Onboard Data Processing and Feature Extraction Automation Workflow

By extracting features (e.g., ship detections, cloud masks) on-orbit, this workflow reduces downlink data volume by 70-90%. This directly lowers commercial ground station leasing costs and frees up antenna time for other satellites, improving the ROI of the entire ground segment. The architecture requires deploying lightweight, quantized ML models to space-grade GPUs or NPUs with strict power and thermal constraints.

Automated onboard processing delivers insights in minutes instead of hours by eliminating the queue for ground-based processing. For time-sensitive use cases like disaster response or maritime tracking, this latency reduction from ~3 hours to ~15 minutes is a competitive differentiator. The workflow involves real-time model inference pipelines, priority-based data product generation, and integration with low-latency downlink scheduling.

This edge automation workflow is foundational for scaling to thousands of satellites. It prevents ground processing infrastructure from becoming a bottleneck, enabling the constellation itself to become a distributed sensing computer. Implementation requires a robust model lifecycle management (MLOps) system for secure, over-the-air updates and version control across the fleet, integrated with platforms like Kubeflow or MLflow.

Automation enables the creation and direct downlink of higher-margin, analysis-ready data products (e.g., change detection alerts, classified imagery). This shifts the business model from selling raw pixels to selling insights, improving average revenue per task. The architecture must include quality validation agents on-board to ensure product accuracy before downlink, with configurable product pipelines for different customer SLAs.

Reducing the volume of data stored and transmitted lowers peak power draw on solid-state drives and communication subsystems, reducing thermal stress and wear. This contributes to longer reliable service life. The workflow design must include power-aware inference scheduling, tying model execution to power budget forecasts from the satellite's energy management system.

Automating feature extraction shifts complex data engineering and ML ops labor from the ground to automated, standardized on-orbit software. This reduces the headcount and specialized skills required in mission control for data handling, allowing teams to focus on higher-value fleet management and exception handling. Implementation requires comprehensive health monitoring and autonomous rollback capabilities for the on-board software stack.

This agent orchestrates the ingestion of raw, high-volume sensor streams (e.g., multispectral imagery, SAR, RF signals) from onboard payloads. It applies time-synchronization, calibrates for sensor drift, and packages data into standardized chunks for the processing pipeline. In a custom build, this component manages memory buffers and priorities to prevent data loss during peak collection events, directly impacting the quality and completeness of downstream features.

The core processing unit where containerized ML models (e.g., YOLO for object detection, U-Net for segmentation) are loaded and executed on space-grade GPUs or NPUs. A custom architecture implements dynamic model swapping based on the satellite's orbital position and tasking plan—running a ship detection model over oceans and a cloud classification model over land. This requires robust model versioning, hardware-aware optimization, and fallback logic to handle inference failures gracefully.

After inference, this component extracts and structures the results (e.g., bounding box coordinates, classification confidence scores, polygon vertices) into lightweight JSON metadata. It then applies lossless compression (like COBS) to this metadata, often achieving a 1000:1 reduction versus downlinking the raw image. The architecture must ensure metadata schema consistency for ground system integration and include checksums for data integrity validation post-downlink.

This intelligent queue determines which extracted features are transmitted based on dynamic priorities. It evaluates factors like feature age, customer SLA, event criticality (e.g., wildfire detection), and available ground contact windows. In a custom build, this manager integrates with the constellation's network scheduler, ensuring high-value insights are downlinked first. It also handles data purging policies when onboard storage limits are approached.

A monitoring agent that tracks model performance metrics (e.g., inference latency, confidence score drift, power consumption) and data distribution shifts. It automatically flags models for retraining when performance degrades beyond a threshold. The workflow packages relevant edge data samples and performance logs for secure, prioritized downlink to initiate ground-based retraining cycles, closing the loop for continuous model improvement without manual intervention.

This is the governance layer that validates all software updates, model deployments, and configuration changes before they are applied on-orbit. It checks digital signatures, runs simulations in a sandboxed environment, and ensures changes do not violate power, thermal, or memory constraints. In production, this component is critical for maintaining fleet homogeneity and security, requiring integration with secure ground-based mission control systems for final approval.