Technical Document
AstraSense White Paper
Planetary Intelligence: A System for Detecting, Explaining, and Acting on Change
Version 1.0 · April 2026 · AstraSense
1. Introduction
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.
2. Problem Statement
Despite advances in remote sensing, three core challenges persist:
2.1 Fragmented Signals
Satellite analysis often relies on isolated indicators, leading to incomplete interpretations.
2.2 Lack of Explainability
Most systems highlight that something changed, but not:
• why it changed
• how reliable the signal is
• what should be done
2.3 Limited Decision Support
Outputs are typically analytical, not actionable. Decision-makers must still interpret results manually.
3. System Overview
Planetary Intelligence is a multi-layer intelligence system composed of five integrated layers:
3.1 Detection Layer
Identifies raw signals of change using:
• Vegetation indices
• Thermal anomaly detection
• Temporal persistence
• Statistical anomalies
3.2 Interpretation Layer
Transforms signals into structured meaning:
• Risk level (LOW / MEDIUM / HIGH)
• Confidence scoring
• Signal agreement across indicators
• Counter-signals (contradictory evidence)
3.3 Spatial Intelligence Layer
Extends analysis across geography:
• Risk Surface (heatmap via spatial sampling + interpolation)
• Contour detection (threshold-based regions)
• Peak detection (local maxima identification)
3.4 Decision Layer
Adds temporal and behavioural context:
• Urgency (IMMEDIATE / MONITOR / LOW)
• Signal stability (STABLE / EMERGING / VOLATILE)
• Consistency (CONSISTENT / FLUCTUATING)
• Time-to-confidence estimation
3.5 Action Layer
Outputs clear, consistent recommendations:
Investigate · Monitor · Ignore
This closes the loop from observation to action.
4. Core Capabilities
4.1 Multi-Signal Validation
Combines independent signals to reduce false positives.
4.2 Explainable Intelligence
Each output includes:
• Structured reasoning ("Why")
• Counter-evidence
• Confidence levels
• Downgrade conditions
4.3 Spatial Awareness
Users can:
• Identify high-risk regions instantly
• Explore risk distribution across areas
• Prioritise regions using ranked outputs
4.4 Temporal Reasoning
The system evaluates:
• Trend direction (increasing / stable / decreasing)
• Persistence over time
• Signal stability
4.5 Prioritisation Engine
Automatically identifies top regions requiring attention, including:
• Risk score
• Affected area (km²)
• Signal agreement
• Summary explanation
5. Fire Detection & Risk Module
The system integrates real-time satellite fire data from publicly available sources:
• Thermal anomaly detection
• Hotspot clustering and intensity estimation
• Spatial distribution of fire activity
Fire Risk Assessment
Rather than predicting fires, the system identifies "elevated fire risk based on early signals of vegetation stress and thermal anomalies."
Outputs include:
• Fire Risk Level
• Supporting signals
• Confidence
• Recommended action
6. Risk Surface Modelling
AstraSense introduces a continuous spatial risk model.
Method:
• Grid-based sampling across viewport
• Per-point risk computation
• Spatial interpolation
• Confidence-weighted rendering
Visual Features:
• Heatmap (green → orange → red)
• Confidence-based opacity
• Contour boundaries at defined thresholds
• Peak detection for interaction
This enables rapid situational awareness across large regions.
7. Human-Centred Design
The system is designed for interpretability.
Key UX Components:
• Scan Mode (guided detection experience)
• Context Panels (structured explanation)
• Priority Regions (top actionable areas)
• Decision Sentence (single-line summary)
Design Principle:
If the user reads one line, they understand the situation.
8. Data Sources
The system is built on publicly available datasets, including:
• Earth observation data from space agency missions
• Optical and thermal satellite imagery (global daily coverage)
• Active fire monitoring data (near real-time)
• Archival planetary mission datasets (Mars surface analysis)
These datasets provide global, continuously updated coverage.
9. Use Cases
9.1 Environmental Monitoring
• Vegetation decline
• Deforestation
• Ecosystem stress
9.2 Fire Risk Assessment
• Hotspot detection
• Early warning signals
• Regional risk mapping
9.3 Geospatial Intelligence
• Land change analysis
• Anomaly detection
• Pattern recognition
9.4 Planetary Analysis
• Surface change detection on Mars
• Albedo shifts
• Dust movement
10. Differentiation
AstraSense differs from traditional tools in three ways:
10.1 From Data to Decision
Not just analysis — but clear action output.
10.2 Explainability First
Every result includes reasoning, uncertainty, and counter-evidence.
10.3 Cross-Domain Intelligence
Same system applies to:
Earth → Mars → future planetary datasets
11. Limitations
The system acknowledges constraints:
• Dependent on satellite data resolution and availability
• Temporal gaps may affect confidence
• Environmental variability (e.g. seasonality) influences interpretation
12. Conclusion
AstraSense introduces a new approach to geospatial intelligence:
from observation → to explanation → to action
By combining multi-signal analysis, spatial modelling, and decision logic, the system enables users to:
• Identify where change is happening
• Understand why
• Act with confidence
13. Contact
AstraSense — Planetary Intelligence Platform
info@astrasense.space