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+image_dataset/unified_visual_dataset.csv filter=lfs diff=lfs merge=lfs -text
+text_dataset/unified_text_dataset.csv filter=lfs diff=lfs merge=lfs -text
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README.md

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+# LemkinAI Multimodal Atrocity Identification Dataset
+
+## Dataset Overview
+
+This multimodal dataset contains comprehensive documentation of mass atrocities and human rights violations spanning 1980-2025, with 6.8+ million documented incidents from 195+ countries. The dataset combines textual documentation with satellite imagery and visual evidence for AI/ML research in atrocity detection, documentation, and prevention systems.
+
+## Dataset Structure
+
+```
+LemkinAI_Multimodal_Atrocity_Identification_Dataset/
+├── README.md (this file)
+├── text_dataset/
+│   └── [Compressed text datasets]
+└── image_dataset/
+    └── [Compressed image datasets]
+```
+
+## Dataset Components
+
+### Text Dataset
+The text component includes:
+- **Legal Documentation**: 5,918 international tribunal records
+- **Contemporary Events**: 475,000+ ACLED filtered mass atrocity events
+- **Humanitarian Violations**: 15,987 documented access violations
+- **Crisis Monitoring**: Crisis Group conflict monitoring data
+- **Military Documentation**: 280,000+ weapons and munitions records
+- **Country-Specific Collections**: Sudan, Syria, Yemen, Russia documentation
+
+### Image Dataset
+The visual component includes:
+- **Satellite Imagery**: 1,521 documented sites with destruction evidence
+- **Myanmar Documentation**: 662 satellite-documented destruction events
+- **Human Rights Evidence**: Visual documentation repository
+- **Geospatial Analysis**: Before/after imagery for temporal analysis
+
+## Data Schema
+
+### Text Records
+```json
+{
+    "incident_id": "unique_identifier",
+    "date": "ISO_8601_date",
+    "location": "standardized_location",
+    "latitude": "decimal_degrees_WGS84",
+    "longitude": "decimal_degrees_WGS84",
+    "event_type": "standardized_event_classification",
+    "actors": "involved_parties",
+    "fatalities": "casualty_count",
+    "source": "data_provenance",
+    "verification_status": "VERIFIED|REPORTED|ALLEGED",
+    "severity_level": "HIGH|MEDIUM|LOW",
+    "description": "incident_description"
+}
+```
+
+### Image Records
+```json
+{
+    "image_id": "unique_identifier",
+    "location_coordinates": [longitude, latitude],
+    "capture_date": "ISO_8601_date",
+    "image_type": "SATELLITE|GROUND|AERIAL",
+    "resolution": "spatial_resolution_meters",
+    "damage_assessment": "destruction_analysis",
+    "verification_status": "VERIFIED|UNVERIFIED",
+    "associated_incident_id": "linked_text_record"
+}
+```
+
+## Data Sources
+
+### Primary Sources
+- **ACLED**: Armed Conflict Location & Event Data Project
+- **International Criminal Court**: Legal proceedings and decisions
+- **Amnesty International**: Evidence Lab documentation
+- **Crisis Group**: Conflict monitoring and analysis
+- **Ocelli Project**: Myanmar satellite documentation
+- **OSMP**: Open Source Munitions Project
+- **UNHCR**: UN Refugee Agency reports
+
+### Temporal Coverage
+- **Start Date**: 1980
+- **End Date**: 2025 (ongoing)
+- **Peak Coverage**: 2010-2025 (highest documentation density)
+
+## Quality Standards
+
+### Data Quality Metrics
+- **Completeness**: Percentage of required fields populated
+- **Accuracy**: Cross-validation against multiple sources
+- **Timeliness**: Incident documentation delay from occurrence
+- **Geographic Precision**: Coordinate accuracy (typically <100m for satellite data)
+
+### Verification Levels
+- **VERIFIED**: Multiple source confirmation with high confidence
+- **REPORTED**: Single credible source documentation
+- **ALLEGED**: Unconfirmed reports requiring additional validation
+
+## Ethical Considerations
+
+### Privacy Protection
+- Personal identifiers redacted except for public officials/perpetrators
+- Victim testimonies anonymized
+- Location precision reduced for sensitive sites
+
+### Access Controls
+- Sensitive imagery requires research approval
+- Full dataset access restricted to verified research institutions
+- Public subset available for educational use
+
+## Usage Guidelines
+
+### Recommended Applications
+- **Atrocity Early Warning Systems**: Temporal pattern analysis
+- **Geospatial Conflict Monitoring**: Satellite change detection
+- **Legal Evidence Analysis**: Documentation for tribunals
+- **Humanitarian Response**: Crisis mapping and needs assessment
+- **Academic Research**: Conflict studies and human rights research
+
+### Technical Requirements
+- **Storage**: 50GB+ for full dataset
+- **Memory**: 8GB+ RAM for processing
+- **Processing**: Recommended GPU for image analysis tasks
+- **Software**: Python 3.8+, pandas, geopandas for data manipulation
+
+## Citation
+
+```bibtex
+@dataset{lemkinai_multimodal_atrocity_2025,
+    title={LemkinAI Multimodal Atrocity Identification Dataset},
+    author={LemkinAI},
+    year={2025},
+    url={https://huggingface.co/datasets/LemkinAI/Multimodal_Atrocity_Identification_Dataset},
+    note={Comprehensive multimodal documentation of mass atrocities and human rights violations, 1980-2025}
+}
+```
+
+### Source Attribution
+When using this dataset, please cite the original data sources:
+- Armed Conflict Location & Event Data Project (ACLED)
+- Amnesty International Evidence Lab
+- International Criminal Court
+- Crisis Group
+- Ocelli Project
+- United Nations agencies
+
+## License
+
+This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0) for research and educational purposes. Commercial use requires separate licensing agreements with original data providers.
+
+## Contact
+
+For dataset access, technical support, or collaboration inquiries:
+- **Organization**: LemkinAI
+- **Dataset Issues**: Open GitHub issue in repository
+- **Research Collaboration**: Contact through Hugging Face dataset page
+
+## Dataset Statistics
+
+- **Total Records**: 6.8+ million incidents
+- **Countries Covered**: 195+
+- **Temporal Span**: 45 years (1980-2025)
+- **Image Collection**: 38GB+ visual evidence
+- **Data Quality**: 87% green status (ready for analysis)
+- **Last Updated**: November 2025
+
+## Changelog
+
+### Version 1.0 (November 2025)
+- Initial release with unified text and image datasets
+- Standardized schema across all data sources
+- Comprehensive quality assessment and validation
+- BigQuery integration for large-scale analysis

image_dataset/high_resolution_images.zip

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image_dataset/image_dataset_README.md

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+# 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:
+
+```json
+{
+  "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
+```python
+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
+```python
+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
+```python
+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:
+
+```python
+# 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:
+
+```bibtex
+@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
+
+---
+
+**Dataset Version**: 1.0  
+**Last Updated**: November 2024  
+**Total Size**: 38GB  
+**Validation Status**: Professionally Verified

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