The following example future-world narratives provide a starting point for the workshop participants. They are open to refinement and for each, you may provide your own version for a Narrative prior to the workshop using the feedback form provided.
Scenario 1. Climate Extremes and Persistent Environmental Stress
By 2040–2060, climate and environmental pressures have intensified beyond historical design assumptions. Extreme events are more frequent, more spatially complex, and more tightly coupled across domains (atmosphere, ocean, cryosphere, land). Tipping Points have been reached and triggered a sequence of unexpected Earth System behaviours. Long-term trends require sustained, standards-based traceable measurements, that are stable over decades, while rapid-onset events demand high-temporal-resolution monitoring.
Earth Observation systems have evolved to provide continuous, resilient, and globally consistent measurements under all conditions. Architectures prioritise long-term stability, calibration integrity, and redundancy, alongside the ability to capture extremes and monitor Tipping Point post-phase evolution without saturation or data loss. Constellations combine reference missions with responsive elements to ensure both continuity and adaptability.
The system is increasingly judged on its ability to deliver trusted observations that support climate monitoring, attribution, and operational response simultaneously, placing new demands on observation design, data continuity, and cross-domain integration.
If you don't like this version, please provide your Alternative Scenario 1 here.
Scenario 2. AI-Native Earth System Prediction
By 2050, Earth system prediction has become fundamentally AI-enabled. Hybrid approaches combining physics-based models and AI/ML dominate operational meteorology, ocean prediction, and climate services. These systems rely on vast volumes of consistent, high-quality observational data for training, validation, and real-time constraint.
Earth Observation is no longer only a source of inputs to models; it is the authoritative data backbone of prediction systems. Demand for Level-1 observations has increased significantly, with emphasis on consistency, calibration stability, and global coverage. Observation strategies are increasingly shaped by the needs of AI systems, including dense sampling, multi-modal data fusion, and temporal coherence.
EO architectures have adapted to include agile constellations, capable of rapidly addressing observational gaps (identified through AI diagnostics) with rapid (2-3 years) “EO concept to launch and operations” part of normal work. The system operates as a dynamic sensing layer, co-evolving with prediction systems to optimise overall performance.
If you don't like this version, please provide your alternative Scenario 2 here.
Scenario 3. Trust, Accountability and Decision-Grade Information
By 2040, Earth Observation is routinely used to support policy decisions, regulatory frameworks, and legal processes. EO-derived information underpins climate compliance, environmental monitoring, security applications, and economic decision-making. However, by 2045, user expectations across policy, security, and commercial sectors require higher accuracy, lower latency, and multi-source integration as standard. Decision-making increasingly depends on near-real-time, uncertainty-quantified information.
European EO systems remain constrained by legacy architectures, fragmented data access, and insufficient temporal resolution. Data are available, but not in the form, timeliness, or quality required.
As a result, European EO systems are no longer considered decision-grade infrastructure. While scientifically robust, they are not trusted for time-critical or legally binding applications. Users increasingly rely on external, vertically integrated providers that can deliver traceable, near-real-time and uncertainty-quantified information. Europe retains capability but loses operational relevance and influence in policy and regulatory domains.
If you don't like this version, please provide your alternative Scenario 3 here.
Scenario 4. Commercialisation and Hybrid EO Architectures
By mid-century, the Earth Observation landscape is characterised by strong integration between institutional and commercial actors. Commercial constellations provide high revisit rates, rapid deployment, and service-driven innovation, while institutional missions maintain reference measurements, long-term continuity, and scientific integrity.
EO architectures have evolved into hybrid systems, where public and private capabilities are combined to deliver performance, resilience, and cost-efficiency. Interfaces between these components are formalised through standards, procurement models, and data policies.
The system design challenge is no longer whether to integrate commercial capabilities, but how to do so without compromising coherence, interoperability, or trust. Governance frameworks play a central role in maintaining alignment between public objectives and market-driven services.
If you don't like this version, please provide your alternative Scenario 4 here.
Scenario 5. Loss of Free and Open Data: Fragmentation of the EO Data Commons
By 2040–2060, the global Earth Observation landscape has shifted away from free and open data. Increasing commercialisation, security concerns, and geopolitical pressures have led to restricted access, licensing constraints, and differentiated data tiers becoming the norm.
Europe’s long-standing commitment to free and open data is progressively weakened. Budget pressures and demands for cost recovery drive the introduction of partial paywalls, prioritised access, and usage restrictions across key datasets. At the same time, international partners and commercial providers adopt closed or subscription-based models, limiting interoperability and data exchange.
As a result, the previously coherent EO data ecosystem fragments. Users face inconsistent access conditions, licensing complexity, and reduced ability to integrate multi-source datasets. Research and innovation slow due to barriers to entry, while operational users increasingly rely on proprietary, vertically integrated data services that guarantee access and performance.
European EO systems remain technically strong, but the loss of a truly open data foundation undermines their role as a global reference infrastructure. Europe forfeits its position as the provider of a widely accessible data commons and loses influence over standards, ecosystem development, and downstream innovation, as value shifts towards closed data platforms and controlled information services.
If you don't like this version, please provide your alternative Scenario 5 here.
Scenario 6. Resilient EO under Systemic Disruption
By 2040–2060, Earth Observation systems operate in an environment where disruption is expected rather than exceptional. Cyber threats, infrastructure failures, supply-chain constraints, and space environment risks (including congestion and debris) all affect system performance.
EO architectures are therefore designed for graceful degradation and rapid recovery. Redundancy is implemented across space, ground, and data segments, and critical observations are ensured through diverse and distributed sensing strategies.
The system incorporates autonomous operations, adaptive tasking, and cross-system interoperability, enabling continuity of service under degraded conditions. Resilience is treated as a primary design parameter, influencing mission design, constellation architecture, data flows, and governance.
If you don't like this version, please provide your alternative Scenario 6 here.
Scenario 7. Fragmentation, Sovereignty and Strategic Autonomy
By 2040–2060, the global context for Earth Observation is shaped by geopolitical fragmentation and shifting alliances. Access to space, data, and infrastructure is no longer fully open, and dependencies on external partners introduce strategic risks.
Europe has responded by strengthening its strategic autonomy in key EO capabilities, while maintaining selective international cooperation where beneficial. EO architectures are designed to ensure independent access to critical observations, alongside interoperability with trusted partners.
The system must balance sovereignty, collaboration, and efficiency, requiring careful architectural choices in mission design, data policy, and infrastructure development. Governance and coordination mechanisms are central to maintaining coherence across a diverse and distributed European EO ecosystem.
If you don't like this version, please provide your alternative Scenario 7 here.