Datasets:

PeterAM4
/

SQuID

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:

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

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:

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%)

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:

@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}, 
}