AstraSense Whitepaper
Planetary Intelligence: A System for Detecting, Explaining, and Acting on Change
AstraSense / Jawelt Bay GmbH · info@ja-welt.com
§ Abstract
Satellite data has become abundant. Interpretation has not.
Across Earth observation and planetary science, vast amounts of imagery and sensor data are available, yet extracting clear, actionable insight remains difficult. Analysts are often left with fragmented signals, ambiguous patterns, and limited decision support.
AstraSense introduces Planetary Intelligence — a system designed to detect change, explain its causes, and guide action. This approach transforms raw satellite data into structured, interpretable intelligence across both terrestrial and planetary environments.
This document describes the system architecture, analytical methodology, data sources, and preliminary validation results. All performance metrics represent prototype evaluation findings and should be interpreted within that context.
§ Architecture
Five-Layer Intelligence Architecture
Planetary Intelligence is structured as a sequential pipeline — each layer transforming raw data into progressively higher-order insight. The architecture is designed for modularity, explainability, and cross-domain applicability.
Click each layer to expand its description. Architecture represents the experimental prototype design.
§ Intelligence Pillars
Core Capabilities
Multi-Signal Validation
Combines independent signals to reduce false positives. Signal agreement across multiple independent indicators increases confidence in risk assessments and reduces noise from single-source anomalies.
Explainable Intelligence
Each output includes structured reasoning ("Why"), counter-evidence, confidence levels, and downgrade conditions. The system is designed so that a user reading one line understands the situation — without requiring domain expertise.
Spatial Awareness
Users can identify high-risk regions instantly, explore risk distribution across areas, and prioritise regions using ranked outputs. The continuous risk surface model enables rapid situational awareness across large geographic extents.
Temporal Reasoning
The system evaluates trend direction (increasing/stable/decreasing), persistence over time, and signal stability. Temporal context is critical for distinguishing transient anomalies from persistent change patterns.
§ Data Sources & Ingestion
Satellite Data Integration
AstraSense integrates data from publicly available satellite missions. All data sources are openly accessible and regularly updated.
Earth Observation
Multiple optical and thermal satellite sources providing global daily coverage for vegetation, thermal anomaly, and surface change analysis.
Active Fire Monitoring
Near real-time fire detection data integrated from publicly available sources, providing global active fire coverage.
Mars Surface Data
Archival planetary mission datasets providing thermal, topographic, and surface imagery for Mars analysis.
§ Methodology
Six-Phase Analysis Pipeline
The analytical pipeline is designed to be auditable at every stage. Each phase produces explicit outputs that feed the next, creating a traceable chain from raw data to decision.
Phase 1 — Ingestion
Satellite Data Acquisition
Raw satellite data is acquired from publicly available sources. Data is normalized to a common spatial reference frame and temporal resolution before processing.
Phase 2 — Extraction
Signal Extraction & Indexing
Primary indicators are computed from raw imagery. Vegetation indices are derived from spectral bands. Thermal anomalies are identified against local baselines. Temporal deviation is computed against historical distributions.
Phase 3 — Validation
Multi-Signal Consensus
Extracted signals are validated against each other. Signal agreement is computed as the proportion of independent indicators pointing toward the same conclusion. Counter-signals are explicitly identified and weighted.
Phase 4 — Classification
Risk Level Assignment
Validated signals are combined using a weighted consensus model. Risk level (LOW/MEDIUM/HIGH) is assigned based on signal agreement, intensity thresholds, and temporal persistence. Confidence score is normalized to [0,1].
Phase 5 — Spatial Modelling
Risk Surface Computation
Point-based risk scores are extended to continuous spatial surfaces. A grid-based sampling framework generates risk estimates at regular intervals. Spatial interpolation produces a smooth risk surface. Contour boundaries are computed at defined threshold levels.
Phase 6 — Decision Synthesis
Action Output Generation
The final output combines risk classification, spatial context, temporal trend, and urgency into a structured decision object. A single-sentence summary is generated for rapid human interpretation. Recommended action (Investigate/Monitor/Ignore) is assigned.
§ Validation & Benchmarking
Preliminary Performance Metrics
All metrics below represent prototype evaluation results. These are internal benchmarks and have not been independently validated. Performance in production environments may differ.
Signal Agreement Rate
Prototype EvaluationHigh
Multi-signal consensus across independent indicators in prototype evaluation.
Detection Latency
Experimental ModuleLow
Time from region selection to full intelligence output in experimental module testing.
Spatial Coverage
Internal BenchmarkGlobal
Earth observation via multiple satellite sources. Mars coverage via archival mission datasets.
Confidence Scoring
Prototype EvaluationNormalized [0,1]
Every output includes a normalized confidence score derived from signal agreement, data quality, and temporal consistency.
§ Conclusion
Future Directions
AstraSense represents an early-stage prototype of a broader vision: planetary intelligence as a foundational capability for understanding and responding to global change. The current system demonstrates the core architecture and methodology, with significant opportunities for enhancement.
Planned research directions include independent validation partnerships, signal enhancement through advanced analytical methods, extended planetary dataset integration, and temporal resolution improvements. All development is guided by scientific rigour and transparent communication of uncertainty.
§ Glossary