Datasets:

TerraSenseAnalytics
/

RAVEN-Bench

---
pretty_name: RAVEN-Bench — Paired EO/IR Video Understanding
language:
- en
license: other
license_name: terrasense-data-release-terms
license_link: https://huggingface.co/datasets/TerraSenseAnalytics/RAVEN-Bench/blob/main/DATA_LICENSE.md
size_categories:
- n<1K
tags:
- video
- infrared
- electro-optical
- multimodal
- evaluation
- video-question-answering
- aerial
extra_gated_heading: "Accept the TerraSense Analytics Data Release Terms to access RAVEN-Bench"
extra_gated_prompt: >-
  The RAVEN-Bench videos and annotations are provided by TerraSense Analytics Ltd.
  under the TerraSense Analytics Data Release Terms (see DATA_LICENSE.md): research and
  non-commercial use only, attribution required, no re-identification of
  individuals, no surveillance, tracking or profiling of specific individuals,
  and downstream recipients must be bound by the same terms. By requesting
  access you agree to these terms.
extra_gated_fields:
  Name: text
  Affiliation: text
  Country: country
  Intended use: text
  I agree to use the dataset for research and non-commercial purposes only: checkbox
  I will not attempt to re-identify any individual appearing in the dataset: checkbox
  I will include the TerraSense attribution notice in any publication that uses the dataset: checkbox
  I agree to the TerraSense Analytics Data Release Terms (DATA_LICENSE file): checkbox
extra_gated_button_content: "Agree and request access"
configs:
- config_name: default
  data_files:
  - split: test
    path: videos/**
---
# RAVEN-Bench

**A Paired EO–IR Video QA Benchmark for Aerial Multimodal Understanding** (NeurIPS 2026, Track on
Evaluations and Datasets) · [Code](https://github.com/yuhu990424/RAVEN-Bench) · [Citation](CITATION.md)

RAVEN-Bench contains **100 temporally aligned EO–IR video pairs (200 MP4 streams)** and **1,200
human-authored four-way multiple-choice questions**, twelve per pair. Each pair has an eight-question
consistency group (C1) and a four-question coherence group (H1). Models are evaluated under EO-only,
IR-only and paired EO+IR input.

**Version:** `1.0.0`

## Access

Access is gated. Sign in, state your affiliation and intended research use, and accept the
[TerraSense Analytics Data Release Terms](DATA_LICENSE.md). Requests are approved by the maintainers.
After approval, the dataset files are available from this repository.

## License and required attribution

The videos and annotations are provided by **TerraSense Analytics Ltd.** under the
[TerraSense Analytics Data Release Terms](DATA_LICENSE.md): **research and non-commercial use only**.
Re-identifying individuals, surveillance, tracking or profiling of specific individuals, and the other
uses listed in section 4 are prohibited. Anyone who redistributes the dataset or a modified version must
pass the same terms on to recipients and identify the modifications. Results such as model outputs and
trained weights are not restricted. Evaluation, example and tool code is licensed under the Apache License 2.0; see [LICENSES.md](LICENSES.md).

Any publication, presentation or distribution that uses the dataset must include:

> This work uses data provided by Terra Sense Analytics Ltd. (www.terrasense.ca).

## Composition

| Item | Count |
| --- | ---: |
| EO–IR video pairs | 100 |
| EO / IR streams | 100 / 100 |
| Questions (12 per pair, 4 options each) | 1,200 |
| L1 / L2 / L3 questions | 386 / 560 / 254 |
| EO / IR / EO+IR required evidence | 656 / 229 / 315 |
| Consistency / coherence groups | 100 (8 items) / 100 (4 items) |
| Total stream duration | 17,939.0 s (mean 89.7 s per stream) |
| Scenario families | 59 ground transport and machinery · 27 maritime · 11 aircraft · 3 people |
| Video bytes | about 158 GB |

The default Hub view has one row per pair with two video columns. Its `test` label names the evaluation
collection; there is no train or validation split.

## Files

```text
videos/metadata.jsonl         # 100 paired rows with EO and IR references
videos/eo/*.mp4               # TerraSense_eo_1.mp4 ... TerraSense_eo_100.mp4
videos/ir/*.mp4               # TerraSense_ir_1.mp4 ... TerraSense_ir_100.mp4
data/annotations/*.json       # one annotation file per pair (source option order and answers)
data/questions.jsonl          # 1,200 evaluation rows with the benchmark option permutation
data/model_inputs.jsonl       # the same questions/options without answers or hints
data/pairs.jsonl              # pairing index
data/captions.jsonl           # event descriptions (not model input)
data/metadata/pairs.csv       # pair index used by the evaluation code
data/questions.csv            # flat question view used by Croissant
croissant.json                # Croissant metadata with Responsible AI fields
release_manifest.json         # membership and file hashes
checksums.sha256              # content checksums, including the 200 MP4 files
```

See [docs/DATA_FORMAT.md](docs/DATA_FORMAT.md) for field definitions.

## Evaluation

The [included evaluation code](code/README.md) reads this dataset and exports requests for all three
input settings. Its configured input policy uses the full videos at 1 frame per second, no default
frame cap, and a 262,144-pixel area budget per frame. See
[the input protocol](code/docs/video_input_protocol.md) and report the actual policy used with results.

Send only the question, the options and the selected video input to a model. Do not send captions,
answers, option roles, capability labels, question types, modality requirements or evidence intervals.
Annotation files use the source option order; `data/questions.jsonl` and `data/model_inputs.jsonl` use
the benchmark's deterministic permutation (`eo_ir_option_shuffle_v1`), so score against the
`correct_answer` in the question table.

To inspect video references without decoding:

```python
from datasets import Video, load_dataset
ds = load_dataset("TerraSenseAnalytics/RAVEN-Bench", split="test", streaming=True)
ds = ds.cast_column("eo", Video(decode=False))
ds = ds.cast_column("ir", Video(decode=False))
pair = next(iter(ds))
print(pair["sample_id"], pair["eo"], pair["ir"])
```

`python examples/download_sample.py` fetches four example pairs (about 415 MiB) with their annotations.

## Responsible use

RAVEN-Bench is intended for research evaluation of EO–IR video reasoning, not for operational deployment.
The release contains no boxes, masks, tracks, trajectories, geolocation, flight paths or operational
metadata. See [docs/RESPONSIBLE_AI.md](docs/RESPONSIBLE_AI.md) for provenance, privacy screening,
limitations and residual risks.

## Issues

Report data problems through the
[GitHub issue tracker](https://github.com/yuhu990424/RAVEN-Bench/issues) or the Community tab of this
dataset. For access or licensing questions, contact the corresponding authors, Zheng Liu
(zheng.liu@ubc.ca) and Mohsen Zardadi (mohsen.zardadi@terrasense.ca).

## Included evaluation code

The [code directory](code/README.md) contains the portable evaluation engine, model runners and configs. Install with `python3 -m pip install -e ./code`; default preparation is offline.

	---
	pretty_name: RAVEN-Bench — Paired EO/IR Video Understanding
	language:
	- en
	license: other
	license_name: terrasense-data-release-terms
	license_link: https://huggingface.co/datasets/TerraSenseAnalytics/RAVEN-Bench/blob/main/DATA_LICENSE.md
	size_categories:
	- n<1K
	tags:
	- video
	- infrared
	- electro-optical
	- multimodal
	- evaluation
	- video-question-answering
	- aerial
	extra_gated_heading: "Accept the TerraSense Analytics Data Release Terms to access RAVEN-Bench"
	extra_gated_prompt: >-
	The RAVEN-Bench videos and annotations are provided by TerraSense Analytics Ltd.
	under the TerraSense Analytics Data Release Terms (see DATA_LICENSE.md): research and
	non-commercial use only, attribution required, no re-identification of
	individuals, no surveillance, tracking or profiling of specific individuals,
	and downstream recipients must be bound by the same terms. By requesting
	access you agree to these terms.
	extra_gated_fields:
	Name: text
	Affiliation: text
	Country: country
	Intended use: text
	I agree to use the dataset for research and non-commercial purposes only: checkbox
	I will not attempt to re-identify any individual appearing in the dataset: checkbox
	I will include the TerraSense attribution notice in any publication that uses the dataset: checkbox
	I agree to the TerraSense Analytics Data Release Terms (DATA_LICENSE file): checkbox
	extra_gated_button_content: "Agree and request access"
	configs:
	- config_name: default
	data_files:
	- split: test
	path: videos/**
	---

	# RAVEN-Bench

	A Paired EO–IR Video QA Benchmark for Aerial Multimodal Understanding (NeurIPS 2026, Track on
	Evaluations and Datasets) · [Code](https://github.com/yuhu990424/RAVEN-Bench) · [Citation](CITATION.md)

	RAVEN-Bench contains 100 temporally aligned EO–IR video pairs (200 MP4 streams) and **1,200
	human-authored four-way multiple-choice questions**, twelve per pair. Each pair has an eight-question
	consistency group (C1) and a four-question coherence group (H1). Models are evaluated under EO-only,
	IR-only and paired EO+IR input.

	Version: `1.0.0`

	## Access

	Access is gated. Sign in, state your affiliation and intended research use, and accept the
	[TerraSense Analytics Data Release Terms](DATA_LICENSE.md). Requests are approved by the maintainers.
	After approval, the dataset files are available from this repository.

	## License and required attribution

	The videos and annotations are provided by TerraSense Analytics Ltd. under the
	[TerraSense Analytics Data Release Terms](DATA_LICENSE.md): research and non-commercial use only.
	Re-identifying individuals, surveillance, tracking or profiling of specific individuals, and the other
	uses listed in section 4 are prohibited. Anyone who redistributes the dataset or a modified version must
	pass the same terms on to recipients and identify the modifications. Results such as model outputs and
	trained weights are not restricted. Evaluation, example and tool code is licensed under the Apache License 2.0; see [LICENSES.md](LICENSES.md).

	Any publication, presentation or distribution that uses the dataset must include:

	> This work uses data provided by Terra Sense Analytics Ltd. (www.terrasense.ca).

	## Composition

	\| Item \| Count \|
	\| --- \| ---: \|
	\| EO–IR video pairs \| 100 \|
	\| EO / IR streams \| 100 / 100 \|
	\| Questions (12 per pair, 4 options each) \| 1,200 \|
	\| L1 / L2 / L3 questions \| 386 / 560 / 254 \|
	\| EO / IR / EO+IR required evidence \| 656 / 229 / 315 \|
	\| Consistency / coherence groups \| 100 (8 items) / 100 (4 items) \|
	\| Total stream duration \| 17,939.0 s (mean 89.7 s per stream) \|
	\| Scenario families \| 59 ground transport and machinery · 27 maritime · 11 aircraft · 3 people \|
	\| Video bytes \| about 158 GB \|

	The default Hub view has one row per pair with two video columns. Its `test` label names the evaluation
	collection; there is no train or validation split.

	## Files

	```text
	videos/metadata.jsonl # 100 paired rows with EO and IR references
	videos/eo/*.mp4 # TerraSense_eo_1.mp4 ... TerraSense_eo_100.mp4
	videos/ir/*.mp4 # TerraSense_ir_1.mp4 ... TerraSense_ir_100.mp4
	data/annotations/*.json # one annotation file per pair (source option order and answers)
	data/questions.jsonl # 1,200 evaluation rows with the benchmark option permutation
	data/model_inputs.jsonl # the same questions/options without answers or hints
	data/pairs.jsonl # pairing index
	data/captions.jsonl # event descriptions (not model input)
	data/metadata/pairs.csv # pair index used by the evaluation code
	data/questions.csv # flat question view used by Croissant
	croissant.json # Croissant metadata with Responsible AI fields
	release_manifest.json # membership and file hashes
	checksums.sha256 # content checksums, including the 200 MP4 files
	```

	See [docs/DATA_FORMAT.md](docs/DATA_FORMAT.md) for field definitions.

	## Evaluation

	The [included evaluation code](code/README.md) reads this dataset and exports requests for all three
	input settings. Its configured input policy uses the full videos at 1 frame per second, no default
	frame cap, and a 262,144-pixel area budget per frame. See
	[the input protocol](code/docs/video_input_protocol.md) and report the actual policy used with results.

	Send only the question, the options and the selected video input to a model. Do not send captions,
	answers, option roles, capability labels, question types, modality requirements or evidence intervals.
	Annotation files use the source option order; `data/questions.jsonl` and `data/model_inputs.jsonl` use
	the benchmark's deterministic permutation (`eo_ir_option_shuffle_v1`), so score against the
	`correct_answer` in the question table.

	To inspect video references without decoding:

	```python
	from datasets import Video, load_dataset

	ds = load_dataset("TerraSenseAnalytics/RAVEN-Bench", split="test", streaming=True)
	ds = ds.cast_column("eo", Video(decode=False))
	ds = ds.cast_column("ir", Video(decode=False))
	pair = next(iter(ds))
	print(pair["sample_id"], pair["eo"], pair["ir"])
	```

	`python examples/download_sample.py` fetches four example pairs (about 415 MiB) with their annotations.

	## Responsible use

	RAVEN-Bench is intended for research evaluation of EO–IR video reasoning, not for operational deployment.
	The release contains no boxes, masks, tracks, trajectories, geolocation, flight paths or operational
	metadata. See [docs/RESPONSIBLE_AI.md](docs/RESPONSIBLE_AI.md) for provenance, privacy screening,
	limitations and residual risks.

	## Issues

	Report data problems through the
	[GitHub issue tracker](https://github.com/yuhu990424/RAVEN-Bench/issues) or the Community tab of this
	dataset. For access or licensing questions, contact the corresponding authors, Zheng Liu
	(zheng.liu@ubc.ca) and Mohsen Zardadi (mohsen.zardadi@terrasense.ca).

	## Included evaluation code

	The [code directory](code/README.md) contains the portable evaluation engine, model runners and configs. Install with `python3 -m pip install -e ./code`; default preparation is offline.