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.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+metadata.jsonl filter=lfs diff=lfs merge=lfs -text
+Relational[[:space:]]reasoning/proximity/proximity.json filter=lfs diff=lfs merge=lfs -text

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README.md

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+---
+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
+---
+
+# 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.

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+}

dataset_manifest.json

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