UAV-DualCog Dataset Repository Guide

Last updated: 2026-04-08

This is the official dataset repository guide for UAV-DualCog. The corresponding paper is currently under peer review, and this dataset release is made public under a single-blind policy.

1. What UAV-DualCog Is

UAV-DualCog is a drone-centric multimodal reasoning benchmark for dual cognition: self-aware reasoning and environment-aware reasoning under aerial observation. The release targets two complementary goals:

• benchmark evaluation for multimodal foundation models,
• reusable structured assets for downstream dataset users.

The benchmark is organized around one primary capability axis and one observation axis:

• dual cognition:
  • self-aware reasoning,
  • environment-aware reasoning;
• media:
  • image tasks,
  • video tasks.

The key point is that dual cognition is the capability being evaluated, while image and video are the media used to expose that capability. This design yields a benchmark that does not only test answer selection, but also tests whether a model can align its reasoning with spatial evidence or temporal evidence.

1.1 Quick Start

Recommended entry points:

1. Read this dataset card to understand the release scope and file contracts.
2. Use the benchmark website to inspect task definitions, examples, and leaderboard views:
   • https://uav-dualcog.lozumi.com/
3. Use the official code repository for loading, preprocessing, and evaluation:
   • https://github.com/SmartDianLab/UAV-DualCog
4. Use the AerialVLN simulator package when reproducing simulator-backed collection or rendering:
   • https://www.kaggle.com/datasets/shuboliu/aerialvln-simulators

For detailed benchmark definitions, construction details, and usage instructions, the benchmark website should be treated as the primary external reference.

2. Benchmark Scope

Current core release:

• 12 released benchmark scenes,
• 512 validated landmarks,
• 4096 image QA samples,
• 2048 video QA samples,
• 4 image task families,
• 2 video task families.

All currently released benchmark task files are test-only. The repository does not currently expose public train or validation splits for task evaluation.

The released 12-scene benchmark subset is drawn from a larger reviewed scene pool. In the public repository, the benchmark task layer and the scene asset layer do not have identical scope:

• task_data currently corresponds to the 12-scene benchmark release;
• scene_data covers the full set of 18 reviewed scenes that have public geometry and landmark-review assets.

This means the repository exposes a broader scene asset pool than the current benchmark task split. Scene-level geometric assets and reviewed landmark assets are provided so that users can inspect the benchmark context rather than treating the task files as opaque black boxes.

For clarity:

• scene_data is a supporting public asset release rather than a training split;
• task_data is a benchmark evaluation release and should be treated as test data.

3. Capability Definition

3.1 Self-aware reasoning

Self-aware reasoning evaluates whether a UAV agent can reason about itself:

• where it is relative to a landmark,
• what it will observe after a described motion,
• what behavior it is executing,
• when that behavior occurs.

3.2 Environment-aware reasoning

Environment-aware reasoning evaluates whether a UAV agent can reason about the external world from its current motion context:

• where the target landmark is relative to the UAV,
• which action is appropriate given the landmark-relative situation,
• how many times a landmark becomes visible in a mission,
• during which time intervals the landmark is visible.

3.3 Evidence-aware evaluation

UAV-DualCog explicitly separates:

• semantic correctness,
• evidence grounding.

For image tasks, a model is evaluated on both:

• selecting the correct answer option,
• localizing the landmark with a normalized bounding box.

For video tasks, a model is evaluated on both:

• predicting the correct semantic answer,
• localizing the relevant time interval(s).

This is one of the core benchmark design principles: answer-only success is not sufficient if the supporting spatial or temporal evidence is incorrect.

4. Task Families

4.1 Image tasks (Stage 4)

The image branch contains four task families. Each released landmark contributes both 4way and 8way difficulty variants.

1. self_where

   • Canonical display name: Landmark-Relative Position Reasoning
   • Cognition: self-aware
   • Input: one landmark-centric reference image plus one egocentric query observation
   • Output: one answer option and one landmark bounding box on the query image
   • Core question: where is the UAV relative to the landmark

2. self_what

   • Canonical display name: Future Observation Prediction
   • Cognition: self-aware
   • Input: one reference image plus a future-view multiple-choice set
   • Output: one answer option
   • Core question: which future observation matches the described motion outcome

3. env_where

   • Canonical display name: Self-Relative Position Reasoning
   • Cognition: environment-aware
   • Input: one current egocentric observation
   • Output: one answer option and one landmark bounding box on the query image
   • Core question: where is the landmark relative to the UAV

4. env_how

   • Canonical display name: Landmark-Driven Action Decision
   • Cognition: environment-aware
   • Input: one current egocentric observation
   • Output: one answer option and one landmark bounding box on the query image
   • Core question: what action decision is appropriate under the current landmark-relative situation

4.2 Video tasks (Stage 3)

The video branch contains two task families.

1. self_instance_recognition_joint

   • Canonical display name: Flight Behavior Recognition and Temporal Localization
   • Cognition: self-aware
   • Input: task video plus mission-conditioned context
   • Output: behavior option(s) and temporal interval(s)
   • Public reporting also derives:
     • composite-level semantic accuracy,
     • atomic-level semantic accuracy,
     • temporal localization quality.

2. env_visibility_reasoning

   • Canonical display name: Landmark Visibility Counting and Interval Reasoning
   • Cognition: environment-aware
   • Input: task video plus target landmark reference
   • Output: visibility count and visible time interval(s)

4.3 Task summary table

Task ID	Display name	Modality	Cognition	Main input	Main output
self_where	Landmark-Relative Position Reasoning	image	self-aware	reference image + query observation	option + bbox
self_what	Future Observation Prediction	image	self-aware	reference image + future-view options	option
env_where	Self-Relative Position Reasoning	image	environment-aware	query observation	option + bbox
env_how	Landmark-Driven Action Decision	image	environment-aware	query observation	option + bbox
self_instance_recognition_joint	Flight Behavior Recognition and Temporal Localization	video	self-aware	task video + mission context	option(s) + interval(s)
env_visibility_reasoning	Landmark Visibility Counting and Interval Reasoning	video	environment-aware	task video + landmark context	count + interval(s)

5. Evaluation Objects and Metrics

5.1 Image tasks

Image-task prediction objects contain:

• answer_option_id
• optionally bbox_xyxy_norm

Main metrics include:

• option accuracy,
• BBox Acc@50IoU,
• mean IoU.

5.2 Video tasks

Video-task prediction objects contain:

• answer option(s) or behavior label(s),
• interval(s) in seconds,
• for visibility tasks, visible count.

Main metrics include:

• semantic correctness,
• temporal IoU or interval agreement,
• count accuracy for visibility reasoning.

The public leaderboard may present aggregated summary views for readability, but the underlying task manifests and experiment outputs retain the task-level prediction structure.

6. Repository Scope and Boundary

The public repository is the release-facing layer of the dataset. It includes:

• scene-level geometry and reviewed landmarks,
• released benchmark task assets,
• released manifests and render requests,
• benchmark-ready media references.

The scope is asymmetric by design:

• scene_data contains the complete 18-scene reviewed scene release;
• task_data currently contains the 12-scene benchmark task release.

The released task layer is also split-asymmetric in another sense:

• the repository currently provides public benchmark test data only;
• it does not provide public train or validation task splits.

It intentionally excludes many internal generation-time artifacts, including:

• internal logs,
• temporary caches,
• internal experiment workspaces,
• internal review-only intermediate files not needed for public reproduction.

7. Top-Level Layout

The public repository is conceptually split into two release layers.

scene_data/
  airsim_env_*/
    pcd_map/
    landmarks_raw/
    landmarks_review/

task_data/
  airsim_env_*/
    image_tasks/
      assets/
      manifests/
      render_requests/
      selections/
    video_tasks/
      missions/
      datasets/
      selections/

7.1 scene_data

This layer stores scene-level assets and landmark review outputs.

Important release note:

• scene_data is not restricted to the 12 benchmark test scenes.

• The current public release contains all 18 reviewed scenes with available scene geometry and landmark-review outputs.

• pcd_map/

  • fused point-cloud assets and geometry support files.

• landmarks_raw/

  • pre-review landmark candidate outputs.

• landmarks_review/

  • reviewed landmark instances and downstream-consumable landmark metadata.

7.2 task_data

This layer stores benchmark task artifacts.

• image_tasks/
  • Stage 4 image QA assets, manifests, and render requests.
• video_tasks/
  • Stage 3 mission-level task videos, final-task metadata, and released manifests.

8. Data Contracts

The following files are the main public contracts that downstream users should treat as stable interfaces.

8.1 Scene review contract

scene_data/<scene>/landmarks_review/<scene>.valid_instances.json

This is the reviewed landmark handoff file used by later stages. It provides:

• stable landmark instance ids,
• reviewed category/subcategory/description fields,
• reference RGB view assets,
• geometry and instance context needed for task generation.

8.2 Image-task manifest contract

task_data/<scene>/image_tasks/manifests/<scene>.latest_manifest.json

Top-level fields include:

• generation metadata,
• scene id and engine,
• released task types and difficulty sets,
• samples.

Each sample contains fields such as:

• sample_id
• landmark_id
• task_family
• task_group
• difficulty
• reference_image
• reference_image_with_bbox
• reference_bbox_xyxy_norm
• target_image
• answer_bbox_xyxy_norm
• task_type
• label_options
• answer_option_id
• prompt_text
• user_prompt
• system_prompt

This contract is sufficient for benchmark inference on image tasks.

Representative sample shape:

{
  "sample_id": "env_7_20_120_self_shared_4way_000001_where",
  "task_type": "self_where",
  "task_group": "self-aware",
  "difficulty": "4way",
  "landmark_id": "20_120",
  "reference_image_with_bbox": "task_data/airsim_env_7/image_tasks/assets/reference_bbox/20_120/....jpg",
  "target_image": "scene_data/airsim_env_7/landmarks_raw/rgb_views/20_120/....jpg",
  "label_options": [
    {"option_id": "A", "label": "..."},
    {"option_id": "B", "label": "..."}
  ],
  "answer_option_id": "D",
  "answer_bbox_xyxy_norm": [0.31, 0.27, 0.58, 0.76]
}

8.3 Video-task manifest contract

task_data/<scene>/video_tasks/datasets/<scene>.latest_manifest.json

Top-level fields include:

• generation metadata,
• scene id and engine,
• released forms,
• task-group flags,
• samples,
• manifest-level summary.

Each sample contains fields such as:

• sample_id
• form
• task_group
• task_name
• task_display_name
• mission_id
• mission_family
• landmark_id
• reference_image_with_bbox
• overview_image
• keyframe_board_image
• video_path
• video_web_path
• fps
• frame_count
• flight_description
• visible_count
• visible_intervals_sec
• difficulty_band
• choice_options
• answer_option_ids
• answer_items

This contract is the benchmark-facing video task interface.

Representative sample shape:

{
  "sample_id": "env_7_batch_env_7_10_55_atomic_0075_self_instance_recognition_joint_000001",
  "form": "self_instance_recognition_joint",
  "task_group": "self-state",
  "mission_id": "batch_env_7_10_55_atomic_0075",
  "landmark_id": "10_55",
  "reference_image_with_bbox": "task_data/airsim_env_7/video_tasks/cache/assets/reference_bbox/10_55/....jpg",
  "video_path": "task_data/airsim_env_7/video_tasks/missions/.../final_task/task_rgb.mp4",
  "video_web_path": "task_data/airsim_env_7/video_tasks/missions/.../final_task/task_rgb_web.mp4",
  "fps": 5,
  "frame_count": 157,
  "visible_count": 1,
  "visible_intervals_sec": [{"start_sec": 0.0, "end_sec": 2.7}],
  "difficulty_band": "easy",
  "choice_options": [
    {"option_id": "A", "label": "..."},
    {"option_id": "B", "label": "..."}
  ],
  "answer_option_ids": ["C"],
  "answer_items": [
    {"option_id": "C", "label": "...", "intervals_sec": [{"start_sec": 1.2, "end_sec": 6.8}]}
  ]
}

8.4 Mission-level Stage 3 contract

task_data/<scene>/video_tasks/missions/<mission_id>/final_task/task_data.json

This file is the mission-level ground-truth contract behind Stage 3 tasks. It contains:

• video
  • media paths,
  • frame manifests,
  • fps,
  • frame counts,
  • video dimensions,
  • capture dimensions;
• target_presence
  • frame-level or interval-level target presence information;
• task_tracks
  • task-specific supervision for:
    • environmental_awareness,
    • self_state_awareness.

This file is the correct entry point when a user needs mission-level temporal supervision rather than only released sample-level manifests.

In practice:

• use video_tasks/datasets/<scene>.latest_manifest.json for benchmark inference and leaderboard-style evaluation;
• use missions/<mission_id>/final_task/task_data.json when mission-level temporal supervision or frame-level inspection is needed.

9. Media and Path Semantics

Image and video paths stored in manifests are release-facing references, not arbitrary internal cache paths.

For Stage 4:

• reference_image_with_bbox points to the released reference image with GT bbox overlay,
• target_image points to the released query observation.

Depending on task subtype and release path, Stage 4 media may point either to:

• released task assets under task_data/.../image_tasks/assets/..., or
• scene-level source views under scene_data/.../landmarks_raw/rgb_views/....

For Stage 3:

• video_path points to the released main task video,
• video_web_path points to a web-playable derivative when available,
• reference_image_with_bbox, overview_image, and keyframe_board_image provide auxiliary evidence views,
• task_data.json -> video.frames_manifest and frame_index_map support frame-level inspection.

If video_web_path is empty for a given sample, downstream users should fall back to video_path.

10. Usage and Reproduction Pointers

This repository guide intentionally focuses on release scope and data contracts.
To avoid divergence and duplicated maintenance, detailed operational steps (environment setup, stage-by-stage commands, benchmark execution, and evaluation scripts) are not repeated here.

Please use the following as the canonical operational references:

• Benchmark website (recommended reading order and Usage page):
  • https://uav-dualcog.lozumi.com/
  • https://uav-dualcog.lozumi.com/usage/
• Official code repository (latest runnable commands and config templates):
  • https://github.com/SmartDianLab/UAV-DualCog
• Simulator package used by construction-stage reproduction:
  • https://www.kaggle.com/datasets/shuboliu/aerialvln-simulators

Practical split for external users:

• Use this dataset guide for file contracts, task semantics, and manifest field definitions.
• Use website + GitHub for concrete execution instructions and reproducibility workflows.

11. Benchmark Provenance

The public release is produced by the four-stage UAV-DualCog construction pipeline:

• Stage 1: scene point-cloud collection and fusion,
• Stage 2: landmark mining, review, and structured annotation,
• Stage 3: behavior-driven mission generation and video task construction,
• Stage 4: landmark-centered image QA generation.

The benchmark website provides:

• task explanations,
• prompt templates,
• examples,
• leaderboard views,
• analysis pages.

Official benchmark site:

• https://uav-dualcog.lozumi.com/

Official code repository:

• https://github.com/SmartDianLab/UAV-DualCog

12. Practical Notes for External Users

• Field names should be consumed in their canonical JSON form.
• Task ids such as self_where or env_visibility_reasoning should be treated as stable benchmark identifiers.
• Display names on the website are reader-facing aliases; manifests retain machine-facing ids.
• Some repository paths may differ slightly across mirrors or release bundles. The canonical structure is the contract described in this guide.
• For actual loading and benchmark evaluation, prefer the official GitHub implementation instead of reimplementing parsers from scratch:
  • https://github.com/SmartDianLab/UAV-DualCog
• For detailed benchmark definitions, construction explanations, and usage walkthroughs, prefer the public benchmark website:
  • https://uav-dualcog.lozumi.com/
• For simulator-backed reproduction, use the released AerialVLN simulator package:
  • https://www.kaggle.com/datasets/shuboliu/aerialvln-simulators

13. Citation and License

• License: follow the repository card and platform metadata for the active release.
• Citation: cite the UAV-DualCog dataset release and record the repository version/date used in evaluation.
• Benchmark-facing supplementary explanations are maintained at:
  • https://uav-dualcog.lozumi.com/
• Official loading and evaluation code is maintained at:
  • https://github.com/SmartDianLab/UAV-DualCog
• Simulator dependency for reproduction is maintained at:
  • https://www.kaggle.com/datasets/shuboliu/aerialvln-simulators