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---
task_categories:
- visual-question-answering
- object-detection
language:
- en
tags:
- remote-sensing
- geospatial
- satellite-imagery
size_categories:
- 1K<n<10K
---
# GeoChat_bench_split Dataset
## Dataset Description
This dataset is a curated subset of the GeoChat_Bench benchmark from the EarthDial-Dataset, specifically containing samples with multiple ground truth bounding boxes.
### Dataset Summary
- **Total Samples:** 1,840
- **Source:** akshaydudhane/EarthDial-Dataset (GeoChat_Bench subset)
- **Task:** Visual grounding / object localization in satellite imagery
- **Modality:** Image (RGB) + Text
### Dataset Structure
The dataset contains 4 columns:
1. **question_id** (string): Unique identifier for each sample
2. **jpg** (Image): RGB satellite/aerial image
3. **question** (string): Question asking for object locations (format: "Give me the location of <ref>object description")
4. **groundtruth** (string): Multiple bounding box coordinates in the format `[[x1,y1,x2,y2,confidence]][[x1,y1,x2,y2,confidence]]...`
### Data Fields
- `question_id`: Unique sample identifier
- `jpg`: PIL Image object in RGB format
- `question`: Natural language question with reference tags
- `groundtruth`: Serialized list of bounding boxes
### Example
```python
{
'question_id': 'sota_2403',
'jpg': <PIL.Image>,
'question': 'Give me the location of <ref>10 large small-vehicles',
'groundtruth': '[[20, 43, 27, 53, 90]][[5, 38, 11, 47, 90]]...'
}
```
## Dataset Creation
### Curation Process
1. Loaded GeoChat_Bench from EarthDial-Dataset
2. Filtered samples containing multiple bounding boxes in ground truth (>1 list)
3. Sorted by number of bounding boxes in descending order
4. Selected top 1,840 samples (all available samples with multiple boxes)
5. Cleaned question text to remove formatting prefixes and tags
### Source Data
Original dataset: [EarthDial-Dataset](https://huggingface.co/datasets/akshaydudhane/EarthDial-Dataset)
## Usage
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("yobro4619/GeoChat_bench_split")
# Access samples
sample = dataset['train'][0]
print(sample['question'])
print(sample['groundtruth'])
```
## License
Please refer to the original [EarthDial-Dataset](https://huggingface.co/datasets/akshaydudhane/EarthDial-Dataset) for licensing information.
## Citation
If you use this dataset, please cite the original EarthDial-Dataset paper.
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