Datasets:

JunDong-dev
/

OptSAR-RSVG

license: other
license_name: optisar-rsvg-source-licenses
task_categories:
  - object-detection
language:
  - en
tags:
  - remote-sensing
  - visual-grounding
  - sar
  - optical
  - cross-domain
  - referring-expression-comprehension
  - benchmark
size_categories:
  - 10K<n<100K
pretty_name: OptSAR-RSVG

OptSAR-RSVG: A Large-Scale Benchmark for Cross-Domain Remote Sensing Visual Grounding

OptSAR-RSVG is a large-scale benchmark for Cross-Domain Remote Sensing Visual Grounding (CD-RSVG). It pairs optical and SAR remote sensing images with free-form natural-language expressions and axis-aligned bounding boxes, enabling a single model to localize referred targets in both optical and SAR domains.

It was introduced together with OptiSAR-Net++ (GitHub), a transformer-free framework for cross-domain remote sensing visual grounding.

📊 Statistics

Split	Images	Annotations (image–text–box)
Train	37,957	74,049
Validation	4,434	7,996
Test	4,434	8,103
Total	46,825	90,148

16 categories: 14 optical + 2 SAR (see table below)
Splits follow an ≈ 8 : 1 : 1 ratio with category- and modality-balanced sampling
Image resolutions vary by source: SAR images are mostly 256×256, optical images range up to 1024×1024
Each image carries a opt_ (optical) or sar (SAR) filename prefix, e.g. opt_000001.jpg / sar_000001.jpg

📁 Directory Structure

Note (Hugging Face layout): the Hub limits each directory to 10,000 files, so the large train split is stored in chunked subfolders (chunk_000 … chunk_007, ≈ 4,745 files each). val and test remain flat. Image xxx.jpg and its label xxx.txt always live under the same chunk index.

OptSAR-RSVG/
├── images/
│   ├── train/
│   │   ├── chunk_000/      # 4,745 images (opt_*.jpg / sar_*.jpg)
│   │   ├── ...
│   │   └── chunk_007/      # 4,742 images  → 37,957 in total
│   ├── val/                # 4,434 images
│   └── test/               # 4,434 images
├── labels/
│   ├── train/
│   │   ├── chunk_000/ … chunk_007/   # YOLO-format .txt, same stem & chunk as the image
│   ├── val/                # 4,434 labels
│   └── test/               # 4,434 labels
├── train.json              # COCO-style annotations (74,049 instances)
├── val.json                # COCO-style annotations (7,996 instances)
└── test.json               # COCO-style annotations (8,103 instances)

When loading, simply resolve a train file via its chunk, e.g. glob images/train/chunk_*/{file_name}, or build the index once with glob across chunks (the file_name in the JSONs is unchanged, e.g. sar_000001.jpg).

📝 Annotation Format

COCO JSON (`train.json` / `val.json` / `test.json`)

Standard COCO instances files with two extra fields per annotation — caption (the referring expression) and category_name:

{
  "id": 1,
  "image_id": 1,
  "category_id": 15,
  "bbox": [74.0, 58.0, 62.0, 123.0],
  "area": 7626.0,
  "iscrowd": 0,
  "ignore": 0,
  "segmentation": [[74.0, 58.0, 136.0, 58.0, 136.0, 181.0, 74.0, 181.0]],
  "category_name": "sar ship",
  "caption": "A massive sar ship is in the middle"
}

bbox is [x, y, width, height] in pixels, top-left origin.

YOLO text labels (`labels/`)

One .txt per image (same file stem), one object per line:

<class_id> <x_center> <y_center> <width> <height>
12 0.395047 0.414894 0.077830 0.090426

Coordinates are normalized to [0, 1]; class_id matches the COCO category_id below.

🏷️ Categories

ID	Name	ID	Name
0	optical vehicle	8	optical parking lot
1	optical bridge	9	optical basketball court
2	optical crossroad	10	optical storage tank
3	optical T junction	11	optical ship
4	optical ground track field	12	optical airplane
5	optical baseball diamond	13	optical harbor
6	optical swimming pool	14	sar transmission tower
7	optical tennis court	15	sar ship

🌱 Sources & Construction

OptSAR-RSVG was consolidated from four public datasets:

RSVGD / RSSVG — optical ships
OPT-RSVG — 14 optical categories
SARVG — SAR ships
TACMT / SARVG 1.0 — SAR transmission towers

Construction pipeline: (1) collection, cleaning and merging of source annotations; (2) data augmentation with GPT-4o-based text rewriting, adding 9,046 images and 19,136 annotations; (3) manual verification and balanced train/val/test splitting.

📏 Evaluation Protocol

Standard CD-RSVG metrics: Pr@0.5–Pr@0.9 (cumulative accuracy at IoU thresholds 0.5–0.9), meanIoU, and cumIoU, evaluated per domain (optical / sar) and overall.

🤖 Companion Model

A trained checkpoint of OptiSAR-Net++ evaluated on this benchmark is available at JunDong-dev/OptiSAR-Net-PlusPlus. Training/evaluation code: GitHub.

⚖️ License

This dataset aggregates several public remote sensing datasets. Its use is intended for research purposes and remains subject to the licenses and terms of the original source datasets listed above. The companion code is released under AGPL-3.0.

📚 Citation

If you use OptSAR-RSVG, please cite:

@article{tang2026optisar,
  title={OptiSAR-Net++: A Large-Scale Benchmark and Transformer-Free Framework for Cross-Domain Remote Sensing Visual Grounding},
  author={Tang, Xiaoyu and Dong, Jun and Cheng, Jintao and Fan, Rui},
  journal={arXiv preprint arXiv:2603.24876},
  year={2026}
}