Data Flow Architecture

The platform follows a standardized data processing pipeline from collection to insights.

Processing Pipeline

Raw Data Collection 
    ↓
Validation & QC 
    ↓
Standardization 
    ↓
Processing & Analysis 
    ↓
Insights & Metrics 
    ↓
Reporting & Distribution

Stage 1: Raw Data Collection

Data is collected from multiple sources:

• Satellite Imagery - Automated satellite data acquisition
• IoT Sensors - Real-time sensor data streams
• Field Observations - Manual data entry
• Third-party APIs - External data sources

Stage 2: Validation & QC

Data quality assurance:

• Format Validation - File format and structure checks
• Data Quality Checks - Completeness and accuracy validation
• Geospatial Validation - Coordinate system and boundary checks
• Metadata Validation - Required metadata verification

Stage 3: Standardization

Data normalization:

• Format Standardization - Convert to internal formats
• Coordinate System - Standardize to WGS84 (EPSG:4326)
• Schema Mapping - Map to internal data models
• Unit Conversion - Standardize measurement units

Stage 4: Processing & Analysis

Core analysis and computation:

• Geospatial Processing - Spatial analysis and calculations
• ML Model Inference - Run AI/ML models on data
• Time-series Analysis - Temporal pattern analysis
• Metric Calculation - Compute biodiversity and carbon metrics

Stage 5: Insights & Metrics

Generate actionable insights:

• Aggregation - Project and site-level aggregations
• Visualization Data - Prepare data for charts and maps
• Trend Analysis - Identify patterns and trends
• Risk Assessment - Calculate risk scores and categories

Processing Times

Typical processing times vary by data type:

Data Type	Typical Processing Time
Small audio files (< 5 MB)	2-5 minutes
Images	1-3 minutes
Medium datasets	10-30 minutes
Large satellite jobs	1-4 hours
Complex analyses	4-24 hours

Processing status can be tracked via the processing_status and processing_message fields in API responses.