image_dataset/image_dataset_README.md

Satellite Imagery Dataset - MAID

Dataset Overview

The satellite imagery component of the Multimodal Atrocity Identification Dataset (MAID) contains 19,950 georeferenced satellite images covering global conflict zones and human rights violation sites. This dataset combines professional validation from international human rights organizations with comprehensive geospatial analysis.

Dataset Statistics

• Total Records: 19,950 georeferenced locations
• Total Size: ~38GB
• Image Types: Multi-spectral satellite imagery (Panchromatic, RGB, Pansharpened, RGBN)
• Coverage: Global conflict zones with professional human rights validation
• Professional Validation: Amnesty International, Displacement_Documentation, ConflictZone_Monitor

Data Structure

image_dataset/
├── metadata/
│   ├── unified_mass_atrocity_dataset.jsonl    # 19,950 records with coordinates
│   ├── unified_mass_atrocity_dataset.csv      # CSV format for analysis
│   └── integration_report.json                # Quality metrics and validation
├── high_resolution/                           # 18GB professional imagery
│   └── [1,524 high-resolution satellite images]
└── low_resolution/                            # 20GB additional coverage  
    └── [1,523 low-resolution satellite images]

Data Sources

Integrated Sources (19,950 total records)

1. Event_Database: 15,542 records - Armed conflict events with satellite validation
2. Satellite_Analysis: 1,366 records - UN satellite analysis of conflict zones
3. Damage_Assessment: 850 records - Building damage assessment imagery
4. Environmental_Impact: 671 records - Flood damage analysis with before/after imagery
5. HR Visual Dataset: 1,521 records - Professional human rights validation
   • Amnesty International: 189 verified sites
   • Displacement_Documentation: 981 refugee camp and displacement sites
   • ConflictZone_Monitor: 351 artisanal mining conflict zones

Image Quality Metrics

Visual Evidence Quality

• 98.4% of imagery has <10% cloud cover
• Multi-spectral coverage: Panchromatic, RGB, Pansharpened, RGBN bands
• Professional validation: Verified by international human rights organizations
• Average confidence score: 95%
• Coordinate accuracy: 100% coordinate completeness in HR visual dataset

Geographic Coverage

• Global scope: Covers all major conflict zones and human rights violation sites
• Cross-validation: 1,521 matches within existing coordinate framework
• Geospatial correlation: All coordinates linked to corresponding satellite imagery

Metadata Schema

Each record in the dataset contains:

{
  "record_id": "unique_identifier",
  "latitude": "decimal_degrees", 
  "longitude": "decimal_degrees",
  "event_type": "conflict_type_classification",
  "severity_score": "0.0_to_1.0_scale",
  "confidence": "validation_confidence_level",
  "source_organization": "validating_organization", 
  "date_acquired": "image_acquisition_date",
  "image_bands": ["available_spectral_bands"],
  "cloud_cover": "percentage_cloud_coverage",
  "ground_sample_distance": "meters_per_pixel"
}

Usage Examples

Loading Metadata

import pandas as pd
import json

# Load JSONL format
records = []
with open('metadata/unified_mass_atrocity_dataset.jsonl', 'r') as f:
    for line in f:
        records.append(json.loads(line.strip()))

# Or load CSV format
df = pd.read_csv('metadata/unified_mass_atrocity_dataset.csv')
print(f"Total records: {len(df):,}")

Accessing Satellite Imagery

from pathlib import Path
import matplotlib.pyplot as plt
from PIL import Image

# High-resolution imagery path
high_res_path = Path('high_resolution/')
low_res_path = Path('low_resolution/')

# Example: Load specific image
sample_dirs = list(high_res_path.iterdir())[:5]
for img_dir in sample_dirs:
    rgb_image = img_dir / f'{img_dir.name}_rgb.png'
    if rgb_image.exists():
        img = Image.open(rgb_image)
        plt.figure(figsize=(10, 10))
        plt.imshow(img)
        plt.title(f'Sample: {img_dir.name}')
        plt.axis('off')
        plt.show()

Geospatial Analysis

import geopandas as gpd
from shapely.geometry import Point

# Create GeoDataFrame from coordinates
geometry = [Point(record['longitude'], record['latitude']) for record in records]
gdf = gpd.GeoDataFrame(records, geometry=geometry, crs='EPSG:4326')

# Geographic distribution analysis
print("Records by region:")
print(gdf.groupby('source_organization').size())

# Conflict hotspot analysis
conflict_density = gdf.dissolve(by='event_type').geometry.bounds
print("Geographic bounds by conflict type:")
print(conflict_density)

Integration with Text Dataset

This imagery dataset is designed to work seamlessly with the text component of MAID:

# Cross-reference with text dataset
import sys
sys.path.append('../text_dataset')

# Load both datasets
text_records = json.load(open('../text_dataset/comprehensive_mass_atrocity_database_full.json'))
image_records = [json.loads(line) for line in open('metadata/unified_mass_atrocity_dataset.jsonl')]

# Find geographic matches
def find_nearby_incidents(text_lat, text_lon, image_records, threshold_km=10):
    from geopy.distance import geodesic
    matches = []
    for img_record in image_records:
        distance = geodesic((text_lat, text_lon), 
                          (img_record['latitude'], img_record['longitude'])).kilometers
        if distance <= threshold_km:
            matches.append((img_record, distance))
    return sorted(matches, key=lambda x: x[1])

# Example usage
text_incident = text_records['records'][0]
if 'location' in text_incident:
    nearby_imagery = find_nearby_incidents(
        text_incident['location']['latitude'],
        text_incident['location']['longitude'], 
        image_records
    )
    print(f"Found {len(nearby_imagery)} nearby satellite images")

Use Cases

Machine Learning Applications

• Damage Assessment: Training models to detect building destruction and infrastructure damage
• Conflict Prediction: Combining satellite imagery with temporal analysis for early warning systems
• Multi-modal Analysis: Cross-referencing satellite evidence with textual incident reports
• Change Detection: Before/after analysis of conflict zones and humanitarian crises

Research Applications

• Human Rights Documentation: Visual evidence for international legal proceedings
• Displacement Monitoring: Tracking refugee movements and camp establishment
• Environmental Impact: Assessing ecological damage from conflicts and violations
• Academic Studies: Computational analysis of conflict patterns and geographic factors

Technical Specifications

Image Formats

• File Format: PNG (lossless compression)
• Bit Depth: 8-bit and 16-bit depending on source
• Coordinate System: WGS84 (EPSG:4326)
• Naming Convention: {source}_{region}_{date}_{band}.png

Spectral Bands Available

• Panchromatic: High spatial resolution grayscale
• RGB: True color composite
• Pansharpened: Enhanced resolution color imagery
• RGBN: RGB + Near-infrared for vegetation analysis

Quality Assurance

Validation Process

1. Coordinate Verification: GPS coordinates validated against multiple sources
2. Professional Review: Human rights experts verified incident locations
3. Cross-Reference: Multiple satellite passes confirm location accuracy
4. Metadata Completeness: All records include comprehensive attribution data

Quality Metrics

• Spatial Accuracy: <10m average positional error
• Temporal Relevance: Images acquired within 6 months of reported incidents
• Spectral Quality: Radiometrically calibrated imagery
• Coverage Completeness: 98.4% cloud-free imagery

Ethical Considerations

Responsible Use Guidelines

• Academic Research: Approved for scholarly analysis and publication
• Human Rights Advocacy: Supporting documentation of violations for legal proceedings
• Policy Development: Evidence-based humanitarian and conflict resolution policies
• Technology Development: Building AI systems for conflict prevention and response

Prohibited Uses

• Individual Identification: No tracking or identification of specific persons
• Military Targeting: Not for operational military intelligence or targeting
• Commercial Surveillance: No commercial surveillance or monitoring applications
• Privacy Violation: Respect for civilian privacy and data protection standards

Data Provenance

All imagery is sourced from:

• Open-source satellites: Publicly available satellite imagery
• Professional organizations: Validated by international human rights bodies
• Academic partnerships: University research collaborations
• Government declassified: Released government satellite analysis

Citation

If you use this dataset in your research, please cite:

@dataset{maid_imagery_2024,
  title={MAID Satellite Imagery Dataset: Global Conflict Zone Analysis},
  author={Lemkin AI},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/LemkinAI/Multimoda_Atrocity_Identification_Dataset},
  note={19,950 georeferenced satellite images with professional human rights validation}
}

License

This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0) license with the following requirements:

• Attribution: Cite dataset creators and contributing organizations
• Academic Use: Freely available for research and educational purposes
• Responsible Use: Adhere to ethical guidelines for human rights research
• Non-Commercial: Professional validation sources require non-commercial use

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Dataset Version: 1.0
Last Updated: November 2024
Total Size: 38GB
Validation Status: Professionally Verified