoints in normalized coordinates
Image-level captions

This release includes only the officially split annotated subset. The original local workspace contains 32 extra RGB images without matching official annotations or split membership; they are listed in metadata/unused_images.json.

Splits

Split	Images	Objects
train	2563	15431
validation	107	566
test	108	676

Schema

Each example contains:

id: image identifier
split: dataset split
image: RGB image
width, height: image size
image_caption: image-level caption
background_caption: optional source background text
num_objects: number of airplane instances
plane_types: list of instance classes
objects: full instance annotations

Each item in objects contains:

plane_type
bbox: {xmin, ymin, xmax, ymax}
keypoints: list of {label, x, y}

Notes

Most instances contain 8 skeleton keypoints.
6 legacy instances contain 6 or 7 keypoints; see metadata/keypoint_anomalies.json.
Caption sources are mixed: DetailCaption, caption-multi.json, Caption-Background, and a small auto-generated subset.

Usage

from datasets import load_dataset

ds = load_dataset("your-username/MARS20")
sample = ds["train"][0]

print(sample["id"])
print(sample["plane_types"])
print(sample["objects"][0])

Files

data/*.parquet: train/validation/test splits with embedded images
metadata/summary.json: split stats and class counts
metadata/class_names.json: category names
metadata/unused_images.json: excluded source RGB images

Citation

@article{zhu2026airplanegen,
  author  = {Zhu, Lingxuan and Ma, Yanze and Wu, Jiaji and Fan, Yanbo and Wang, Xiaobing and Tan, Mingzhou},
  title   = {AirplaneGen: Skeleton-Guided Generation of Remote Sensing Images with Multi-Instance Airplanes},
  journal = {Remote Sensing},
  year    = {2026},
  volume  = {18},
  number  = {12},
  pages   = {1940},
  doi     = {10.3390/rs18121940},
  url     = {https://doi.org/10.3390/rs18121940}
}