C3 software from orbit to the tactical edge. Built by operators, deployed across the most demanding national-security missions.
Auria | Copyright 2026 | All rights reserved
Mission-ready integration
COLLECTING · 38.4°N 115.8°W
HeliOS · CPAW
Collection Active
EO/IR · GSD 0.41 m
Swath 24 km · Sun 62°
Cloud 8% · Downlink queued
COLLECTION PLANNING & ANALYSIS WORKSTATION
Satellite Imagery Collection Planning
AI-optimized collection planning software for imaging satellite constellations, balancing mission objectives against system constraints in real time.
THE PROBLEM
Imaging satellite operators need optimized plans to meet constraints and goals
01
System constraints are numerous, variable, and dynamic
02
Performance must meet large scale constellations and changing conditions and new tasking
03
Balance mission objectives to maximize return
THE SOLUTION
Mature state-of-the-art AI optimizes collection planning for any mission
01
AI optimization computes and scores collection opportunities across the constellation
02
Dynamic replanning adapts to imaging results, new tasking, and changing conditions.
03
Automated scheduling scales with constellation growth.
04
Enterprise integration coordinates optimized mission-wide imaging.
HOW IT WORKS
AI Scheduling Optimizes Collection Across Entire Constellation
CPAW models spacecraft systems, constraints, environmental conditions, and mission tasking through high-fidelity simulation.
Accurate constraint modeling
Intelligent opportunity scoring
Weather and lighting analysis
Competing target management
Strategic updates for changing conditions
Planning insights and mission effectiveness
High fidelity modeling accounts for field-of-view and sensor footprints, slew rates, power generation and consumption, pointing constraints, and data storage and downlink capacity to generate executable plans informed by real-world constraints.
Key Capabilities
- Models sensor capabilities, power, data storage, orbital mechanics, sensor and platform agility
- Ensures plans respect spacecraft operational constraints and meets tasking requirements
Imagery collection opportunity scoring quantifies timeslots for which all constraints are met. Multiple AI driven algorithms optimize to create the highest scoring plans balancing multiple goals across the entire constellation.
Key Capabilities
- Computes and scores thousands of collection opportunities
- Optimizes for image quality, area coverage, cloud cover, cost (or revenue), and more
- Scales for high performance collectors and large constellations
- Machine learning used to improve future opportunity scoring based on past results
Sophisticated environmental modeling identifies the most promising opportunities and scores them higher to ensure plans generated have the highest probability of success.
Key Capabilities
- Integrates weather forecasts and cloud cover probability
- Optimizes for sun angle and lighting conditions
- Maximizes successful high quality captures, minimizes failed collections
Algorithms select from and deconflict ranked/scored collection opportunities across the constellation to generate optimized constellation-level collection plans.
Key Capabilities
- Identifies and avoids scheduling conflicts automatically
- Resolves competing demands intelligently
- Maintains productivity while satisfying priorities
Adaptive replanning automatically updates the collection schedule when conditions change, maintaining imaging quality despite satellite anomalies, weather shifts, new tasking, and priority changes affecting original assignments.
Key Capabilities
- Responds to system anomalies and weather changes
- Updates to address new ad-hoc tasking in seconds
- Adapts to priority shifts automatically
- Maintains optimization despite disruptions
Demonstrable collection opportunity selection rationale and mission effectiveness metrics support confidence in automated scheduling.
Key Capabilities
- Visualizes collection plans and schedule performance
- Identifies constellation utilization and mission effectiveness through metrics
- Incorporates manual guidance and optimization goal updates without restarts
01
Constellation-Scale
Coordinates Unlimited Imaging Satellites
02
Orders of Magnitude Faster
Than Competitor Solutions
03
AI-Powered Optimization
Deconflicted and Optimized Plans in Seconds to Minutes
04
NOAA Landsat Program
Optimizes Collection Planning Operations
FAQ
Common Questions AboutImaging Collection Planning
What is CPAW and how does AI-powered imaging collection planning work?
How does high-fidelity spacecraft modeling improve collection planning?
What environmental factors does CPAW consider when planning imagery collections?
Can CPAW adapt collection plans when conditions or priorities change?
How does CPAW optimize imaging across multiple constellation satellites?
What makes CPAW different from manual imaging collection planning?
Can CPAW integrate with ground station scheduling and mission systems?
Can CPAW integrate with ground station scheduling and mission systems?
What imaging programs use CPAW for collection planning operations?
How does CPAW performance compare to competitor solutions?
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.
Ready To Maximize Imaging Constellation Productivity?
Whether coordinating government imaging missions or commercial Earth observation fleets, CPAW provides proven AI-powered optimization that maximizes return from high value assets.