ReCon1M

ReCon1M is a remote-sensing scene graph generation dataset derived from FAIR1M. This repository packages the local release as structured Hugging Face Parquet annotations while keeping PNG images as ordinary repository files.

The packaged release contains 22,262 images, 60 object classes, and 59 predicate classes:

Split	Images
train	11,131
validation	3,710
test	7,421

These counts describe the files in this packaged release. They differ from the counts in the paper abstract, which may describe another ReCon1M release.

Repository layout

images/<split>/<first-two-ID-characters>/<id>.png
data/...
README.md

The image column is a POSIX repository-relative path such as images/train/09/09868.png. It contains neither image bytes nor an automatic Pillow object. Most images use .png; the three JPEG-encoded source images use their corrected .jpeg filenames: 00024.jpeg, 00033.jpeg, and 00037.jpeg.

Schema

• id: original zero-padded image identifier.
• image: repository-relative PNG path.
• width, height: PNG dimensions.
• image_source, gsd: metadata from the object annotation header.
• polygons: one four-point polygon per object. Coordinates and vertex order are preserved; polygons are not converted to bounding boxes.
• labels: object ClassLabel values.
• difficult: original per-object difficulty integers.
• relations.subject_index, relations.object_index: zero-based indices into labels and polygons.
• relations.predicate: predicate ClassLabel values.

The source object and predicate vocabularies use one-based IDs. This package maps them to zero-based Hugging Face ClassLabel indices without changing their vocabulary order. No attributes field is added.

Download and open images

Install datasets, huggingface_hub, and Pillow, then download the repository snapshot and load its Parquet annotations:

from pathlib import Path

from datasets import load_dataset
from huggingface_hub import snapshot_download
from PIL import Image


repo_dir = Path(snapshot_download("wliafe/recon1m", repo_type="dataset"))
dataset = load_dataset("wliafe/recon1m")

sample = dataset["train"][0]
image_path = repo_dir / sample["image"]

with Image.open(image_path) as image:
    image.load()
    print(image.size)

OpenCV can read the same resolved path:

import cv2


image_bgr = cv2.imread(str(image_path), cv2.IMREAD_COLOR)
if image_bgr is None:
    raise RuntimeError(f"Failed to read {image_path}")

The complete snapshot includes roughly 32.6 GB of PNG files. Calling only load_dataset() downloads the Parquet annotations, not all image files.

For reproducible loading, pass the same revision to both calls:

revision = "<commit-sha>"
repo_dir = Path(
    snapshot_download(
        "wliafe/recon1m",
        repo_type="dataset",
        revision=revision,
    )
)
dataset = load_dataset("wliafe/recon1m", revision=revision)

Citation

If you use ReCon1M, cite the original paper:

@article{sun2024recon1m,
  title={ReCon1M: A Large-scale Benchmark Dataset for Relation Comprehension in Remote Sensing Imagery},
  author={Sun, Xian and Yan, Qiwei and Deng, Chubo and Liu, Chenglong and Jiang, Yi and Hou, Zhongyan and Lu, Wanxuan and Yao, Fanglong and Liu, Xiaoyu and Hao, Lingxiang and Yu, Hongfeng},
  journal={arXiv preprint arXiv:2406.06028},
  year={2024}
}

See the ReCon1M paper for the dataset methodology. Use of the images and annotations remains subject to the terms of the original ReCon1M and FAIR1M releases.