Datasets:
license: cc-by-nc-sa-4.0
task_categories:
- image-text-to-text
tags:
- vqa
- vision-language-model
- top-down-images
- aerial-images
- benchmark
configs:
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data_files:
- split: main_0deg
path: data/main_0deg-*
- split: main_90deg
path: data/main_90deg-*
- split: main_180deg
path: data/main_180deg-*
- split: main_270deg
path: data/main_270deg-*
- split: case_study_zoom_in
path: data/case_study_zoom_in-*
- split: case_study_integrity
path: data/case_study_integrity-*
- split: case_study_height
path: data/case_study_height-*
- split: case_study_depth
path: data/case_study_depth-*
dataset_info:
features:
- name: index
dtype: int64
- name: image
dtype: image
- name: question
dtype: string
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
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- name: answer
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- name: category
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download_size: 629990525
dataset_size: 469809749.5
TDBench: Benchmarking Vision-Language Models in Understanding Top-Down / Bird's Eye View Images
Kaiyuan Hou+, Minghui Zhao+, Lilin Xu, Yuang Fan, Xiaofan Jiang (+: Equally contributing first authors)
Intelligent and Connected Systems Lab (ICSL), Columbia University
8 Representative VLMs on 10 dimensions in TDBench
Abstract: Top-down images play an important role in safety-critical settings such as autonomous navigation and aerial surveillance, where they provide holistic spatial information that front-view images cannot capture. Despite this, Vision Language Models (VLMs) are mostly trained and evaluated on front-view benchmarks, leaving their performance in the top-down setting poorly understood. Existing evaluations also overlook a unique property of top-down images: their physical meaning is preserved under rotation. In addition, conventional accuracy metrics can be misleading, since they are often inflated by hallucinations or "lucky guesses", which obscures a model's true reliability and its grounding in visual evidence. To address these issues, we introduce TDBench, a benchmark for top-down image understanding that includes 2000 curated questions for each rotation. We further propose RotationalEval (RE), which measures whether models provide consistent answers across four rotated views of the same scene, and we develop a reliability framework that separates genuine knowledge from chance. Finally, we conduct four case studies targeting underexplored real-world challenges. By combining rigorous evaluation with reliability metrics, TDBench not only benchmarks VLMs in top-down perception but also provides a new perspective on trustworthiness, guiding the development of more robust and grounded AI systems. Project homepage: this https URL
📢 Latest Updates
- Apr-23-25: Submitted pull request to VLMEvalKit repository.
- Apr-10-25: Arxiv Preprint is released arxiv link. 🔥🔥
- Apr-01-25: We release the benchmark dataset.
💡 Overview
🏆 Contributions
- TDBench Benchmark. We introduce TDBench, a benchmark designed specifically for evaluating VLMs on Top-down images originate from real scenarios is aerial operation or drone applications. We carefully curated a dataset manually comprising a total of 2000 questions.
- Rotational Evaluation. We introduce an evaluation strategy RotationalEval specifically designed for top-down images. Due to the nature of top-down images, rotations do not affect the semantic meaning, whereas this is not true and does not physically make sense naturally for front-view images.
- Four Case Studies. We performed 4 case studies that frequently occur in the real world.These studies evaluate specific capabilities of VLMs under controlled conditions, providing actionable insights for practical deployment while identifying critical challenges that must be addressed for reliable aerial image understanding.
📊 Benchmarks Comparison
Overview performance of 8 open source VLMs and 6 propriety VLMs on 10 dimensions with RotationalEval method.
🗂️ Case Studies
Top-down images are usually captured from a relatively high altitude, which may introduce several challenges such as small object, different perspective. Furthermore, top-down images do not contain depth information in most cases, yet depth is very important for many real-world applications such as building height estimation and autonomous drone navigation and obstacle avoidance. Based on these considerations, we also conduct the following four case studies in paper.
Digital Magnification for Small Object Detection
- Provide insights on post-processing the images to enable VLMs to see small objects
Altitude Effects on Object Detection
- Guidelines on drones' hovering height for different object detection tasks
Object Visibility and Partial Occlusion
- Study when objects are partially hidden or occluded by other objects
Z-Axis Perception and Depth Understanding
- Assessing the depth reasoning from top-down images
🤖 Sample Usage: How to run TDBench
TDBench is fully compatible with VLMEvalKit.
Installation
- First, install the VLMEvalKit environment by following the instructions in the official repository
- Set up your model configuration and APIs according to VLMEvalKit requirements
Datasets (for VLMEvalKit run.py)
Standard Evaluation - Tests 9 dimensions with 4 rotation angles
tdbench_rot0(0° rotation)tdbench_rot90(90° rotation)tdbench_rot180(180° rotation)tdbench_rot270(270° rotation)
Visual Grounding - Tests visual grounding with 4 rotation angles
tdbench_grounding_rot0(0° rotation)tdbench_grounding_rot90(90° rotation)tdbench_grounding_rot180(180° rotation)tdbench_grounding_rot270(270° rotation)
Case Studies - 4 studies
tdbench_cs_zoomtdbench_cs_heighttdbench_cs_integritytdbench_cs_depth
Usage Examples
Standard Evaluation
To only evaluate a single rotation
python run.py --data tdbench_rot0 \
--model <model_name> \
--verbose \
--work-dir <results_directory>
To apply RotationalEval, simply run all rotations
python run.py --data tdbench_rot0 tdbench_rot90 tdbench_rot180 tdbench_rot270 \
--model <model_name> \
--verbose \
--work-dir <results_directory>
Visual Grounding Evaluation
To only evaluate a single rotation
python run.py --data tdbench_grounding_rot0 \
--model <model_name> \
--verbose \
--judge centroid \
--work-dir <results_directory>
To apply RotationalEval, simply run all rotations
python run.py --data tdbench_grounding_rot0 tdbench_grounding_rot90 tdbench_grounding_rot180 tdbench_grounding_rot270 \
--model <model_name> \
--verbose \
--judge centroid \
--work-dir <results_directory>
Case Studies
Run all case studies with:
python run.py --data tdbench_cs_zoom tdbench_cs_height tdbench_cs_integrity tdbench_cs_depth \
--model <model_name> \
--verbose \
--work-dir <results_directory>
Output
VLMEvalKit prints and saves each dataset's output in <results_directory>/<model_name>. Check xxx_acc.csv for accuracy score, and xxx_result.xlsx for detailed VLM outputs.
RotationalEval is triggered automatically after running all rotations. Results will be printed and saved as xxx_REresult.csv.
📜 Citation
If you find our work and this repository useful, please consider giving our repo a star and citing our paper as follows:
@article{hou2025tdbench,
title={TDBench: Benchmarking Vision-Language Models in Understanding Top-Down Images},
author={Hou, Kaiyuan and Zhao, Minghui and Xu, Lilin and Fan, Yuang and Jiang, Xiaofan},
journal={arXiv preprint arXiv:2504.03748},
year={2025}
}
📨 Contact
If you have any questions, please create an issue on this repository or contact at kh3119@columbia.edu or mz2866@columbia.edu.
