Add SQuID dataset card with statistics

README.md

@@ -1,33 +1,232 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: image
-    dtype: image
-  - name: question
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: type
-    dtype: string
-  - name: tier
-    dtype: int32
-  - name: gsd
-    dtype: float32
-  - name: acceptable_range_lower
-    dtype: float64
-  - name: acceptable_range_upper
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 3939200871
-    num_examples: 2000
-  download_size: 3958457415
-  dataset_size: 3939200871
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: cc-by-4.0
+task_categories:
+- visual-question-answering
+language:
+- en
+tags:
+- satellite-imagery
+- spatial-reasoning
+- benchmark
+- quantitative-reasoning
+size_categories:
+- 1K<n<10K
 ---
+
+# SQuID: Satellite Quantitative Intelligence Dataset
+
+A comprehensive benchmark for evaluating quantitative spatial reasoning in Vision-Language Models using satellite imagery.
+
+## 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
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@article{massih2025squid,
+  title={Preserving Pixel-Level Precision: SQuID Dataset and QVLM Architecture for Quantitative Geospatial Reasoning},
+  author={Peter A. Massih, Eric Cosatto},
+  journal={arXiv preprint arXiv:XXXX.XXXXX},
+  year=2025
+}
+```
+
+---
+*Generated on 2026-01-18 17:13:25*