SOAR

Objectives of the Product

Maritime routing and compliance reporting commonly rely on fragmented inputs: marine weather products of uneven skill, separate onboard logs and regulatory formats that demand manual reconciliation. Limited traceability drives conservative routing, frequent captain overrides of optimisation advice, higher fuel and GHG emissions and disputes where ‘what conditions actually occurred’ is difficult to evidence.

SOAR addresses this trust and performance gap through an end-to-end platform that connects data, decisions and evidence. It continuously ingests Earth observation data, AIS tracks and onboard telemetry, then time-aligns them, runs quality checks, and records provenance (where the data came from and how it was processed).

A fusion layer blends these sources to produce consistent marine weather fields and confidence metadata. On top, AI models improve short-term forecasts and translate sensor signals into sea-state conditions (e.g., GNN-GRU with physics-aware bias correction), providing frequent updates.

In parallel, SOAR estimates GHG emissions and generates compliance outputs aligned with EU ETS, EU MRV, and IMO DCS. Key datasets and reports are anchored in a tamper-evident permissioned ledger to support audits and dispute resolution. Targets include ≥20% better short-term accuracy than existing providers <10s dashboard responses, and ±5% emissions accuracy.

Customers and their Needs

SOAR targets shipping companies and fleet operators as primary customers, with captains’ teams, fleet performance managers and compliance/ESG officers as core end-users. Secondary customers and users include chartering teams, cargo owners/charterers, insurers and reinsurers, ports/offshore stakeholders, and voyage-optimisation or compliance software providers that integrate marine weather data and emissions intelligence via APIs.

Maritime operators need routing guidance they trust in practice: frequent, accurate forecasts that reflect observed conditions, clear confidence indicators, and evidence that supports decisions. They also need automated emissions monitoring and outputs aligned with EU ETS, EU MRV and IMO DCS to reduce manual reporting burden, errors and audit risk.

Charterers and insurers need independently defensible voyage reconstructions (conditions, routes, emissions) to accelerate claims handling, charter-party performance assessments and dispute resolution.

Integrators need stable, well-documented interfaces and provenance-rich data products to embed SOAR into existing workflows. Across all groups, needs include secure access control, GDPR-aligned handling of operational data, and tamper-evident records suitable for audits. These stakeholders are involved through requirements validation with early supporters and pilot partners/planning, vessel integration activities, and iterative feedback on dashboards, reports and API payloads to ensure operational fit.

Targeted customer/users countries

Primary focus: EU shipping operators and stakeholders exposed to EU ETS/EU MRV obligations (including operators trading to/from EU ports), with initial engagement anchored in Greece through the consortium and pilot ecosystem.

Secondary reach: international maritime operators, charterers, and insurers involved in EU and global trades and audit/compliance workflows.

Product description

SOAR is a cloud-native, microservice-based marine weather intelligence platform that integrates onboard telemetry, AIS and multi-source EO/reanalysis/forecast datasets into a unified data layer. A streaming ingestion service performs time-alignment, QA/QC, normalisation and provenance tagging; a fusion engine generates marine weather analyses (winds, waves, currents). The AI forecast suite combines data assimilation and spatiotemporal models (e.g., GNN-GRU) with physics-aware bias correction (PINN + wavelet residuals) to deliver rolling 0–72h forecasts plus 4–10-day outlooks, exposed through dashboards and APIs.

A routing component consumes the latest analyses and constraints to produce route recommendations and scenario comparisons; benefit targets include improved route adherence and fuel savings ranges used in the business case (e.g., 5–15%). An emissions module segments voyages, applies vessel-specific emission factors, propagates uncertainty, and formats outputs aligned with EU ETS, EU MRV and IMO DCS. A permissioned integrity layer anchors hashes of key datasets/reports to create tamper-evident, auditable voyage records.

Added Value

SOAR brings added value by addressing the adoption barrier that limits many marine weather services: maritime operators do not trust recommendations built mainly on numerical models or third-party forecasts, with limited linkage to what the vessel actually experienced. This drives overrides, inconsistent fuel consumption, and weak audit readiness. Many market alternatives sit in one slice of the value chain, marine weather intelligence, route optimisation, EO data services, or measurement hardware, leaving operators to stitch together data, reconcile assumptions, and defend outputs during disputes.

SOAR stands out because it links what was observed, what was decided and what was reported in one traceable chain. It combines Earth observation and reanalysis data with AIS and onboard telemetry to build a view of sea-state conditions along the route, and it reconstructs each voyage so users can clearly connect marine weather forecasting to routing decisions and resulting emissions.

Its AI models use onboard measurements to refine local sea-state conditions and reduce systematic marine weather forecast errors, which increases confidence and helps maritime operators make safer and smarter decisions. Finally, SOAR anchors key datasets and reports in a permissioned, tamper-evident record, providing evidence that is useful for charter-party claims, insurance underwriting, and EU ETS/EU MRV/IMO DCS audits. Everything is delivered through one platform (dashboards and APIs), reducing the need to stitch together multiple vendors.

Current Status

SOAR is in the product development and integration phase. The end-to-end architecture is defined, covering streaming ingestion, multi-source fusion, AI forecasting, emissions accounting, integrity anchoring, and the dashboard/API layer.

Initial versions of the ingestion and QA/QC pipelines and the fusion workflow operate in a development/test environment, enabling time-aligned processing of onboard telemetry, AIS and EO/reanalysis inputs. Early AI forecasting components have been exercised on historical datasets to benchmark reconstruction/forecast skill against reference baselines. A permissioned ledger environment for tamper-evident logging is configured for test deployments, alongside cloud-native microservices for scalable processing.

Work in progress includes expanding the data catalogue (public and commercial EO and partner feeds), refining model performance and uncertainty handling, integrating emissions algorithms and report formats (EU ETS, EU MRV, IMO DCS), and iterating dashboards and API payloads with early adopters and pilot stakeholders to confirm operational workflow fit

AgroEstate

Objectives of the Product

AgroEstate addresses the lack of transparent, standardised, and data-driven evaluation of agricultural land, a problem faced by farmers, agribusinesses, advisors, brokers, and energy producers when buying, selling, leasing, or assessing land. Today, land-related decisions rely heavily on fragmented data, local knowledge, manual assessments, and costly site visits, leading to uncertainty, hidden risks, and inefficient transactions.

AgroEstate provides a digital platform that delivers parcel-level land intelligence by combining Earth observation data, geospatial analytics, and contextual economic information into a single, user-friendly environment. The platform enables users to evaluate land productivity, crop suitability, climate and drought risk, renewable energy potential, and other key indicators through interactive maps, dashboards, and downloadable reports.

The solution is delivered through a B2B-first and B2B2C approach, engaging agribusinesses, cooperatives, advisors, brokers, and energy producers as primary users and distribution channels, while gradually enabling direct access for individual farmers at later stages. By embedding advanced analytics into existing professional workflows and offering scalable self-service tools, AgroEstate reduces uncertainty, improves decision-making, and increases transparency across the agricultural land market.

Customers and their Needs

AgroEstate targets multiple customer groups involved in agricultural land transactions and management, including agri-businesses and cooperatives, farmers, agri-investors, energy producers, sellers and lessors, real-estate agencies and brokers, and agricultural advisors and consultants. These users participate in the activity through requirements elicitation, MVP testing, and validation, providing operational feedback to ensure the platform reflects real market needs.

Across these groups, a common challenge is the difficulty of evaluating agricultural land objectively and consistently. Current practices rely on fragmented data sources, manual inspections, and experience-based judgement, making parcel comparison slow, costly, and uncertain. Farmers and agri-businesses need reliable information on productivity, climate risks, and crop suitability. Investors and energy producers require rapid screening and risk-return insights. Sellers, brokers, and advisors need credible, standardised information to support transparent transactions and professional recommendations.

AgroEstate addresses these needs by providing parcel-level evaluations through integrated EO analytics, climate indicators, and economic modelling, delivered via user-friendly dashboards. By reducing information asymmetry and simplifying complex land analysis, the platform supports faster decision-making, improved investment confidence, and more efficient agricultural land transactions.

Targeted customer/users countries

Greece, Serbia, Bulgaria, Hungary, Romania, Italy, Spain, France, Germany, UK, EU.

Product description

Today, agricultural land assessment relies either on costly expert appraisals and site visits, or on fragmented digital tools that address single aspects such as crops, climate, or soil in isolation. These approaches are slow, difficult to compare across parcels, and poorly aligned with real transaction and investment workflows.

AgroEstate differentiates itself by offering end-to-end, parcel-specific land intelligence tailored to concrete user decisions such as buying, selling, leasing, expanding operations, or screening land for renewable energy. Through integrated dashboards, users can virtually evaluate land performance, risk exposure, suitability, and value using consistent, standardised indicators designed specifically for land-related decisions.

AgroEstate is designed as a decision-support product for the land market, combining usability, comparability, and affordability. By delivering actionable insights at a fraction of the cost and time of conventional evaluations, AgroEstate lowers entry barriers, accelerates decision-making, and enables more transparent and efficient land transactions across Europe.

Added Value

AgroEstate brings added value by replacing fragmented, manual, and experience-based land assessment practices with a standardised, automated, and scalable digital approach. Today, agricultural land evaluation typically relies on local knowledge, isolated datasets, on-site visits, and consultancy-driven assessments that are costly, time-consuming, and difficult to compare across locations. Existing tools either focus on single indicators or require expert interpretation, limiting their usability and scalability.

AgroEstate differentiates itself by integrating multiple Earth observation data sources and analytics at parcel level into a single, coherent evaluation framework. Once processed, parcel analytics can be reused by multiple. The platform delivers consistent outputs through interactive dashboards, comparison tools, and reports, enabling objective, transparent decision-making across different users and regions.

Compared to competitors, AgroEstate combines automation, reusability, and role-based delivery, offering both self-service access and API integration for professional intermediaries. This allows the solution to scale efficiently, lower entry barriers for smaller users, and embed advanced analytics directly into real-world workflows, something traditional methods and most existing digital solutions do not provide.

Current Status

The activity was launched in late January 2026 and is currently in its initial phase.

Work currently in progress includes detailed requirement elicitation, prioritisation of services, and preparation of the system architecture and data pipelines. In parallel, engagement with supported user groups is ongoing to refine use cases and validation criteria. Upcoming activities include the start of core platform development and the initiation of iterative testing and validation cycles with stakeholders

ac-URBAN

Objectives of the Product

Analysing Earth Observation (EO) data is complex and often inaccessible to non-technical users, involving multiple technical steps like dataset selection, preprocessing, and analysis. This complexity challenges organisations such as municipalities and environmental groups, which need data-driven decisions for issues like climate adaptation and infrastructure planning.

Despite the availability of EO data, there’s a gap between its potential and actual use. Large Language Models (LLMs) combined with geospatial processing can bridge this gap, but current solutions rely on proprietary technology. Actinia-copilot URBAN offers an open-source alternative, ensuring digital sovereignty and user protection.

Initially focusing on urban climate adaptation, it helps municipalities and planners gain actionable insights for initiatives like urban greening. The system allows users to analyse urban structures for climate adaptation using natural language interactions, reducing the need for geospatial expertise. Users can select areas or rely on the system’s suggestions based on indicators like sealed surfaces.

Actinia-copilot URBAN automatically acquires and processes relevant EO data, presenting results through interactive maps and reports to support decision-making. Actinia-copilot URBAN integrates open-source LLMs with the geoprocessing engine actinia, enabling natural language queries for EO data analytics. This shifts the complexity to AI, empowering users across sectors to extract insights easily.

Customers and their Needs

The targeted customers and users of the product are primarily public sector organisations, civil engineering and consulting firms, environmental monitoring organisations, and non-governmental organisations (NGOs). These stakeholders are actively involved in spatial planning, infrastructure development, environmental assessment, and climate adaptation activities, all of which require reliable, spatially explicit, and evidence-based information.

The primary user needs include easy access to high-quality EO data, transparent and reproducible analytical workflows, and actionable outputs that can be readily interpreted by non-specialists. Key challenges in meeting these needs are the technical complexity of EO data processing, limited in-house geospatial expertise, high costs of proprietary solutions, and concerns regarding data governance and digital sovereignty. The product addresses these challenges by providing an open-source, natural language-driven interface that automates EO data analysis.

Targeted customer/users countries

The targeted customers and users are currently primarily located in European countries with a strong potential for subsequent adoption across additional international markets.

Product description

The product called actinia-copilot URBAN, is an open-source, AI-enabled Earth Observation analytics system designed to support urban planning and climate adaptation. It integrates large language models with the actinia geoprocessing engine to enable users to perform complex EO data analyses through natural language interactions.

The system automatically translates user queries presented in natural language into reproducible geospatial workflows, handling data discovery, acquisition, preprocessing, and analysis across heterogeneous EO datasets. The core innovation lies in combining open-source large language models with operational geospatial processing in a fully open-source AI stack, ensuring transparency, extensibility, and digital sovereignty. Unlike proprietary solutions, the system enables public-sector–compliant deployment and long-term sustainability.

Added Value

The ac-URBAN solution delivers significant added value over existing EO analytics platforms by combining natural-language access, advanced geoprocessing, and a fully open-source architecture. In contrast to many competitors that rely on proprietary LLMs and closed geoprocessing engines, ac-URBAN is built on open technologies, enabling transparency, auditability, and long-term sustainability.

A key differentiator is the deep integration of AI agents with the open-source actinia and GRASS GIS ecosystem. Rather than producing black-box results, ac-URBAN translates user queries into explicit, inspectable process chains, allowing users and institutions to understand, validate, and reproduce analytical outcomes. This is particularly critical for public-sector, research, and policy-driven urban applications where explainability and trust are essential.

Unlike competing platforms that offer fixed products or opaque AI-driven outputs, ac-URBAN supports dynamic, user-driven analyses across global EO datasets while also enabling the integration of high-resolution, locally provided data. By leveraging open-source AI components, Retrieval-Augmented Generation, and cloud-native geoprocessing, ac-URBAN empowers non-technical users without sacrificing scientific rigor. The result is a vendor-independent, extensible solution that delivers transparent, adaptable, and locally actionable urban insights beyond the capabilities of proprietary, black-box alternatives.

Current Status

The implementation of the de-risking phase of the activity is from January to June 2026.

EO-CARE

Objectives of the Product

Municipalities and businesses across Europe face growing climate risks — floods, wildfires, droughts, heatwaves, cold snaps — but lack the tools to act. Current systems rely on outdated, fragmented data that cannot predict compound hazards or justify adaptation investments. Decision-makers struggle to translate satellite observations into actionable strategies, leaving communities and supply chains vulnerable.

EO-CARE solves this by combining near-real-time Earth Observation data from Copernicus satellites with AI-driven climate models to deliver hyperlocal risk assessments at 100-meter resolution. The platform enables municipalities to map heat islands, monitor coastal erosion, and simulate the cost-effectiveness of green infrastructure. Businesses can assess supply chain vulnerabilities and comply with climate disclosure frameworks like TCFD.

Unlike generic tools, EO-CARE integrates historical hazard registries with IPCC climate scenarios, predicting cascading events — such as drought-fueled wildfires triggering flash floods. Aligned with Portugal’s National Adaptation Roadmap (RNA 2100) and EU interoperability standards (ENTI), it provides transparent, evidence-based planning for resilient cities and climate-proof operations. EO-CARE transforms satellite data into decisions, bridging the gap between observation and action.

Customers and their Needs

EO-CARE targets two primary customer segments: municipalities (local and regional governments) and businesses (SMEs and enterprises in sectors like agriculture, logistics, tourism, and infrastructure).

Municipalities need to protect citizens and infrastructure from escalating climate hazards but face fragmented data, limited technical capacity, and difficulty justifying adaptation budgets. They require tools to map vulnerabilities (e.g., urban heat islands, flood-prone areas), prioritise interventions, monitor implementation, and demonstrate compliance with national policies like Portugal’s RNA 2100 and EU frameworks.

Businesses struggle to assess physical climate risks to assets and supply chains, hindering compliance with financial disclosure requirements (TCFD, EU Taxonomy). They need transparent, data-driven insights to quantify disruption risks — such as crop failures, transport delays, or facility damage — and evaluate return-on-investment for resilience measures.

Both groups are involved through interviews, surveys, and workshops during the de-risking phase to validate requirements and co-design the platform. Municipalities, regional authorities, private companies and academia have supported, confirming demand for a unified, interoperable solution that translates Earth Observation data into cost-effective, evidence-based climate action.

Targeted customer/users countries

Primary market: Portugal

Secondary markets (near-term expansion): Spain, France, Italy, Greece, and other Southern European countries facing similar climate risks (droughts, wildfires, coastal flooding, heatwaves).

Long-term vision: Pan-European deployment, with scalability to other ESA Member States and climate-vulnerable regions globally.

The de-risking phase focuses on Portuguese stakeholders (municipalities in Azores, Madeira, and mainland cities; regional authorities; SMEs; academic institutions), with validation pilots planned in these regions. The platform’s design — built on Copernicus data, IPCC/CORDEX climate models, and EU interoperability standards (ENTI) — ensures adaptability to other countries with minimal customisation.

Product description

EO-CARE is a cloud-based decision-support platform that integrates Earth Observation data with AI-driven climate modeling to deliver hyperlocal (100m resolution) risk assessments and adaptation planning tools.

Core capabilities:

Multi-hazard risk mapping: Real-time and scenario-based analysis of floods, droughts, wildfires, heatwaves, and coastal erosion using Copernicus services (Atmosphere, Climate Change, Land, Marine, Emergency) and Sentinel-1/-2/-3 satellites.

Compound risk prediction: Federated AI/ML models forecast cascading events (e.g., drought → wildfire → flood), validated against historical disaster data.

Cost-benefit analysis: Simulates ROI of adaptation measures (green infrastructure, early warning systems) using IPCC AR6 and CORDEX climate scenarios.

TCFD-compliant reporting: Assesses supply chain vulnerabilities and generates climate disclosure reports for businesses.

Monitoring dashboard: Tracks implementation of adaptation actions aligned with Portugal’s RNA 2100 and EU taxonomy.

Innovation: Unlike static tools, EO-CARE fuses near-real-time EO data with physics-informed AI, achieving 10x finer resolution than existing platforms. ENTI-compliant APIs enable seamless integration with municipal GIS and financial systems.
User interaction: Role-based web dashboards (municipalities view risk maps and investment scenarios; businesses access supply chain analytics).

Added Value

EO-CARE delivers distinctive value over existing solutions by fusing Earth Observation (EO) and AI into a unified, interoperable platform:

Near-Real-Time & Hyperlocal: Unlike static tools with coarse resolution (10–100km), EO-CARE integrates live Copernicus data and downscaled climate models to deliver dynamic risk assessments at 100-meter resolution.
Compound Risk AI: While competitors track single hazards, EO-CARE’s federated AI predicts cascading events (drought → wildfire → flood), validated against historical disasters like Pedrógão Grande.
Cross-Sectoral: Dual dashboards serve both municipalities (RNA 2100 adaptation planning) and businesses (TCFD supply chain analysis), bridging the public-private silo gap.
Interoperable & ENTI-Compliant: Pre-built APIs ensure seamless integration with municipal GIS and financial systems, supporting Portugal’s smart-city standards (ENTI) and GDPR-compliant local data processing. 5. Modular Scalability: Validated in diverse regions (Azores, Madeira, and Pilot Cities), its modular architecture ensures scientific robustness and adaptability to other Mediterranean and European contexts, unlike rigid global frameworks.

Current Status

The EO-CARE activity has successfully completed the proposal phase and secured a contract with ESA under the InCubed-2 programme.

The project is currently at the Kick-off stage, launching the De-risking Cycle (Phase 1). The Consortium — led by Get2C with Tech2C and Phair-Earth — is initiating the Feasibility Study to validate the system architecture, scientific models, and user requirements.

Work in progress:

Requirement consolidation with key stakeholders (municipalities and businesses).

Definition of the preliminary system concept and architecture (WP3100).

Technical and scientific assessment of climate and AI models (WP2300).

Upcoming activities:

Market and economic analysis (WP2100).

Resources and needs mapping (WP4100).

Preparation of the De-risking Review (DRR) to confirm viability before proceeding to the full Product Development Cycle.

BridGEO

Objectives of the Product

BridGEO addresses a common challenge faced by public authorities, planners, and institutions in emerging and developing countries: critical environmental and urban decisions rely on complex satellite data that are fragmented, difficult to interpret, and often inaccessible to non-expert users. As a result, water management, urban growth control, and risk mitigation are frequently based on incomplete or disconnected information.

BridGEO provides an online, WebGIS-based platform that integrates satellite and local geodata into a unified, user-friendly system tailored to each country’s context. Its modular EO-based analytical services allow users to select affordable, fit-for-purpose solutions covering water body dynamics, imperviousness and urban sprawl, and ground motion or slope stability. A map-centric interface, cross-service analysis tools, and contextual interpretation layers transform raw data into clear, actionable insights without requiring advanced technical expertise.

To ensure long-term impact, BridGEO complements its tools with targeted, hands-on training for non-technical and technical staff alike. This builds local capacity to operate the platform independently, interpret results confidently, and apply evidence-based insights to urban planning, water management, and environmental governance, supporting sustainable and locally driven decision-making.

Customers and their Needs

BridGEO targets national and local public authorities, research institutes, and regional planning bodies in emerging and developing countries, such as those in Ethiopia, Peru, and Chile. These users are directly involved in territorial monitoring, disaster risk reduction, water resource management, and urban planning. They use the BridGEO web-based platform to routinely monitor ground movements, surface water dynamics, and land consumption, supporting early warning systems, risk mapping, and evidence-based planning decisions.

Their needs include accurate and frequent detection of landslides, the ability to analyse ground deformation trends over time, regular monitoring of reservoir volume and extent, and reliable identification of urban expansion and loss of agricultural land. They also require improved skills in GIS and satellite data analysis to integrate EO information with socio-economic and administrative data.

Meeting these needs is challenging due to limited financial resources, fragmented and inconsistent historical data, insufficient data resolution or update frequency, and a shortage of trained personnel. Traditional field surveys are costly and slow, while many EO solutions are complex and require expert knowledge. BridGEO addresses these challenges by providing affordable, integrated services and hands-on training that enable institutions to independently apply EO data in their daily operations.

Targeted customer/users countries

Targeted countries are Ethiopia, Peru, and Chile.

Product description

BridGEO is an online, WebGIS-based platform that delivers integrated Earth Observation services for territorial monitoring, risk management, and environmental governance. The system combines three core analytical services within a unified environment. The Water Bodies Dynamics service provides monthly monitoring of reservoirs in user-defined areas, delivering trend analysis and change detection at 10 m spatial resolution and a minimum mapping unit of 0.5 ha. The Imperviousness Dynamics service classifies and monitors land cover using an adapted EAGLE Level 2 scheme, generating six-monthly updates and automated land-change maps at the same spatial resolution. The Ground Motion service delivers trends in ground elevation change over a three-year window, updated every six months, with outputs available at native resolution and resampled grids of 50 m or greater.

The main innovation lies in the integration of these services into a single, map-centric web interface. Users interact through intuitive dashboards, predefined thematic views, and automated cross-service analyses that reveal correlations between urban growth, water dynamics, and ground instability. A contextual interpretation layer translates technical results into plain-language insights. BridGEO is complemented by modular, hands-on training—delivered on-site or remotely via an AI-supported LMS—enabling users to independently analyse data and apply results in real-world decision-making.

Added Value

BridGEO delivers significant added value compared to existing EO-based monitoring methods by combining higher-resolution data, more frequent updates, and integrated analysis within a single operational platform. For water bodies monitoring, BridGEO goes beyond commonly used approaches—such as reliance on a single index like NDWI—by generating inland surface water detection maps based on multiple multispectral indices. This improves detection accuracy and robustness across different environmental and seasonal conditions. The service also provides water occurrence and frequency maps, distinguishing maximum, seasonal, and permanent water bodies, as well as inter-annual and seasonal variation maps that support informed management of agricultural activities during dry and wet periods.

Compared to existing national and international services, BridGEO offers capabilities that are currently unavailable or limited in many emerging countries. High-resolution imperviousness maps enable detailed urban analysis and detection of subtle land cover changes, addressing a major gap in countries such as Ethiopia. Relative to Copernicus services, BridGEO delivers higher spatial resolution, more frequent updates, and a tighter integration between imperviousness analysis and next-generation IRIDE data. By integrating these advanced datasets into a unified, user-friendly system, BridGEO provides more actionable, locally relevant insights than standalone or medium-resolution EO services.

Current Status

The project has made significant progress in assessing current conditions, identifying vulnerabilities, and spotting opportunities, establishing a solid foundation for prioritising services. Requirements definition is underway, with user needs, system specifications, and service expectations being gathered, analysed, and documented to ensure clarity of functional and non-functional requirements.

Technical development has commenced, focusing on the design, construction, and testing of EO services, the delivery platform, and associated training courses. Work in progress includes refining technical solutions, validating system functionalities, and preparing training materials. Upcoming activities will concentrate on finalising requirements, completing system testing, and rolling out training programmes. Overall, the project is advancing steadily, with foundational analysis and technical development progressing in parallel to ensure services are well-aligned with user needs and operational priorities.

EARS-GNSS RO

Objectives of the Product

GNSS Radio Occultation (GNSS RO) is a space-based remote sensing technique for studying the Earth’s atmosphere. It uses the atmosphere’s refractive properties, which bend radio signals from GNSS satellites as they reach a low Earth Orbit (LEO) receiver. This bending causes an additional Doppler shift, which, when compared to the expected shift in vacuum, allows the atmospheric contribution to be used to derive the bending angle.

The bending angle (along with impact parameter) is a key radio occultation observable, forming the basis for retrieving vertical profiles of atmospheric refractivity, temperature, pressure, and humidity. GNSS RO provides high vertical resolution, global coverage, and long-term stability, making it valuable for numerical weather prediction (NWP). Minimising latency between measurement and data assimilation is crucial for applications like nowcasting.

The Earth Atmospheric Remote Sensing via GNSS RO (EARS-GNSS RO) mission is developing a LEO satellite capable of determining key parameters of Earth’s atmosphere with high vertical resolution and low latency using the GNSS RO technique. Key aspects of the mission are:

On-board processing: The mission uses a high-performance System-on-Chip (SoC) and an advanced Precise Orbit Determination (POD) algorithm, enabling on-board data processing.
Latency Reduction: Thanks to onboard data processing and a network of low data-rate ground stations, the latency of transmitted information is reduced.

Customers and their Needs

The primary users of GNSS RO data from the EARS mission include:

Numerical Weather Prediction (NWP) centres
Meteorological agencies
Climate monitoring institutions
Commercial organisations relying on weather and atmospheric intelligence

These users engage with the activity through data assimilation, quality control feedback, and validation campaigns, integrating the data into forecasting, nowcasting, and climate monitoring workflows.

Users need timely, accurate atmospheric profiles with precise geolocation and low latency, along with reliable coverage and products compatible with existing operational and commercial systems.

Challenges include maintaining low latency and high quality, as well as managing the extra power required for on-board processing. In addition, the implementation of on-board precise orbit determination (POD) to compute real-time satellite positions with sufficient accuracy to ensure the quality of the calculated bending angles and atmospheric profiles poses further challenges.

Targeted customer/users countries

The targeted customers/users are both commercial and scientific, spanning Europe, North America, and Asia.

Product description

The primary EARS product is low-latency, high-quality atmospheric data. The system is composed of the following main building blocks:

EARS Platform, hosting the RO instrument and a secondary payload, providing the necessary spacecraft resources and interfaces.
RO Instrument, including a GNSS receiver, POD module, and dedicated antennas for GNSS RO measurements.
Ground Segment, responsible for telemetry, tracking and command (TT&C) operations and low-latency data downlink and distribution.
Cloud Services, enabling scalable and user-friendly access to RO data products for operational and commercial users.

A block diagram of the complete end-to-end system, including the GNSS RO measurement context described in the product objectives, is shown below:

The EARS system combines advanced algorithms with optimised on-board hardware to enable Near Real-Time (NRT) processing of measured data directly on orbit. This innovative approach optimises downlink usage and significantly reduces data latency, allowing faster delivery of user-ready atmospheric products. Users interact with the product through cloud-based services, integrating the data directly into forecasting, nowcasting, and atmospheric analysis workflows.

Added Value

The proposed solution delivers added value compared to existing GNSS RO services through innovations in processing, platform architecture, and European commercial sourcing.

Onboard near-real-time processing sets this solution apart from current missions relying on ground-based processing. By performing signal acquisition, processing, and atmospheric profile derivation on board a small satellite, data latency is reduced from hours to under 30 minutes. This enables operational use cases such as nowcasting, rapid-response meteorology, and aviation weather services.

Compact, scalable platform architecture based on a high-performance Software Defined Radio (SDR) with integrated on-board POD and processing allows deployment on small platforms, e.g., 8U-12U CubeSats. Distributed constellations can provide higher spatial and temporal coverage at lower cost.

European-origin commercial data source – the system enhances strategic autonomy, offering a resilient alternative to non-European providers and supporting institutional, regional, and potential dual-use applications.

Current Status

The EARS project is currently in the de-risking phase, focusing on a detailed analysis of the market and customer requirements and the mission, payloads and system-level requirements. The subsequent activities focus on the definition of preliminary requirements and the architecture for the mission, payload, platform, and ground segment.

AI Visual Copilot

Objectives of the Product

Organisations across sectors such as agriculture, forestry, infrastructure, and insurance face major barriers in applying Earth observation (EO) data. Creating tailored AI models for tasks like object detection or segmentation requires costly manual labelling, deep technical expertise, and months of development time. As a result, many EO projects are delayed, scaled back, or abandoned.

FlyPix AI addresses these challenges with the AI Visual Copilot, a no-code SaaS solution that streamlines EO model creation. By combining one-shot learning with AI-assisted annotation, the Visual Copilot reduces labelling efforts by up to 95% and cuts model development time from months to days. Users without AI expertise can interactively define models, leverage smart labelling suggestions, and deploy fit-for-purpose solutions at scale.

The Visual Copilot integrates seamlessly into the FlyPix AI platform, supporting diverse EO data sources from satellites, drones, and aerial imagery. Its modular design ensures scalability, adaptability, and rapid prototyping for a wide range of applications, from land use monitoring and biodiversity protection to damage assessment and infrastructure tracking.

This solution empowers customers to transform raw EO data into actionable insights faster, cheaper, and with greater flexibility, unlocking new opportunities for data-driven decision-making across industries and public authorities.

Customers and their Needs

The targeted customers include geospatial and geographic information systems (GIS) service providers, aerial inspection firms, insurers, public authorities, and environmental agencies. These organisations rely on EO data to monitor assets, assess risks, track environmental change, and support critical decisions. However, they face significant challenges in leveraging EO data effectively.

Key needs include:

Faster model creation: Current workflows require extensive manual labelling and weeks of preparation, delaying projects.
Lower costs: Tailored AI models demand high budgets, often consuming most of a project’s resources.
Ease of use: Many customers lack in-house AI expertise, making advanced EO analytics inaccessible.
Scalability: Models are difficult to adapt across datasets, regions, or new use cases.
Confidence in ROI: High upfront investment and uncertain outcomes discourage adoption, especially among SMEs and public bodies.

Through the activity, pilot customers such as insurers, GIS firms, and aerial inspection providers will directly test the Visual Copilot, providing feedback on workflows and usability. By enabling no-code, AI-assisted model creation that reduces costs and time by up to 95%

Targeted customer/users countries

Based on the proposal, the targeted customer/users’ countries are Germany, Austria, Netherlands, and the rest of EU countries; United Kingdom, USA, Japan and India.

In addition, the product is positioned for broader uptake across Europe, with future expansion to North America and Asia-Pacific.

Product description

FlyPix AI Visual Copilot is a no-code, cloud-based solution that accelerates the creation of AI models for EO data. It enables users to perform object detection, segmentation, and tracking on satellite, aerial, and drone imagery with minimal technical expertise. Customers interact through an intuitive interface where they can define tasks visually, apply AI-assisted labelling, and deploy models within hours rather than months.

Innovation lies in the one-shot learning approach, enabling model creation from a single example, combined with automated labelling and modular scalability. This transforms EO analytics into a fast, affordable, and user-friendly process, unlocking new opportunities for industries such as agriculture, forestry, insurance, and infrastructure monitoring.

Added Value

FlyPix AI Visual Copilot delivers a step-change in how organisations build AI models for Earth observation by eliminating the biggest barriers—cost, time, and lack of expertise. Competing platforms require large, annotated datasets, weeks of preparation, and advanced technical knowledge, making EO-driven insights inaccessible to many organisations.

In contrast, FlyPix AI Visual Copilot allows users to build accurate detection or segmentation models from just a single example. Combined with AI-assisted annotation, it reduces labelling efforts by up to 95% and lowers project costs by as much as 70%. This makes EO analytics affordable for small enterprises, local authorities and NGOs, in addition to large commercial players.

Unlike competitors focused on narrow use cases, Visual Copilot offers broad versatility—supporting tasks from agriculture monitoring and forestry management to insurance damage assessment and infrastructure tracking. Its no-code interface empowers non-experts to create, validate, and deploy models quickly, accelerating decision-making in critical areas such as climate resilience, urban planning, and disaster response. The added value lies in democratising EO model creation: transforming months of expert-driven work into a simple, scalable, and cost-effective workflow that unlocks new opportunities for innovation and impact across industries.

Current Status

AI Visual Copilot activity builds on a working prototype already integrated into the FlyPix AI platform. Initial proof-of-concept projects have been successfully delivered for customers in insurance, forestry, and infrastructure, validating both the technical approach and market demand. Letters of Interest (LoIs) from pilot customers confirm strong commitment to adopt the solution once available.

Currently, the team is refining the prototype into a scalable SaaS module, focusing on one-shot learning, AI-assisted labelling, and seamless EO data integration. Pilot users are engaged in requirements gathering and early validation to ensure usability and alignment with operational needs. The upcoming phase will expand testing with multiple pilot customers, finalise integration with EO data providers, and prepare the platform for commercial rollout. With InCubed’s support, the activity is on track to deliver a validated, market-ready product by early 2026.

ENGINE

Objectives of the Product

The main problem in the Earth observation (EO) market is that the technology used is too complex and requires specialised knowledge for most users to interpret satellite data and integrate it into their operations, hindering wider adoption.

The solution is the ENGINE project, an AI-powered agent integrated into the SpaceKnow Guardian® platform. Its objective is to democratise geospatial intelligence, initially focusing on urban development and construction monitoring.

ENGINE achieves this by:

Allowing users to control the analysis platform and receive results using simple natural language.
Eliminating the need for remote sensing expertise.
Combining imagery intelligence with contextual data from open-source intelligence (OSINT) to deliver actionable, all-in-one insights.

This integration streamlines the entire analysis process, making powerful, scalable EO insights accessible to a much broader range of businesses and organisations.

Customers and their Needs

The targeted primary customers for the activity are from the urban planning and construction monitoring segments. Other key segments include national security entities, NGOs, and the oil and gas industry. The long-term vision is to expand to virtually any organisation that can benefit from scalable satellite imagery analysis.

Customers face challenges due to the complexity of integrating Earth observation (EO) solutions and the need for specialised, technical expertise to interpret geospatial data. This creates a steep learning curve and limits adoption. Users interact with the product, ENGINE, an LLM-powered autonomous agent, by using natural language (written or spoken) to control the system and request analyses. This involvement allows them to autonomously monitor projects and receive actionable insights and contextual information without needing expertise in remote sensing.

Targeted customer/users countries

Europe, US, KSA.

Product description

The product is ENGINE (SpaceKnow Guardian® Operator), an LLM-powered autonomous agent integrated into SpaceKnow’s cloud-based platform, SpaceKnow Guardian®.

Capabilities and Innovation:

ENGINE’s core capability is democratising geospatial intelligence. It transforms the analysis of satellite images (IMINT) by combining it with contextual data from open-source intelligence (OSINT). The innovation lies in the LLM-powered agent, which simplifies the entire geospatial analysis workflow, dramatically reducing the technical knowledge required from the user. It also employs proprietary algorithms to process challenging low-resolution SAR images.

User Interaction and System Outline:

The user interacts with the product using natural language (written or spoken inputs). The system processes the request and delivers results as automated responses in an LLM-processed format (written, spoken, or visual).

Added Value

The proposed solution, ENGINE, brings added value and a competitive edge through several key innovations:

LLM-Powered User Assistance: The main innovation is an LLM-powered agent that assists the user, reducing the entry barrier for geospatial analysis. It suggests the most suitable approach and simplifies the workflow, a significant advancement over current offerings.
Data Fusion: It achieves optimal, customised results by merging Earth observation (EO) data with other sources like open-source intelligence (OSINT), a capability uncommon in the industry.
Superior Processing: Proprietary algorithms are even able to process challenging low-resolution Synthetic Aperture Radar (SAR) images, addressing data quality difficulties that may limit competitors.
Streamlined Service: By employing AI agents for both user input and output interpretation to add contextual knowledge, ENGINE is not just an improved interface but an enhanced overall service, making it highly competitive against rivals like Preligens or Orbital Insights.

Current Status

The activity kicked off it the beginning of Q4 2025. Currently, the first work packages are running, focused on State-of-the-Art Review, Foundation Model and Agency Framework exploration, and External Data Research. These work packages will be reviewed in the first milestone. The Requirements’ Review is planned for 11 December 2025.

VISIONS

Objectives of the Product

The market’s insatiable demand for more data, at higher spatial and temporal resolutions, is driven by the need to gain deeper insight into the Earth’s environment and respond to the observed situations in a timely manner. In response to this, the product developed under VISIONS offers substantial performance improvements over existing products in the SSTL portfolio, providing higher resolution data, as well as significantly greater data return, and is capable of rapid tasking and product delivery. The problem to be addressed is providing the Earth Observation (EO) market with cost-effective sub-0.5 m resolution imagery, provided in a way that is timely, trusted, actionable and at a reasonable cost. The multispectral payload developed in a previous activity enables customers to benefit from actionable data and insights at very high resolution to enhance their services and business operations in a wide range of vertical markets including agriculture, disaster and resource management, defence and security.

Customers and their Needs

For Commercial EO companies, customers need cost-optimised, high-performance imaging solutions in Low Earth Orbit (LEO) to maximise profitability of their business, shorten lead times to accelerate their time to market and revenue generation, and then to maximise the utilisation of their mission in a variety of markets and applications. Commercial customers also need an ability to scale operations rapidly in response to greater market demand, driving developments toward repeat builds, less bespoke solutions, and modularity in small satellite space systems.

Government customers (Civil and Defence) also express these commercial needs but with added pain points related to sovereignty and EO data independence, filling critical EO data gaps, capacity building and knowledge exchange, highly secure architectures with strict security and data traceability requirements, and finally value-for-money and ensuring the best use of public spending to benefit society and the economy as a whole.

Targeted customer/users countries

As a small satellite and payload manufacturing company, SSTL customers target satellite operators, across Commercial EO companies and Governments. Key target market areas include UK, Europe, Middle East and South-East Asia (EMESEA) .

Product description

The VISIONS activity directly supports the delivery of SSTL’s Precision product; a 450kg, mini class satellite, capable of being compatible with affordable rideshare launchers, and capturing very-high resolution imagery from a 500-km orbit. The Precision imager capitalises on half-pixel shifted pixels and processing in the PAN band to provide superior resolution and image quality (particularly benefiting from reduced aliasing). The half-pixel shifted pixels in the PAN band improve the spatial sampling from the native PAN GSD of 0.6m to 0.3m. In addition, the Imager can also simultaneously capture four 1.2m-GSD multispectral channels from a choice of six available (Coastal blue, B, G, R, Red Edge & NIR). The product benefits from an upgraded downlink chain in X-Band capable of an Over the Air (OTA) data rate of 900Mbps per transmitter. The modular payload and downlink chain that sits on the spacecraft platform comprises upgraded avionics modules that are designed for constellation production to meet market demands.

The product is designed for a wide range of LEO spacecraft EO operators who can seamlessly integrate into their ground segment’s infrastructures and data service to their users and downstream value chain.

Added Value

Customers will gain combined added value over other small EO satellites on the market from the Precision product including:

Increased information and intelligence value per square kilometre, through ‘best in class’ High EO Imaging Performance
Reduced operational costs through increased downlink rate
Reduced costs through compact design and ability to access affordable rideshare launch options
Enhanced customer use and business cases through agility, tasking and data latency
Reduced non-revenue generating data through access to on-board processing capabilities
Rapid delivery, modular approach, and ready for production for constellations.

Current Status

The activity had its kick-off meeting on 23 October 2025.

Emissions Watch

Objectives of the Product

The UK government has implemented several policies and strategies to reduce methane emissions and achieve net-zero by 2050. However, without good data underpinning decision-making and action, the UK will fail to meet these targets. This solution will bridge the gap between existing satellite-based measurement data and operationally relevant, actionable information.

Greenhouse gas emissions linked to UK consumption are rising, driven largely by imported goods. Due to its high global warming potential, methane is a priority for action, as the potent greenhouse gas contributed around 16% of the UK’s territorial emissions in 2023. Agriculture, waste, and energy sectors accounted for 49%, 30%, and 8% of these emissions respectively. Methane also makes up an estimated 15% of emissions embedded in imports, largely from oil and natural gas—53 billion cubic meters imported in 2022 alone.

Over half of methane emissions are “fugitive,” meaning their sources and timing are unpredictable and so difficult to estimate or detect without frequent monitoring. Without robust data on methane sources, the UK risks missing its net-zero targets. Satellites offer a cost-effective, consistent way to for regular monitoring of methane emissions.

Emissions Watch delivers detailed mapping and monitoring of industrial greenhouse gas emissions using advanced satellite analytics and contextual facility data. It provides timely, actionable insights to support compliance, inform policy, and drive sustainable outcomes.

Customers and their Needs

Government agencies are the target market for this service—both the United Kingdom and wider Europe.

In the United Kingdom, there are four specific needs:

Replacing modelled estimates of methane emissions in the National Atmospheric Emissions Inventory (NAEI) with empirical, measured data
Tracking emissions from imports against international commitments, assessing compliance and informing future import standards
Supporting the implementation of methane reduction schemes and incentives, such as the expansion of the UK emissions trading scheme and carbon border adjustment mechanism, through independent Monitoring, Reporting, and Verification
Enforcing existing and future environmental and health and safety regulations, such as Leak Detection and Repair programs or emissions threshold penalties

Targeted customer/users countries

The service will be primarily targeted towards the UK before being out to Europe and broader global markets.

Regulatory agencies include the UK Environment Agency, Scottish Environmental Protection Agency, Natural Resources Wales, North Sea Transition Authority, and the Office of Gas and Electricity Markets.

Policy bodies include Defra and DESNZ, as well as similar agencies across European governments.

Product description

Emissions Watch will provide a comprehensive view of methane emissions tied to specific facilities, allowing the tracking of emissions at the site level—Asset-Level Analytics—and portfolio level by type, sector, ownership, or geography for Executive Reporting.

The service will initially focus on methane from onshore oil and gas and waste facilities using artificial intelligence (AI) and satellite data to identify and map facilities.

By combining information from a range of public and private data sources, it will then connect these mapped locations with important details such as the type of facility and ownership information, creating a “Facility database”.

Finally, Emissions Watch will draw on remotely sensed satellite short-wave infrared data (GHGSat and Sentinel-2) to both detect and measure emissions and then link those measurements directly to the corresponding industrial facilities via machine learning.

Added Value

The key areas of innovation brought by Emissions Watch include:

Greater accountability for emissions variability and unexpected sources. Augmenting existing estimates of methane emissions modelled by customers with observed empirical data (from satellite) will improve the overall accuracy of emissions recorded. This is done by addressing inherent issues of error and uncertainty caused by the dependency on generalised emissions factors and activity data used in modelling. Modelling approaches fail to capture super-emitter events or operational anomalies that contribute disproportionately to total emissions (up to 60% according to different sources). Furthermore, independent mapping of methane emitting facilities (using satellite and AI) will ensure full transparency and accounting for all emitting facilities, minimising non detection or underestimation that results from fugitive emissions which are typically an important part of the total emissions profile.
Linking emissions activity to the accountable party. Providing origin certainty and linking probability of ownership at the source of emissions will allow relating of observation data through to relevant regulatory or licensing frameworks to support decision making and action.
Comprehensive global reference for observed emissions data. A complete emissions dataset (enabled by combining GHGSat with public sector data) uniquely supports analytics and reporting that allows an individual facility to be put into the context of global trends (By facility type, sector, ownership or geography). This allows for performance measurement to support applications such as tracking reduction targets or benchmarking against competitors.

Current Status

The activity held its Kick-Off meeting on the 4 September 2025.