Satellite Imagery
Collection Planning

CPAW (Collection Planning & Analysis Workstation) combines high-fidelity spacecraft modeling with AI scheduling algorithms determining optimized satellite-target collection schedules for remote sensing missions. Algorithms evaluate collection opportunities for each target considering sensor capabilities, orbital dynamics, sun angle, forecast cloud cover (where applicable), and defined target collection constraints. Algorithms generate optimized collection plans maximizing collection quality and quantity across satellite constellations.

High-fidelity modeling accounts for spacecraft constraints—sensor field-of-view and footprints, squint angles, slew rates, power budgets, pointing constraints, data storage capacity, and orbital mechanics—ensuring collection plans respect actual operational capabilities. Generic planning tools apply simplistic rules and modeling missing spacecraft-specific limitations causing assignments violating physical constraints satellites cannot execute. Detailed modeling generates operationally feasible plans matching real spacecraft capabilities preventing wasted collection attempts on impossible assignments.

CPAW integrates cloud forecasts, sun angle calculations, and lighting conditions into collection opportunity scoring, optimizing for image quality not just satellite availability. Algorithms schedule collection opportunities based on predicted environmental conditions favoring successful captures. Planning without environmental awareness may schedule collections during predicted poor weather or lighting wasting satellite resources on unusable imagery clouded or poorly lit.

Yes. CPAW algorithm performance supports the dynamic update of collection assignments when satellites experience anomalies, weather predictions change, or new high-priority targets emerge scheduling alternative collection opportunities maintaining mission effectiveness. Systems without CPAWs performance cannot replan fast enough to keep up with the realities of the dynamic conditions affecting satellite remote sensing systems and customers with changing requirements.

AI algorithms compute and score thousands of target collection opportunities across the entire constellation – considering every imaging satellite's sensor capabilities, orbital position, pass geometry, and environmental factors simultaneously. Automated evaluation and AI-driven collection scheduling generates optimized collection schedules human planners and less capable scheduling tools cannot compete with due to the volume of opportunities and the balancing of multiple optimization factors involved.

CPAW can evaluate thousands of imaging opportunities for hundreds or thousands of targets simultaneously considering sun angle, cloud cover, sensor resolution, and orbital geometry where manual planners and simplistic planning tools check basic pass times for each target one satellite at a time. Manual assignment accepts the first "available" satellite pass, missing opportunities where a different constellation asset would deliver higher-quality imagery captured under optimal conditions. AI optimization determines the best collection opportunities for each target based on a multi-factor figure-of-merit and generates deconflicted collection schedules maximizing image scores across the entire constellation..

Yes. CPAW manages collection planning, recorder management, and uplink/downlink planning. Astro Scheduler integrates with CPAW to coordinate complex scheduling across multiple communication networks and ground station contacts. This integration ensures complete mission workflow—from collection planning through recorder allocation to downlink timing—operates cohesively without conflicts or missed data opportunities.

CPAW's configurable multi-factor figure-of-merit (FOM) scoring balances competing collection goals—image quality, area coverage, cloud cover, cost, and more—based on user-defined priorities. The FOM is quantifiable and adjustable, so you can shift optimization priorities as mission goals or conditions change without rebuilding schedules. This flexibility allows CPAW to work with any mission system: ground station availability, priority changes, weather updates, or new tasking all feed into FOM adjustments, keeping collection plans optimal despite changing constraints.

USSF deploys CPAW for Missile Track Custody (MTC) operations, optimizing multi-intelligence collection planning across the Resilient Missile Warning and Tracking (RMWT) MEO constellation. a commercial imagery operator uses a customized version of CPAW for its Direct Access Facilities, helping remote partners optimize reserved time on a commercial imagery operator satellites. CPAW supports government and commercial missions requiring constellation-scale optimization impossible through manual planning.

CPAW generates deconflicted, optimized collection plans in seconds to minutes across entire constellations. It computes and scores thousands of collection opportunities simultaneously—a scale manual planners and simplistic tools cannot match. Machine learning continuously improves opportunity scoring based on past results, so planning quality improves over time. The combination of speed, constellation-scale optimization, and adaptive learning delivers capability competitors cannot replicate through manual processes or generic scheduling tools.