---
license: cc-by-4.0
task_categories:
- visual-question-answering
language:
- en
tags:
- satellite-imagery
- spatial-reasoning
- benchmark
- quantitative-reasoning
- VLM
- language-understanding
size_categories:
- 1K<n<10K
pretty_name: SQuID
---

# SQuID: Satellite Quantitative Intelligence Dataset

A comprehensive benchmark for evaluating quantitative spatial reasoning in Vision-Language Models using satellite imagery.

## Related Resources

- **Code repository**: https://github.com/PeterAMassih/qvlm-squid
- **Paper (arXiv)**: https://arxiv.org/abs/2601.13401

## Dataset Overview

- **2000 questions** testing spatial reasoning on satellite imagery
- **587 unique images** across four datasets
- **1950 auto-labeled** questions from segmentation masks (DeepGlobe, EarthVQA, Solar Panels)
- **50 human-annotated** questions from NAIP imagery with consensus answers
- **1577 questions** include human-agreement ranges for numeric answers
- **3 difficulty tiers**: Basic (710), Spatial (616), Complex (674)
- **3 resolution levels**: 0.3m, 0.5m, 1.0m GSD

## Human Annotation & Agreement Methodology

### Human Annotation Process
- **50 questions** on NAIP 1.0m GSD imagery were annotated by humans
- **10 annotators per question** resulting in 500 total annotations
- **Answer aggregation**:
  - **Numeric questions**: Used MEDIAN of all responses for robustness
  - **Categorical questions** (connected/fragmented): Used MAJORITY voting
  - **Binary questions**: Converted yes/no to 1/0 and used majority

### Human Agreement Quantification
From the 500 human annotations, we computed the Mean Median Absolute Deviation (MAD) for each question type:
- **Percentage questions**: MAD = ±1.74 percentage points
- **Proximity questions**: MAD = ±2.25 percentage points
- **Count questions**: Normalized MADc = 0.19 (proportional to count magnitude)

For count questions, we use a normalized MAD (MADc) that makes the acceptable range proportional to the count value:
```
MADc = median(|Xi - median(X)|) / median(X) = 0.19
```

### Acceptable Range Calculation
These MAD values were applied to ALL numeric questions in the benchmark to define acceptable ranges:

```python
import math

# For percentage questions (absolute deviation)
if question_type == 'percentage':
    lower = max(0.0, answer - 1.74)
    upper = min(100.0, answer + 1.74)
    
# For count questions (proportional deviation)
# range(C) = [C - max(1, C × MADc), C + max(1, C × MADc)]
elif question_type in ['count', 'building_proximity', 'building_flood_risk', 
                        'building_fire_risk', 'connectivity']:
    MADc = 0.19
    dr = max(1, answer * MADc)  # At least ±1 deviation
    lower = max(0, math.floor(answer - dr))
    upper = math.ceil(answer + dr)
    
# For proximity percentage questions (absolute deviation)
elif 'within' in question and 'm of' in question:
    lower = max(0.0, answer - 2.25)
    upper = min(100.0, answer + 2.25)
```

**Example count ranges with MADc = 0.19:**
- C=5 → range [4, 7]
- C=10 → range [8, 12]
- C=50 → range [40, 60]
- C=100 → range [81, 120]

Special cases:
- Zero values have no range (exact match required)
- Binary/fragmentation questions have no range (exact match)
- Ranges are capped at valid bounds (0-100 for percentages, ≥0 for counts)

## Question Types

The benchmark includes 24 distinct question types organized into three tiers:

### Tier 1: Basic Questions (710 questions)
- **percentage**: Coverage percentage of a land use class
- **count**: Number of separate regions or objects
- **size**: Area measurements of regions
- **total_area**: Total area covered by a class
- **binary_comparison**: Comparing quantities between two classes
- **binary_presence**: Checking if a class exists
- **binary_threshold**: Testing if values exceed thresholds
- **binary_multiple**: Checking for multiple instances

### Tier 2: Spatial Questions (616 questions)
- **proximity_percentage**: Percentage of one class near another
- **proximity_area**: Area of one class near another
- **binary_proximity**: Presence of one class near another
- **building_proximity**: Number of buildings near other features
- **building_flood_risk**: Buildings at flood risk (near water)
- **building_fire_risk**: Buildings at fire risk (near forest)
- **connectivity**: Counting isolated patches by size
- **fragmentation**: Assessing if regions are connected or fragmented
- **power_calculation**: Calculating solar panel power output

### Tier 3: Complex Questions (674 questions)
- **complex_multi_condition**: Areas meeting multiple spatial criteria
- **complex_urban_flood_risk**: Urban areas at flood risk (near water)
- **complex_urban_fire_risk**: Urban areas at fire risk (near forest)
- **complex_agriculture_water_access**: Agricultural land with irrigation potential
- **complex_size_filter**: Filtering by size thresholds
- **complex_average**: Average sizes of regions

## Loading the Dataset

```python
from datasets import load_dataset

# Load dataset
dataset = load_dataset("PeterAM4/SQuID")

# Access a sample
sample = dataset['train'][0]
image = sample['image']  # PIL Image
question = sample['question']
answer = sample['answer']  # String or numeric
type = sample['type']

# Convert answer based on type
if type in ['percentage', 'count', 'proximity_percentage', 'proximity_area',
            'building_proximity', 'building_flood_risk', 'building_fire_risk',
            'connectivity', 'size', 'total_area', 'power_calculation'] or 'complex' in type:
    answer_value = float(answer)
elif 'binary' in type:
    answer_value = int(answer)  # 0 or 1
elif type == 'fragmentation':
    answer_value = answer  # "connected" or "fragmented"
```

## Fields

- **id**: Question identifier (e.g., "SQuID_0001")
- **image**: Satellite image path
- **question**: Question text with GSD notation
- **answer**: Ground truth answer
- **type**: One of 24 question types
- **tier**: Difficulty level (1=Basic, 2=Spatial, 3=Complex)
- **gsd**: Ground sampling distance in meters
- **acceptable_range**: [lower, upper] bounds for numeric questions (when applicable)

## Evaluation

For numeric questions, check if predictions fall within the acceptable range:

```python
import math

def evaluate(prediction, sample):
    if 'acceptable_range' in sample:
        # Numeric question - check if within human agreement range
        lower, upper = sample['acceptable_range']
        return lower <= float(prediction) <= upper
    else:
        # Non-numeric question - exact match required
        return str(prediction).lower() == str(sample['answer']).lower()
```

The acceptable ranges represent the natural variation in human perception for spatial measurements.

## Dataset Distribution

### By Tier
- **Tier 1 (Basic)**: 710 questions (35.5%)
- **Tier 2 (Spatial)**: 616 questions (30.8%)
- **Tier 3 (Complex)**: 674 questions (33.7%)

### Top Question Types
- **complex_multi_condition**: 490 questions (24.5%)
- **count**: 178 questions (8.9%)
- **binary_comparison**: 172 questions (8.6%)
- **size**: 166 questions (8.3%)
- **percentage**: 157 questions (7.8%)
- **proximity_percentage**: 123 questions (6.2%)
- **binary_proximity**: 122 questions (6.1%)
- **proximity_area**: 107 questions (5.3%)
- **connectivity**: 104 questions (5.2%)
- **fragmentation**: 98 questions (4.9%)

### By Source
- **DeepGlobe (0.5m GSD)**: 612 questions, 174 images - Land use classification masks
- **EarthVQA (0.3m GSD)**: 1241 questions, 364 images - Building detection and land cover masks
- **Solar Panels (0.3m GSD)**: 97 questions, 35 images - Solar panel segmentation masks
- **NAIP (1.0m GSD)**: 50 questions, 14 images - Human-annotated diverse scenes


## Statistics Summary

- **Zero-valued answers**: 102 (5.1%)
- **Questions with ranges**: 1577 (78.8%)
- **Average questions per image**: 3.4

## Notes

- Questions explicitly state minimum area thresholds (e.g., "ignore patches smaller than 0.125 hectares")
- Zero-valued answers indicate absence of features (intentionally included for robustness testing)
- The benchmark tests both presence and absence of spatial features to avoid positive-only bias
- Human agreement ranges allow for natural variation in spatial perception and counting
- All measurements use metric units based on the specified GSD (Ground Sampling Distance)
- Count ranges use proportional MADc (0.19) so larger counts have wider acceptable ranges

## Source Datasets & Attribution

SQuID is constructed from publicly available remote-sensing datasets. We use only images from published validation or test splits and comply with the original dataset licenses.

- **DeepGlobe**  
  Ilke Demir et al., *DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images*, CVPR Workshops 2018.  
  Source: https://deepglobe.org/

- **EarthVQA**  
  Junjue Wang et al., *EarthVQA: Towards Queryable Earth via Relational Reasoning-based Remote Sensing Visual Question Answering*, ICCV 2023.  
  Source: https://github.com/WangJunjue/EarthVQA

- **Photovoltaic (Solar Panels) Dataset**  
  H. Jiang et al., *Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery*, Earth System Science Data, 2021.  
  Source: https://essd.copernicus.org/articles/13/5389/2021/

- **NAIP Imagery**  
  U.S. Geological Survey, *National Agriculture Imagery Program (NAIP)*.  
  Source: https://www.usgs.gov/core-science-systems/national-geospatial-program/national-agriculture-imagery-program

All derived annotations, questions, and acceptable answer ranges introduced in SQuID are released under **CC BY 4.0**.


## Citation

If you use this dataset, please cite:

```bibtex
@misc{massih2026reasoningpixellevelprecisionqvlm,
      title={Reasoning with Pixel-level Precision: QVLM Architecture and SQuID Dataset for Quantitative Geospatial Analytics}, 
      author={Peter A. Massih and Eric Cosatto},
      year={2026},
      eprint={2601.13401},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2601.13401}, 
}
```

---