Dynamic Earth Observation Payload Tasking and Scheduling Automation

Manual schedulers cannot evaluate the combinatorial complexity of thousands of daily requests against real-time constraints like cloud cover, satellite memory, and ground station availability. An AI-driven scheduler continuously re-optimizes the tasking queue, filling every available imaging window with the highest-value job. This directly converts idle satellite time into billable capacity, improving the return on capital for the constellation.

The workflow automates the entire request-to-schedule pipeline: ingesting orders from customer portals (like UP42 or SkyWatch), validating feasibility, running optimization models, and outputting validated command sequences to mission planning software (e.g., AGI STK, FreeFlyer). This eliminates the full-time equivalent (FTE) roles dedicated to manual scheduling and conflict resolution, reallocating engineering talent to higher-value fleet management and anomaly response.

Automated multi-priority, multi-client optimization ensures high-value, time-sensitive requests (e.g., disaster monitoring, defense intelligence) are scheduled ahead of routine mapping jobs. By dynamically weighing SLAs, weather forecasts, and satellite visibility, the system guarantees premium customers get the responsiveness they pay for, directly impacting contract renewal rates and enabling tiered pricing models that capture more value.

A static, human-generated schedule cannot react to sudden events like wildfires, volcanic eruptions, or geopolitical incidents. An autonomous re-tasking agent, integrated with external event feeds, can evaluate satellite availability, calculate new slew maneuvers, and inject high-priority imaging tasks within minutes. This captures ephemeral, high-value data that would otherwise be lost, creating a competitive edge in intelligence and monitoring markets.

Manual scheduling risks command conflicts, thermal limit violations, or memory overruns that can damage payloads or cause data loss. The automated workflow embeds constraint checking (power, thermal, pointing, data volume) and validates every schedule against a digital twin of the satellite's state. This proactive guardrail prevents mission-threatening mistakes, protects multi-million dollar assets, and ensures data delivery integrity.

Adding satellites to a manually operated constellation linearly increases scheduling complexity and headcount needs. An automated tasking system scales sub-linearly; the core optimization engine and agentic workflows handle 50 satellites as efficiently as 5. This architectural advantage is critical for operators planning mega-constellations, as it decouples operational cost from fleet size and protects margins during aggressive expansion.

The workflow ingests imaging requests from diverse sources—customer portals, API feeds, internal mission queues—and normalizes them into a standard schema. This agent handles authentication, extracts geospatial footprints, desired parameters (resolution, spectral bands), and SLAs, then queues them for constraint analysis. Automating this eliminates manual data entry and request triage, reducing order-to-queue time from hours to minutes.

A core orchestration agent evaluates each request against a dynamic model of satellite states. It checks for orbital visibility, onboard memory and power budgets, sun elevation angles, and predicted cloud cover from weather feeds. Requests that pass are tagged with a feasibility score and potential satellite candidates. This replaces manual scheduler spreadsheets and heuristic guesswork, directly increasing the pool of schedulable tasks by 25-40%.

This agent runs a combinatorial optimization algorithm (often mixed-integer programming or heuristic search) to build a conflict-free, revenue-optimized schedule for a planning horizon (e.g., 24-72 hours). It balances priorities, SLAs, satellite wear-and-tear, and ground station availability. The output is a validated tasking timeline that integrates with mission planning software (e.g., AGI STK, NASA GMAT) for command generation.

A reactive agent monitors external event feeds (e.g., wildfire alerts, breaking news, ship-tracking AIS anomalies) and the internal schedule. When a high-value, time-sensitive opportunity is detected, it dynamically evaluates the cost of displacing lower-priority scheduled tasks, generates a revised, fuel-aware maneuver plan, and routes it for expedited approval. This captures revenue from opportunistic imaging that manual ops would miss.

Not all decisions are fully autonomous. The workflow includes configurable approval gates for schedule releases, high-cost re-tasking, or SLA exceptions. An agent presents the optimized schedule or change proposal with a clear rationale and impact analysis to a human operator via a dashboard, integrating with tools like Jira or ServiceNow for tracking. Exceptions (e.g., failed cloud check) are automatically routed for manual review or customer notification.

The entire workflow is instrumented for observability, logging every decision, constraint check, and optimization outcome. A digital twin of the constellation and scheduler allows for 'what-if' simulation of new pricing models or scheduling algorithms before deployment. Performance data (e.g., schedule adherence, revenue per pass) feeds back to retrain forecasting and optimization models, creating a closed-loop system that improves over time.