---
license: cc-by-nc-4.0
language:
- en
pretty_name: RSFaith-Bench
size_categories:
- 10K<n<100K
task_categories:
- visual-question-answering
tags:
- remote-sensing
- vision-language
- benchmark
- scene-graph
- multiple-choice
- geospatial
configs:
- config_name: default
  data_files:
  - split: benchmark
    path: metadata.jsonl
---

# RSFaith-Bench

RSFaith-Bench is a remote-sensing vision-language benchmark designed to
evaluate grounded visual reasoning beyond surface-level object recognition.
The benchmark covers perception, relational reasoning, and temporal reasoning
over remote-sensing imagery. Each example is formulated as a multiple-choice
question and is paired with a compact scene graph, supporting evidence, and an
executable reasoning program.

The release contains 13,511 question-answer records, 16,288 referenced images,
and 12,876 compact scene graphs.

## Using the Dataset

The annotation files can be loaded directly as JSON. The root-level
`metadata.jsonl` provides a flat index over all records:

```python
from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files={"benchmark": "metadata.jsonl"},
    split="benchmark",
)
print(dataset[0])
```

Images and scene graphs are stored in per-subcategory archives. To restore the
file layout referenced by the JSON records, extract the archives in place:

```bash
huggingface-cli download <namespace>/RSFaith-Bench \
  --repo-type dataset \
  --local-dir RSFaith-Bench

find RSFaith-Bench -name assets.tar.zst -print0 |
  while IFS= read -r -d '' archive; do
    (cd "$(dirname "$archive")" && tar -I zstd -xf assets.tar.zst)
  done
```

After extraction, the `images` and `scene_graph` fields in each subcategory
JSON file resolve relative to that subcategory directory. The `image_t1`,
`image_t2`, and `scene_graph` fields in `metadata.jsonl` resolve relative to
the repository root.

## File Organization

The dataset is organized by reasoning level and subcategory. Each subcategory
directory contains:

- `<subcategory>.json`: question-answer records for the subcategory.
- `assets.tar.zst`: compressed `images/` and `scene_graphs/` directories.

```
RSFaith-Bench
├── README.md
├── metadata.jsonl
├── dataset_manifest.json
├── croissant.json
├── Perception
│   ├── object_presence
│   │   ├── object_presence.json
│   │   └── assets.tar.zst
│   ├── object_counting
│   │   ├── object_counting.json
│   │   └── assets.tar.zst
│   ├── fine_grained_recognition
│   │   ├── fine_grained_recognition.json
│   │   └── assets.tar.zst
│   └── object_localization
│       ├── object_localization.json
│       └── assets.tar.zst
├── Relational reasoning
│   ├── directional
│   │   ├── directional.json
│   │   └── assets.tar.zst
│   ├── topological
│   │   ├── topological.json
│   │   └── assets.tar.zst
│   ├── proximity
│   │   ├── proximity.json
│   │   └── assets.tar.zst
│   ├── projective_ordering
│   │   ├── projective_ordering.json
│   │   └── assets.tar.zst
│   └── aggregate_distribution
│       ├── aggregate_distribution.json
│       └── assets.tar.zst
└── Temporal reasoning
    ├── category_turnover
    │   ├── category_turnover.json
    │   └── assets.tar.zst
    ├── net_change
    │   ├── net_change.json
    │   └── assets.tar.zst
    └── semantic_transition
        ├── semantic_transition.json
        └── assets.tar.zst
```

## Data Fields

Each question-answer record contains the following fields:

- `question_id`: anonymized question identifier.
- `scene_id`: anonymized scene identifier.
- `level`: high-level reasoning category.
- `subcategory`: fine-grained reasoning category.
- `question`: natural-language question.
- `answer`: correct answer.
- `answer_type`: answer representation.
- `choices`: multiple-choice options.
- `images`: relative image paths.
- `scene_graph`: relative scene graph path.
- `support`: grounded support evidence.
- `program`: executable reasoning specification.

## Dataset Statistics

| Level | Subcategory | Records |
| --- | --- | ---: |
| Perception | object_presence | 969 |
| Perception | object_counting | 1,085 |
| Perception | fine_grained_recognition | 1,228 |
| Perception | object_localization | 1,298 |
| Relational reasoning | directional | 1,166 |
| Relational reasoning | topological | 921 |
| Relational reasoning | proximity | 987 |
| Relational reasoning | projective_ordering | 944 |
| Relational reasoning | aggregate_distribution | 866 |
| Temporal reasoning | category_turnover | 1,515 |
| Temporal reasoning | net_change | 1,273 |
| Temporal reasoning | semantic_transition | 1,259 |

## Dataset Construction

RSFaith-Bench is constructed from remote-sensing scenes represented as grounded scene graphs. The scene graphs encode objects, spatial relations, temporal
changes, and compact global inventories when applicable. Question-answer pairs
are generated from programmatic templates and then curated to balance reasoning
categories, answer distributions, and scene coverage. The released records
retain the reasoning support and program specification so that each answer can
be traced back to the corresponding scene graph.

## Licensing

The dataset is released under the [Creative Commons Attribution Non Commercial 4.0](https://creativecommons.org/licenses/by-nc/4.0/deed.en), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## Citation Information

If you use RSFaith-Bench in your research, please cite the accompanying paper:

```bibtex
@misc{rsfaithbench2026,
  title        = {RSFaith-Bench: When Correct Answers Come with Unfaithful Evidence in Remote Sensing MLLMs},
  author       = {Anonymous},
  year         = {2026}
}
```

## Acknowledgement

RSFaith-Bench is built from remote-sensing data sources including
[DIOR](https://gcheng-nwpu.github.io/),
[DOTA](https://captain-whu.github.io/DOTA/dataset.html),
[FAIR1M](https://www.gaofen-challenge.com/benchmark),
[SECOND](https://github.com/GeoZcx/A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images/tree/master/dataset),
[xBD](https://xview2.org/dataset), and
[ReCon1M](https://arxiv.org/abs/2406.06028). We thank the creators and
maintainers of these datasets for making their resources available to the
research community.