| --- |
| 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. |
|
|
