Infrastructure
Core technical architecture underpinning the ingestion, management, and dissemination of EO data.
Earth Observation Data Infrastructure (EODI) Standard
This document establishes a rigorous standard for Earth Observation Data Infrastructure (EODI), defining the essential capabilities that any credible EO data platform must provide. It emphasizes operational integrity over product-specific features, making clear what a high-functioning EODI shall do to support mission-critical applications.
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Introduction
The standard is organized into eight core domains – Infrastructure, Security, Automation, Consistency, Governance & Compliance, Reliability & Resilience, Scalability & Performance, and Notifications & Event Triggers – outlining for each what is expected, why it matters, and the key principles of an effective system. Together, these chapters set the minimum benchmark for any serious EODI serving technical and commercial users.
This standard directly addresses real operational challenges in the EO industry. It calls out common pain points such as
fragmented data silos, vendor lock in, labor intensive workflows, and the difficulty of managing petabyte scale datasets.
A core tenet of this standard is that a true Earth Observation Data Infrastructure must be fundamentally provider agnostic. The
infrastructure must operate as an independent instance that is free from the commercial interests of any satellite operator or
data vendor. If a vendor profits from selling imagery, they have an incentive to recommend their own collections ahead of better
aligned opportunities from competitors. An infrastructure that is not provably neutral becomes another data catalog with limited
autonomy, rather than a platform able to make optimal decisions across sensors.
The standard also defines baseline requirements for how this infrastructure is deployed and maintained. An EODI must be API
first to support automated and machine driven workflows, and it must be containerised to allow secure hosting inside customer
environments, government clouds, or private networks. These characteristics remove the need to grant third party operators
privileged access to sensitive systems or tasking logic.
Rather than promoting hypothetical concepts, the standard prescribes practical architectural patterns to overcome the issues
above and enable secure orchestration of both commercial and private satellites.
The guidance is grounded in proven practices: modular, interoperable architecture; automation and API-first design; robust governance; and scalable, resilient operations. Ultimately, this document is a strategic blueprint for building EO data infrastructures that are modular, scalable, resilient, and user-centric, enabling program operators to focus on extracting insights from data – not struggling with the underlying infrastructure – in a cost-effective and transparent manner.
Eight Core Domains
Essential capabilities that any credible EO data platform must provide.
Security
Protection of sensitive data and systems through comprehensive security measures.
Automation
Streamlined processes that reduce manual intervention and increase efficiency.
Consistency
Standardized approaches ensuring reliable and predictable operations.
Governance & Compliance
Framework for managing operations within regulatory and policy requirements.
Reliability & Resilience
Systems designed to maintain operations under various conditions and recover from failures.
Scalability & Performance
Ability to handle growing data volumes and user demands efficiently.
Notifications & Event Triggers
Real-time communication and automated responses to system events.
Core Domain
1. Infrastructure
What is Expected
The EODI's infrastructure is the core technical architecture underpinning the ingestion, management, and dissemination of EO data. It must seamlessly integrate many distributed components, data sources, and partner inputs into a unified whole that behaves as a single high-functioning platform. In practice, this means supporting multi-constellation operations: data from numerous satellite missions and providers are orchestrated under one system so that users experience one cohesive environment.
The infrastructure must encompass end-to-end workflows covering everything from initial data acquisition or satellite tasking, through processing and cataloging, to final delivery. It should provide both intuitive, user-centric interfaces and robust programmatic APIs, allowing users to self-service their needs (e.g. discover, order, and retrieve imagery) with minimal manual involvement. In essence, the EODI infrastructure is the backbone that ensures all other aspects (security, automation, etc.) function in concert.
Earth Observation Data Infrastructure is the core system that ingests, manages, and delivers EO data as one coherent platform. It must integrate distributed sensors, archives, and providers under a single architecture so users experience one system rather than many. The scope covers the end-to-end flow from tasking and acquisition through processing, cataloging, and delivery, with both a clear web UI and complete APIs so users can search, order, and retrieve without manual handoffs.
A unified platform removes silos and queue friction. Requests should trigger automated fulfilment that schedules collections, ingests results, standardizes outputs, and delivers them reliably, shrinking latency, reducing operational overhead, and building trust that the same system will perform under load and during critical windows.
Key Infrastructure Principles
Unified Data Integration: The EODI must bring together all relevant EO data sources (satellites, sensors, archives) under a common framework. Using a distributed "system of systems" architecture, the platform supports specialized processing for different data types while still presenting a unified interface. In practice this means maintaining a centralized catalog or metadata repository that enables discovery across the entire holdings – for example, NASA's Common Metadata Repository unifies search over all EOSDIS datasets. Users shouldn't need to know which satellite or archive a given dataset came from; the infrastructure abstracts sources into one cohesive data lake. Data from new suppliers or missions can be plugged in without disrupting the overall system, ensuring the platform continuously grows in scope without fragmenting. Consolidate satellites, sensors, and archives behind a common catalog and metadata layer so the platform hides source quirks and presents a single discovery and retrieval surface.
Self-Service and APIs: Make every core function available in the UI and via stable APIs so users and external systems can place orders, track state, and pull results without tickets or emails.
Automated Fulfilment: Trigger deterministic workflows for tasking, acquisition monitoring, ingestion, preprocessing, and delivery, with recorded state transitions so every step is auditable.
Modular and Cloud-Native: Use independently deployable services for catalog, ordering, processing, delivery, and authentication, scaling components in isolation and updating them without downtime.
Core Domain
2. Security
What is Expected
Security is a foundational aspect of any EO data platform – an EODI must safeguard data and operations through robust measures at every layer. This includes controlling access to data and functions (strong authentication and fine-grained authorization), protecting data both in storage and in transit (encryption everywhere), and monitoring for any malicious or inappropriate activity. Security-by-design should be evident throughout the system: from user login and API key management, to network architecture and software development practices. The platform is expected to isolate each customer's data and processing in a multi-tenant environment such that one user cannot accidentally or intentionally access another user's assets. All data must be encrypted end-to-end, meaning it stays encrypted on disk and travels only over secure, encrypted channels. Strong identity management is required (e.g. support for multi-factor authentication and single sign-on integration) along with role-based access controls so that only authorized personnel can reach sensitive datasets or administrative functions. Additionally, the infrastructure must have audit logging and intrusion detection in place to catch and respond to security incidents. Compliance with relevant security standards and best practices (such as the principles of zero-trust architecture and least-privilege access) should be built in from the start.
Security must be designed in from identity to network to data. Enforce strong authentication, fine-grained authorization, tenant isolation, and encryption in transit and at rest while operating with continuous monitoring, intrusion detection, auditable logs, SSO, and MFA so every role adheres to least-privilege boundaries.
Why It's Important
Customers need absolute trust that the platform will protect their valuable and sensitive information. Many EODI users are in government, defense, or commercial sectors where EO imagery and derived products can carry strategic or competitive significance. A security breach compromising imagery (for example, revealing sensitive locations or projects) could have serious consequences. Likewise, a loss or tampering of user data or analytics could undermine missions and erode stakeholder confidence. Strong security, on the other hand, gives clients the confidence to integrate the EODI deeply into their workflows. If the platform meets high security standards (often demonstrated via certifications like ISO 27001 or SOC 2 compliance), users will treat it as a trusted extension of their own infrastructure. Conversely, if security is weak or uncertain, clients will hesitate – they might silo the platform from critical systems or avoid uploading their own data, greatly limiting its usefulness. In summary, without robust security, no amount of functionality will make an EO platform viable.
Users move sensitive and sometimes regulated workloads onto the platform. Breaches, leaks, or silent tampering are unacceptable, and only a platform that can demonstrate control, isolation, and traceability will be trusted in the critical path of operations.
Key Security Principles
Strong Authentication & Access Control: All access to the EODI must be gated by reliable authentication and fine-grained authorization. Every user (and service) should verify their identity via strong methods – e.g. passwords combined with MFA, secure tokens, or federated single sign-on – and then only be allowed to perform actions consistent with their role. The platform should enforce the principle of least privilege, meaning each account is granted the minimum permissions necessary for its tasks. A centralized identity and access management system should make it easy for administrators to add or remove users, assign roles, and audit permissions, with every access attempt logged for review. Enforce MFA, SSO, scoped tokens, and least-privilege RBAC with centralized identity to simplify joiners, movers, leavers, and service accounts.
Encryption & Data Protection: The platform must employ state-of-the-art encryption to protect data confidentiality and integrity at all times. Data at rest (in databases, object storage, backups, etc.) should be encrypted using strong algorithms, so that even if someone obtains the storage media, the content remains unreadable. Data in transit must be encrypted via protocols like HTTPS/TLS. Proper encryption key management is critical – keys should be stored securely (e.g. in hardware security modules or dedicated key vaults) with strict control over access and rotated periodically to reduce exposure. Encrypt everywhere with robust key storage and rotation, keeping keys in managed vaults or HSMs with clear administrator separation.
Continuous Monitoring & Incident Response: Security is not "set and forget" – a robust EODI needs active monitoring and a prepared incident response plan. Continuous monitoring means tracking system logs, user behavior, and network traffic for anomalies that could indicate a breach or misuse. Coupled with that, there must be an incident response process in place so the operations team can contain and investigate issues quickly, with regular security audits and penetration tests probing for weaknesses. Instrument logs, metrics, and network signals, alert on anomalies, and execute a tested incident-response playbook with clear containment and notification paths.
Compliance & Data Governance: Align with relevant standards and regional controls, respecting license terms and data-residency requirements through technical enforcement rather than policy alone.
Core Domain
3. Automation
What is Expected
Automation is at the heart of an effective EO data infrastructure. The platform should take care of repetitive, complex workflows automatically, without requiring manual intervention at each step. An EODI is expected to automate key processes from data acquisition all the way to data delivery. For instance, onboarding a new satellite data stream, updating a geospatial index, generating a derived product (like a mosaic or analysis-ready dataset), or fulfilling a user's imagery order should all be handled by automated pipelines once the initial trigger or request is received. The system must expose programmatic API endpoints for all major functions so that users and third-party systems can script and integrate these actions into their own software pipelines. Internally, the platform should follow an event-driven design so that when new raw data arrives it automatically kicks off processing jobs, moves outputs to the next stage, or notifies the user.
Automation should run the routine work. Once triggered, ingest pipelines, index updates, product generation, and order fulfilment must execute without manual steps, with every capability accessible through APIs and an event-driven core ensuring new data and job completions propagate state automatically.
Why It's Important
From an operational perspective, automation translates directly into speed, scalability, and consistency. Users benefit by being able to incorporate the EODI into their workflows seamlessly. Without automation, users would be forced to manually check for new data, run processing steps by hand, and distribute results – a slow, error-prone approach that doesn't scale when hundreds or thousands of new datasets are pouring in. Automation ensures consistency – the same task performed via an automated pipeline will execute the same way every time – and allows the infrastructure to respond to events faster than humans ever could.
Automation increases speed, reduces errors, and scales with demand, allowing users to integrate the platform into their own processes while eliminating polling, spreadsheets, and ad hoc scripts that fail under growth.
Key Automation Principles
API-First Design: Every core capability of the platform should be accessible via an API. The EODI's own web interface should effectively be a client of the same public APIs offered to users, ensuring anything possible in the UI can also be automated programmatically. The UI should consume the same surface customers use so anything clickable is scriptable, with clear versioning and documentation.
Workflow Orchestration: Automated platforms require an orchestration mechanism to manage complex multi-step workflows. The EODI should include a workflow engine or scheduler that knows how to execute and chain tasks in the right order while handling contingencies. Use orchestration to chain steps, manage retries, handle branching, and persist state with observable, re-runnable runs.
Event-Driven Operations: The EODI should be designed in an event-driven fashion so that components communicate and react to events rather than relying on periodic checks. This reduces latency and decouples services for greater scalability and reliability. Publish and react to events for data arrival, job state, and delivery to keep work moving without timers or polling.
Remove Manual Touchpoints: Continuously replace human checks with programmatic checks so operators handle exceptions and policy decisions, not the happy path.
User Automation: Provide examples, CLIs, and SDKs so customers can schedule pulls, push products to their storage, and integrate with their own notification or ETL stacks.
Core Domain
4. Consistency
What is Expected
Consistency in an EODI means that data and operations behave in a uniform, predictable manner across the entire system. The platform is expected to enforce standardized formats, protocols, and processes for all the data it handles, regardless of source or type. Whether a user is accessing optical imagery from one satellite or radar data from another, they should encounter similar metadata fields presented in the same structure and data files that adhere to a common set of conventions.
The infrastructure should include normalization pipelines that automatically convert incoming data into the platform's preferred formats and reference systems. All external interfaces – API responses, download links, coordinate systems – should likewise be consistent so a user never has to handle special cases or quirks for different collections; the EODI abstracts those differences away by organizing and presenting data systematically.
Data and interfaces should look and behave the same regardless of source, with standardized formats, metadata, and normalized inputs so downstream tools can assume shared conventions for projections, band ordering, and packaging.
Why It's Important
Consistency is operationally important because it dramatically reduces the burden on users and lowers the chance of errors. By providing data in a consistent, analysis-ready form, the EODI lets users focus on extracting insights rather than doing data janitorial work. It also fosters interoperability and fusion of data, making it much easier to combine information from multiple sources when they "fit together" out of the box.
Consistency removes integration drag so analysts and pipelines can be built once and reused across providers, with simpler multi-sensor fusion when products align on schema and shared quality gates.
Key Consistency Principles
Standardized Formats & Metadata: Adopt community-established standards (such as Cloud-Optimized GeoTIFF and the STAC metadata model) so that files are interoperable across common GIS tools without conversion and metadata is Findable, Accessible, Interoperable, and Reusable (FAIR). Use well-adopted formats and schemas so every item arrives with predictable structure.
Normalization Pipelines: Automatically harmonize incoming datasets into the platform-wide conventions, including reprojection, format conversion, metadata translation, and consistent quality preprocessing (e.g. cloud masking or radiometric calibration) so users receive analysis-ready products. Normalize inputs during ingestion so downstream systems inherit consistency by default.
Uniform Access Behaviour: Keep search patterns, item shapes, links, and authentication flows stable across datasets, handling special licensing rules internally while leaving interfaces fixed.
Quality and Versioning: Enforce quality checks and publish explicit versions for reprocessed data with transparent lineage so users can track updates safely.
Consistent Documentation: Maintain documentation for formats, fields, error codes, and examples so they stay aligned with the live system whenever interfaces change.
Core Domain
5. Governance & Compliance
What is Expected
Governance and compliance in an EODI context refer to the policies, controls, and oversight that ensure data is managed properly, lawfully, and in line with organizational requirements. An EODI must have strong governance mechanisms to control how data is used and shared, and to comply with all relevant laws, regulations, and contractual obligations associated with that data. This means clearly defining who can access what data and under what conditions and baking those rules into the system's design.
It also means managing the data lifecycle in a way that meets both operational needs and compliance requirements – for example, enforcing retention policies, ensuring right-to-be-forgotten requests can be honored if applicable, and providing auditable provenance for every dataset. The platform should support robust auditing and reporting capabilities so that at any time administrators can answer questions like who accessed a dataset, where it originated, and whether it has been modified.
Define who can access which datasets and actions, under what terms, and for how long, enforcing those rules in code while delivering full auditability, provenance, reporting, lifecycle automation, and support for regional or license constraints.
Why It's Important
Many EODI customers, especially government agencies and large enterprises, have strict compliance requirements. If they use a platform that cannot demonstrate proper governance controls, they may be legally unable or very reluctant to adopt it. Good governance prevents misuse of data, protects both the provider and users from legal trouble, and gives organizations confidence that they remain in control of their data even when it's hosted on the platform. In the event of disputes or security incidents, having proper governance logs and controls is essential to investigate and take action.
Enterprises and agencies need evidence of control, with automated enforcement preventing accidental misuse and clear logs accelerating investigations and compliance reviews while reducing legal risk.
Key Governance & Compliance Principles
Access Governance & Permissions: Implement fine-grained access controls and well-defined permission structures to govern who can access data and functionality. Support grouping users into roles or teams, approval workflows where necessary, and secure sharing mechanics that are tracked and revocable. Model roles, groups, projects, and dataset ACLs with request-and-approve flows and revocable sharing backed by audit trails.
License Management & Usage Compliance: Encode license rules and enforce them automatically to prevent accidental or intentional misuse of licensed datasets, including geographic or temporal restrictions, expirations, and transparency about permitted uses. Encode geographic, temporal, and redistribution limits, expiring access on schedule and surfacing permitted uses to the user.
Auditing & Accountability: Maintain tamper-evident logs of all significant actions and provide tools for reviewing them, ensuring transparency and accountability. Enable automated alerts for unusual behavior and furnish reports needed for formal compliance audits. Keep immutable logs of access, mutation, and sharing with administrator views and scheduled reports.
Data Lifecycle & Retention: Apply retention, archival, and deletion automatically, verifying backups and integrity on a schedule.
Legal & Regulatory Alignment: Honor residency, export controls, and sector policies through technical controls with clear user feedback when actions are blocked.
Core Domain
6. Reliability & Resilience
What is Expected
Reliability and resilience refer to the platform's ability to function continuously, deliver consistent performance, and recover gracefully from problems or disruptions. An EODI should be highly reliable: it must have minimal unplanned downtime, quick response times, and be engineered without single points of failure. Resilience emphasizes the platform's ability to absorb and adapt to unexpected events – from hardware failure to regional outages – and continue operating.
This involves redundant components, geographically diverse deployments, replicated data storage, and robust monitoring so degradation is detected and addressed before it becomes a major outage. Disaster recovery plans should define how the platform restores services if a catastrophic event occurs, with regular testing to ensure readiness.
Engineer for uptime, graceful degradation, and rapid recovery by removing single points of failure, replicating data, monitoring health, and testing disaster recovery and failover on a routine cadence.
Why It's Important
Many users embed the EODI deeply into time-sensitive workflows. If the platform is down or slow at critical moments, it can have serious operational consequences – delaying emergency response, interrupting automated agronomic programs, or undermining mission outcomes. Trust is earned through proven reliability, transparent incident communication, and rapid recovery when issues arise.
Operational users depend on predictable data, so outages and data loss erode trust and force costly customer workarounds; a resilient platform stays available during incidents and communicates clearly.
Key Reliability & Resilience Principles
High-Availability Architecture: Design for redundancy at every critical layer so that failures do not interrupt service. Use multi-AZ deployments, rolling updates, and zero-downtime change management to maintain uptime.
Data Redundancy & Backups: Store data with replication and regular backups so that a loss or corruption in one copy does not result in permanent data loss. Replicate hot paths, verify backups, and meet documented RPO and RTO targets.
Resilient Processing & Delivery: Retry transient failures, checkpoint long jobs, provide alternate delivery routes, and use elastic capacity to absorb spikes.
Monitoring & Alerting: Instrument the platform with comprehensive monitoring of health, performance, and error metrics, coupled with prioritized alerts and on-call response so issues are detected and handled before users are impacted. Track saturation, errors, latency, and SLOs while exposing a status page or API for users.
Regular Testing & Drills: Run load tests and failover exercises, fixing gaps before real incidents surface.
Core Domain
7. Scalability & Performance
What is Expected
Scalability and performance are about the platform's capacity to handle growth and to provide fast, efficient service under load. An EODI must be designed to scale along multiple dimensions – data volume, number of users, and computational demand – without degradation of performance. The architecture should ensure that current performance levels can be maintained (or even improved) as the system scales to much larger workloads in the future.
This means embracing elastic infrastructure, efficient indexing, caching, and delivery mechanisms tailored to geospatial-temporal data, and distributed processing that can accelerate heavy analytics. Users should experience minimal wait times for searches and downloads even as the backend manages petabyte-scale archives and thousands of concurrent workflows.
Scale with data, users, and compute without degrading the experience by using elastic infrastructure, smart indexing, caching, and distributed processing that keep queries fast and delivery efficient at petabyte scale.
Programs grow, and if performance collapses under volume or concurrency, adoption stalls; efficient search and transfer keep analysis quick and costs predictable.
Key Scalability & Performance Principles
Elastic Resource Scaling: Use cloud-native or containerized workloads that can scale automatically as demand fluctuates, adding compute, storage, and network capacity dynamically and releasing it when no longer needed. Autoscale services, workers, and queues while scaling down when idle to control spend.
Optimized Data Indexing & Retrieval: Implement spatial and temporal indexing, caching, and cloud-optimized data formats so that queries return quickly and downloads are efficient even against massive catalogs. Use tiling and cloud-optimized formats to keep reads quick.
Parallel & Distributed Processing: Distribute heavy processing across multiple workers or clusters so large analytics jobs finish quickly, scaling roughly linearly with the resources added. Partition workloads and run them across clusters so big jobs finish within acceptable windows.
Continuous Performance Tuning: Benchmark, profile, and optimize hot paths, baking performance reviews into release cycles.
Geographic Distribution: Place services and caches near users, using CDNs or regional replicas to cut latency and share load.
Core Domain
8. Notifications & Event Triggers
What is Expected
Notifications and event triggers refer to the platform's capability to actively inform users or external systems about important events, rather than expecting users to continuously poll or check for updates. An EODI should have a robust notification subsystem that can alert users when certain conditions are met – for example, when a requested image is ready for download, when new data matching a saved search appears, or when a processing job completes. The system should support multiple channels such as email, SMS, in-app alerts, and machine-readable callbacks like webhooks.
Notify humans and systems when events matter, supporting email, in-app, SMS, and webhooks with user-defined subscriptions for saved searches, job states, or thresholds and flexible frequency or digest controls.
Why It's Important
This capability significantly improves efficiency and responsiveness for users. In many EO applications, timeliness is crucial. Consider disaster response, where responders need to know the moment new imagery is available; or monitoring scenarios where a threshold crossing must trigger immediate action. Event triggers also enable automation loops: a notification about new data availability can automatically kick off a user's own processing pipeline, update a dashboard, or inform downstream systems without human intervention.
Timely alerts close the loop from capture to action, eliminating polling so downstream automation starts on time and incidents are handled faster.
Key Notifications & Event Triggers Principles
Real-Time Data Alerts: Deliver notifications as close to real-time as feasible when events occur, supporting multiple channels such as email, SMS, web and mobile push, and webhooks so information reaches humans and machines without delay. Emit notifications quickly when items ingest, jobs complete, or orders deliver, including enough context for immediate action.
User-Defined Subscriptions & Filters: Allow users to create subscriptions or watches on certain criteria so they hear only about events that matter to them. Users should be able to save geospatial/temporal queries, job states, or other conditions and choose the frequency or digest format of alerts, controlling channels and cadence.
Integration with External Systems: Provide developer-friendly hooks (webhooks, message queues, standard messaging protocols) so notifications can trigger downstream automation and seamlessly plug into external workflows. Offer reliable webhooks and message-queue options so customer platforms can trigger pipelines, dashboards, or ticketing.
Reliable Delivery: Implement retries, dead-letter tracking, and delivery logs while preventing alert floods with batching and rate limits.
Operational Communications: Use a visible status channel for maintenance windows and incidents, sharing impact and remediation steps.