Archives: Activities Portfolio

CrossBandInsights

Objectives of the Product

CrossBandInsights delivers an advanced InSAR monitoring solution that combines data from C-band and X-band satellite missions to overcome limitations of traditional single-band methods. By integrating C-band with X-band’s high-resolution – able to detect sensitive measurements – the system provides enhanced spatial and temporal data diversity. This fusion enables more comprehensive monitoring of ground deformation across varied land cover types and complex environments, addressing challenges faced by civil engineering companies and public authorities.

The technology is designed for flexibility, capable of merging data from any satellite band to ensure future adaptability. It offers highly accurate deformation measurements, allowing precise detection of ground shifts and assessment of structural integrity. This empowers clients to make informed decisions for infrastructure maintenance and risk management. Overall, the solution supports the safety, resilience, and sustainability of critical infrastructure by delivering robust monitoring and actionable data to end-users.

Customers and their Needs

CrossBandInsights targets civil engineering companies and public administrations involved in infrastructure projects, who need to ensure safety across all project phases.

Civil engineering companies face challenges in assessing infrastructure stability and detecting early deformations. Public administrations require comprehensive monitoring for geohazards and urban planning. Current InSAR methods often have limited data sources and inadequate temporal or spatial coverage due to the use of single satellite missions. This makes comprehensive assessment difficult for end users, sometimes leading to delayed responses and increased risks.

CrossBandInsights resolves these issues by combining C-band and X-band satellite SAR data with a new processing algorithm. This fusion provides more accurate and comprehensive deformation measurements, including Line-of-sight (LOS), vertical, and East-West components. The solution is user-friendly, customisable, and affordable, and it enhances the safety, resilience, and sustainability of infrastructure projects.

Targeted customer/users countries

Spain

Product description

The CrossBandInsights solution is an advanced InSAR monitoring product that enhances ground deformation analysis by integrating multi-mission C-band and X-band SAR data. It provides precise deformation measurements in Line-of-Sight (LOS), Vertical, and East-West components. This innovative approach overcomes the limitations of single-mission InSAR by merging data from various satellite sources, significantly improving both temporal and spatial resolution, especially in a mix of different land coverage.

Our new algorithm performs joint processing of these diverse datasets, leading to more consistent time series, improved Atmospheric Phase Screen (APS) estimation, and higher data quality and density, especially in critical areas. Customers, primarily civil engineering companies and public authorities, will interact with CrossBandInsights through our user-friendly, web-based TREmaps platform and standard GIS tools. This platform will visualise the enhanced 1D/2D deformation data, facilitating timely risk identification, proactive maintenance decisions, and comprehensive infrastructure stability assessments.

The innovation lies in this multi-sensor data fusion at the processing level, offering an unparalleled understanding of ground dynamics for urban environments and critical infrastructure.

The product architecture involves several key building blocks:

  • PS-SHELL Processing: Initial interferometric processing for each geometry, generating 3D unwrapped phase time series and error bars.
  • Joint APS Estimation & Merging Algorithm: A core innovative component that performs a harmonised processing and filtering of atmospheric contributions across multiple geometries.
  • 2D Decomposition: Derivation of precise Vertical and East-West deformation components.
Added Value

CrossBandInsights brings added value by overcoming the limitations of current InSAR solutions. While other InSAR solutions only rely on single-satellite missions with limited data, coverage, and assessment capabilities, CrossBandInsights integrates C-band and X-band satellite SAR data. This fusion, combined with a state-of-the-art processing algorithm, will significantly enhance both temporal and spatial resolution, particularly in areas with a mix of diverse land coverages.

Specifically, the solution’s key innovations include multi-mission data integration, enhancing resolution by combining the strengths of C-band (wider coverage) and X-band (denser measurement points, higher sensitivity). This enables a more

comprehensive view of ground deformation, including Line-of-Sight (LOS), vertical, and East-West components. Furthermore, CrossBandInsights is explicitly tailored for civil engineering and infrastructure monitoring, providing precise and relevant insights for this sector. TRE ALTAMIRA’s patented SqueeSAR® processing chain further strengthens this unique advantage. The solution is also designed to be user-friendly, customisable, and affordable, utilising readily available satellite data.

Current Status

The project is currently at the end of the second month. The main activities are focused on the production of the Requirement Review (RR) deliverables. Specifically, efforts include:

  • Consolidation of User Requirements, through internal assessment based on existing client feedback from civil engineering and public administration contracts.
  • Consolidation of System Requirements, by refining them to ensure alignment with User Needs and User Requirements.
  • Identification of key components for Business Plan preparation.
  • Refinement of the risk register.

The next set of activities, following ESA’s approval of the RR documents, will focus on designing the workflow required for generating the target product.

EO4Biodiversity

Objectives of the Product

Biodiversity plays a vital role in supporting food security, clean water, medicine, and resilience to climate change. The UK and other governments are committed to reducing the negative impacts of development on biodiversity, while also enhancing habitats to support its recovery.

In the UK, all investments are required to deliver at least a 10% net gain in biodiversity. As a result, developers must demonstrate that they have assessed the biodiversity value of existing or improved habitats using Standardised Biodiversity Units (SBUs). However, calculating these impacts—especially for large-scale projects like new water resource schemes—can be time-consuming and costly.

Earth observation (EO) data offers a way to streamline parts of this process by automating biodiversity impact assessments across landscapes. EO4Biodiversity supports the creation of a tool that enables public bodies, businesses, and infrastructure developers to carry out automated evaluations of how different development options affect biodiversity. The tool identifies habitat types based on UK government classification standards and calculates biodiversity values using SBUs, helping to simplify and accelerate the assessment process.

Customers and their Needs

The legislation on Biodiversity Net Gain (BNG) applies to all new developments—including energy, transport, water, and housing—making the EO4Biodiversity tool relevant to a wide range of potential users.

Initial buy-in and a strong commitment to co-development and early adoption have already been secured with Water Resources South East (WRSE), one of the five regional water resources groups. WRSE brings together six water companies and a broad coalition of stakeholders, including the Environment Agency, Natural England, Ofwat, local planning authorities, and environmental organisations.

In its most recent regional plan, WRSE assessed over 4,000 scheme options. Each option may involve multiple assets spread across large areas, with significant construction impacts. Manually calculating SBUs for each option is estimated to cost around €1.6 million—and this process must be repeated every five years.

An automated approach is therefore essential. EO4Biodiversity addresses this need by enabling efficient, repeatable biodiversity assessments at scale, helping organisations meet regulatory requirements while saving time and resources.

Targeted customer/users countries

EO4Biodiversity supports strategic biodiversity net gain (BNG) requirements set by the UK government, as well as similar policies under the EU’s Nature Restoration Law. The project addresses both societal and productivity impacts:

  • Societal impacts: contributing to the protection and enhancement of biodiversity across landscapes.
  • Productivity impacts: improving the efficiency of regulated BNG assessments across multiple sectors, including energy, transport, water, and housing.

By aligning with these legislative frameworks, EO4Biodiversity ensures relevance and value for a broad range of stakeholders working to meet biodiversity goals efficiently and effectively.

Product description

EO4Biodiversity automates the identification of habitat types using formal definitions from UK government biodiversity evaluation processes. It also streamlines the calculation of SBUs, reducing the time and cost associated with manual assessments.

The service processes freely available land cover datasets and integrates optimisation tools to support the review of development options. Earth observation (EO) data is enhanced with existing datasets from public entities and refined through machine learning techniques.

These elements—EO data, ground truth information, and machine learning—are combined in a web-based application called EO4Biodiversity. This tool is designed for use by a wide range of public sector organisations and commercial developers.

Assessing the biodiversity impacts of new infrastructure projects, such as water resources or energy schemes, is now a statutory requirement. Currently, these assessments are carried out without the benefit of EO data. EO4Biodiversity introduces a new approach by post-processing publicly available EO land cover datasets to meet the specific needs of biodiversity impact analysis and BNG compliance.

Source: HR Wallingford
Added Value

No other solutions currently available in the market offer the same level of functionality as EO4Biodiversity. The project was initiated in response to a direct request from Water Resources South East (WRSE), a consortium of six water companies, who sought a more efficient method for assessing biodiversity impacts using Earth Observation (EO) data.

WRSE currently relies on a series of manual steps that are both time-consuming and costly. By automating key parts of the process, EO4Biodiversity addresses a clear gap in the market, offering a scalable, data-driven solution that meets the growing demand for efficient biodiversity net gain assessments.

Current Status

Activity WP2000 covers the identification of requirements and the development of the system architecture for the EO4Biodiversity tool. This includes a detailed exploration of statutory requirements for biodiversity metric calculations, a review of existing tools and UK government guidance, and an assessment of current habitat-type databases to evaluate their suitability for integration.

These foundational tasks ensure that the tool is built on a robust understanding of regulatory needs and existing resources, setting the stage for a solution that is both compliant and practical for end users.

IVSEN

Objectives of the Product

The Integrated VHR Satellite for Energy Networks (IVSEN) project addresses the critical needs of energy operators and infrastructure managers for timely, reliable, and cost-effective Earth Observation (EO) data. Existing solutions are often expensive, difficult to scale, and limited in responsiveness, creating inefficiencies and financial risks. IVSEN tackles these challenges by developing a microsatellite platform optimised for Very High Resolution (VHR) EO applications, with a focus on industrial scalability, ease of assembly, weight optimisation, and cost efficiency, plus a clear user-driven approach to provide tangible operational, financial, and efficiency benefits to energy networks users.

Customers and their Needs

The Integrated VHR Satellite for Energy Networks (IVSEN) targets energy operators and infrastructure managers, who face rising challenges in monitoring and securing their networks. Their main problems are vegetation encroachment around power lines, which causes outages and wildfire risks, and damages assessment after extreme weather events, increasingly disrupting transmission and distribution systems. Current inspection methods—manual patrols, helicopters, or drones—are costly, inefficient, and lack scalability, limiting visibility and timely response.

IVSEN proposes a satellite-based monitoring solution built around a Very High Resolution (VHR) payload, specifically optimised for imaging infrastructures. Users will be actively involved through requirement collection, questionnaires, reviews, and validation activities. The solution empowers them to shift from reactive to proactive asset management, lowering maintenance costs, enhancing risk assessment, and improving resilience. Ultimately, IVSEN provides utilities with timely, scalable, and cost-effective EO insights to safeguard infrastructure and ensure reliable service.

Targeted customer/users countries

The targeted user base spans Europe and the Americas.

Product description

The IVSEN project is developing a Very High Resolution (VHR) Earth Observation satellite optimised for energy network monitoring. The payload is a telescope achieving <0.5 m resolution, integrated into a compact, lightweight platform compatible with small satellite launches.

Key innovations include agile and stable ADCS for precise pointing, high-rate communications, optimised power management and advanced thermal stability. Beyond the satellite, IVSEN delivers an end-to-end solution: data acquisition, AI-driven analytics, and an end-user platform offering dashboards, 3D visualisation, and automated reporting. Designed as a “flying camera,” the system balances bespoke integration with scalable standardisation, delivering both a satellite product and a turnkey service for infrastructure monitoring. This ensures utilities gain actionable insights to enhance grid resilience, reduce costs, and improve operational efficiency.

Added Value

The IVSEN solution delivers clear advantages over both payload and service competitors who offer Very High Resolution (VHR) optical instruments which are usually heavier, less optimised for microsat platforms, or produced on an ad-hoc basis.

IVSEN’s payload, derived from the VHR SATLANTIS proprietary payload concept, combines proven CubeSat technologies with miniaturised optics to achieve sub-50 cm resolution in a compact, scalable, and cost-efficient design—making it uniquely suited for agile EO missions targeting energy networks.

In terms of end-to-end services, current leaders provide analytics for utilities, but rely largely on third-party satellite data, limiting control over revisit time, tasking flexibility, and long-term cost structures. By integrating a proprietary VHR satellite with dedicated service elements, IVSEN offers utilities both ownership options (a standalone satellite) and a turnkey monitoring service (with analytics and periodic reporting). This dual approach ensures greater responsiveness, higher data reliability, and direct tailoring to energy-sector needs.

Current Status

The IVSEN activity builds on SATLANTIS’ VHR payload, the largest optical instrument in its portfolio, and it leverages heritage from the flying iSIM technology. Current work focuses on payload interfaces, integration, and front-end electronics to ensure compliance with EO mission requirements.

On the platform side, Alén Space is leading new designs for the microsatellite subsystems, while solar panel development will adapt lightweight, DHV-reinforced composites to meet demanding mass and geometry requirements. On the Data Segment side, GeoAI provides expertise based on its user platforms and dedicated algorithms for energy networks-related applications.

Saturnalia HFE

Objectives of the Product

Crop insurance faces several challenges that can affect its effectiveness and sustainability. One significant issue is the inefficiency and high costs associated with loss adjusters. The process of assessing crop damage often relies on manual inspections, which can be time-consuming, inconsistent, and prone to human error.

Saturnalia leverages cutting-edge satellite technology to provide daily monitoring and analysis of agricultural crops. Our service offers near real-time insights into crop health, growth patterns, potential issues and damage assessment, enabling growers and crop insurance companies to make informed decisions swiftly and accurately.

Insurance companies benefit from our technology to better estimate risks, manage claims, reduce fraud, and assess damage from natural disasters. The potential for transformation in these traditionally conservative industries is immense. The Saturnalia monitoring service is a tool for insurance companies to quickly assess the extension of damage and support the loss adjusters’ activity in the field.

Customers and their Needs

Insurance companies need to support experts in correctly assessing damage caused by natural events (hail, frost and excess of water). They also need prompt access to weather data for damage assessment. Moreover, they need to measure risk associated with every single parcel crop.

Targeted customer/users countries

Our targets are crop insurance companies operating in Europe, especially in Italy.

Product description

Saturnalia aims to become a reference to help loss adjusters in the field. Our plan is to use satellite imagery to support crop insurance activities. Automatic damage assessment is not available on the market as it requires data, know-how and continuous interaction with end-users to be built. Loss adjusters are going to use the Saturnalia app to collect data in the field, providing a constant feedback flow.

Added Value

We stand out as we have built, together with the leaders in the agriculture insurance market, a full suite of tools to fill the data gap in the crop insurance value chain: from daily satellite monitoring up to an app for data collection in the field, we are covering every aspect of the value chain. Our main added value is the strong connection with the end users and fast response given the feedback from them. Data from satellites enable an objective measurement of anomalies, leaving loss adjusters to focus on the other operations, improving throughput and accuracy.

Current Status

The project has officially started. Now, the focus is on collecting necessary data from insurance companies. Once ready, we will start retrieving all the satellite and weather to train the models.

CORE

Objectives of the Product

Monitoring ground movement and infrastructure stability is essential for public safety, but traditional methods are expensive, slow, and risky, especially in remote or difficult-to-access areas. CORE, developed by GVL, addresses this challenge with a smart and scalable solution that combines satellite radar (InSAR) and high-resolution optical imagery.

By integrating these technologies with artificial intelligence and machine learning, CORE delivers accurate, automated, and continuous monitoring of land and infrastructure. It provides early warnings of ground shifts or settlement, helping engineers, planners, and environmental managers act before issues escalate.

Unlike many systems that rely on a single data source, CORE combines multiple streams to offer a clearer and more comprehensive view of conditions across urban, rural, and natural landscapes. Its use of cost-effective off-the-shelf components and open-source tools also makes it more affordable and easier to deploy.

With CORE, users can remotely monitor large areas, reduce the need for site visits, improve safety, and make more informed decisions that support both infrastructure resilience and environmental sustainability.

Customers and their Needs

CORE is designed for infrastructure owners, public authorities, environmental agencies, and engineering consultancies that need accurate, reliable ground motion data to ensure safety, maintain assets, and meet regulatory requirements. These customers often manage large, complex, or remote sites such as railways, bridges, skyscrapers, peatlands, and floodplains. Challenges include high costs, logistical complexity, and safety risks associated with traditional ground-based surveys. In many cases, manual monitoring is not feasible due to difficult terrain, vegetation cover, or restricted access. As a result, they lack continuous, real-time insights into ground stability, increasing the risk of delayed responses to potentially hazardous shifts.

CORE addresses these needs by providing a cost-effective and scalable monitoring solution that delivers continuous, automated, and accurate ground motion data across large areas. Customers can access processed insights via a user-friendly platform without requiring specialised expertise in remote sensing or data analysis. Through the integration of AI, CORE also supports early warning systems and predictive maintenance planning. By reducing the need for on-site visits and improving access to high-quality geospatial data, CORE helps users manage risk more effectively, prioritise interventions, and improve long-term resilience of both infrastructure and the environment.

Targeted customer/users countries

CORE is targeted at customers and users across Europe, with a primary focus on the United Kingdom.

Product description

CORE is a modular, AI-powered ground motion monitoring system that integrates satellite radar (InSAR), GNSS-equipped corner reflectors, and optical imagery. It is designed to deliver continuous, accurate, and automated monitoring of ground and structural movement across diverse terrains, including urban areas, infrastructure corridors, and natural environments.

The system’s core building blocks include:

  1. Sensing Layer – AT-InSAR and PSInSAR data and optical satellite imagery
  2. Data Processing Layer – Open-source software and GVL’s AI/ML engine for data fusion, anomaly detection, and predictive analysis
  3. Visualisation and User Interface – A web-based platform that displays results through dashboards, maps, and alerts, enabling intuitive access for both technical and non-technical users

Innovation lies in the fusion of multiple sensing modalities with machine learning to improve data reliability, automate processing, and enhance early warning capabilities. Unlike conventional systems that rely on a single method, CORE adapts to varied monitoring environments with greater accuracy and efficiency. Users interact with CORE via a cloud-based portal, where they can access historical trends, real-time updates, and custom alerts to support decision-making, compliance, and risk management.

Added Value

CORE provides a significant improvement over traditional ground motion monitoring methods by combining multiple sensing technologies including InSAR and optical data into one integrated solution powered by artificial intelligence and machine learning. Most existing systems rely on a single data source, such as PSInSAR, and often require dense infrastructure or frequent field visits. The project stands out by offering reliable monitoring across a wide range of environments including rural, vegetated, and remote areas where conventional methods are less effective or too costly. The use of Al-Terrain InSAR and open-source processing tools allows for higher quality results at a lower cost, making the solution more scalable and accessible. The platform delivers continuous and automated insights, providing users with timely alerts and predictive analytics to support proactive management and risk mitigation. It is also designed with a user-friendly interface that does not require specialist expertise, enabling broader adoption. Compared to other systems that may be limited in scope, expensive to deploy, or slow to respond, CORE offers better coverage, increased reliability, and faster access to decision-ready data. This results in safer, smarter, and more cost-effective monitoring for infrastructure and environmental applications.

Current Status

The project has started in April 2025 and is progressing well according to the plan.

RESTART

Objectives of the Product

Cotton farm professionals encounter interconnected agronomic and financial challenges, including unpredictable crop health, pest infestations, water shortages, and fluctuating market prices. RESTART aims to directly tackle these challenges by providing:

  • Crop Monitoring, Pest and Irrigation Alerts: by utilising Earth Observation (EO) and meteorological data, farmers obtain timely notifications regarding crop stress, pest development, and irrigation needs, enhancing field management practices.
  • Yield Prediction: in-season AI models combine remote sensing and agronomic data to produce precise, location-specific yield projections.
  • Financial Forecasting: analytics of the cotton market, along with predictive modeling and hedging strategies, enable farmers to optimise the timing of sales and safeguard profit margins.

RESTART inherits knowledge from previous ESA and EU-funded projects, effectively combining agronomic intelligence and financial forecasting within a scalable, tiered-subscription platform. The system is designed for wide accessibility, suiting both smallholders and large cooperatives.

Customers and their Needs

RESTART targets smallholder cotton farmers, agricultural cooperatives and organisations and large agribusinesses. In cotton farming, it is common for one entity to manage everything from budgeting and input scheduling to production planning. These raise the need for practical, holistic insights—such as crop health alerts, irrigation needs, pest outbreaks predictions, and yield forecasts—to enhance productivity and reduce costs. The financial forecasting service provided by RESTART is essential, as customers usually lack knowledge in economics, trading and hedging strategies, leading to poor decisions regarding the crop market. In addition, individuals possess inadequate expertise in satellite and/or AI technology, emphasising the need for user-friendly and comprehensive dashboards and notifications. Consultants and traders will benefit from RESTART, as their roles need insightful, scalable tools to assist multiple farms or large agribusinesses.

Targeted customer/users countries

Greece, Europe, scalable for global coverage.

Product description

RESTART is an AI-driven decision support platform based on EO satellite imagery, real-time meteorological and market data. Its services include:

  1. Crop Monitoring
  2. Predictive Alerts for pest outbreaks
  3. Predictive Alerts for water stress
  4. Yield Forecasting using seasonally adaptive AI models
  5. Financial Insights through cotton futures price forecasting and market insights.

RESTART stands as a first-of-its-kind platform due to its:

  • Holistic cotton farm management, including the consolidation of agronomic and financial forecasting within a single platform.
  • Advanced multi-parametric modelling, integrating a wide range of input data including EO, pest scouting, weather, historical yield data, soil moisture levels, financial markets’ data and other economic indicators.
  • Proactive modelling, anticipating risk before onset.
  • Scalable cloud architecture that accommodates both smallholders and large farms.
  • User-centric interface that provides insights through web dashboards, alerts, and APIs.

Farmers engage via a platform that provides insightful dashboards, interactive visualisations, and alert configurations. The platform’s architecture consists of:

  1. Data ingestion layer encompassing satellite, weather, and market APIs
  2. Agronomic and financial processing modules (model training and forecasting)
  3. Storage backend for time-series and historical data
  4. Web and API services, including dashboards, visualisations, and alerting functionalities.
Added Value

RESTART offers distinctive added value compared to existing AgriTech platforms through the following key differentiators:

1. Integrated Agronomic and Financial Intelligence into a Unified Platform

Unlike other platforms that focus exclusively on agronomic or financial advisory systems, RESTART uniquely combines satellite-derived agronomic risk models with AI-driven cotton price forecasting. This integration enables farmers to align field operations with financial decision-making in order to optimise cotton farm management.

2. Predictive Analytics Across Diverse Data Sources

RESTART utilises EO, weather, and financial datasets to improve essential agricultural and business operations, including crop monitoring, yield prediction, pest and irrigation alerts and cotton price forecasting. This AI-driven, multi-dimensional method surpasses single-focus tools by providing proactive, accurate insights that help professionals optimise both field performance and business outcomes.

3. Scalable and Accessible Service

RESTART provides scalable, cloud-based analytics through user-friendly web visualisations and automated alerts designed for both smallholders and large farms, enhancing decision-making promptness and adoption rates.

 4. Cost-Effective, Tiered Subscription Framework

RESTART offers a flexible pricing structure tailored for different cotton farm sizes, thereby democratising access to advanced analytics. This approach ensures affordability in contrast to high-cost competitor enterprise solutions, while maintaining high service value and return on investment.

Current Status

The RESTART activity is currently in its De-risking Cycle (M1–M10).

Ellipsis Map Engine

Objectives of the Product

Existing tabular compute engines are not built for raster data.

The problem is that existing compute engines force users to manually catalogue, shard, and mosaic their raster data for distributed compute and integration. This slows down spatial data science, making professionals lose weeks to manual data preparation and awkward workarounds, instead of gaining insights and scaling capabilities.

As a solution, Ellipsis Map Engine brings users map-native, distributed analysis to all their geospatial data types so they can run their workflows instantly, without the manual work. It’s the missing piece, the fit-for-purpose raster data Lakehouse that brings users fast and easy spatial data science at scale:

  • Automate raster data ingestion, management and distribution
  • Run scalable, flexible and interactive spatial analytics effortlessly
  • Get insights across GIS & non-GIS systems instantly
Customers and their Needs

The targeted user segments are Space/EO-powered value-adders, Civil Engineering – environmental (hazard), water and/or geotechnical engineering and consulting -, and P&C/Ag. insurance (insurance, reinsurance, GIS/NatCat data and/or consulting services). 

Because the state of the art in performant geospatial data analytics currently lacks the possibility to create raster-native frameworks, the existing table-native engines are unable to automatically ingest and spatially shard raster data, leaving the job of catalogue building, sharding and mosaicking to the end user. This is a major handicap for our target customer segments (all professionals who rely on EO/spatial data and who want to use and integrate this into data science workflows, either for internal use or to provide better/new services to clients). The lack of infrastructure that supports such interactive data science on raster data is increasingly painful because EO and environmental data (commonly found in raster format) are increasingly relevant due to climate change and environmental risks that are affecting the industries of our target customers.

End users (representing each of the target segments) are involved in the activity as pilot users and will provide us with feedback and validation throughout the project.

Targeted customer/users countries

Committed pilot users are confirmed for The Netherlands and France. Our target segments have a global presence and Ellipsis Map Engine, as an infrastructure solution, is not geographically limited.

Product description

Under this activity, we are building the Ellipsis Map Engine. This product complements existing table engines by uniquely allowing users to load any EO and spatial data into a cluster, and have each node automatically host a section of the dataset based on geography (spatial sharding). So, when a Python command is created, it can be run both rapidly and geospatially aware, as each node in the cluster only needs to execute the command for the geographic section it has loaded. This simple but effective strategy supports use cases in which ever changing spatial logic needs to be applied to large raster/spatial data on-the-fly.

Our team’s existing product, Ellipsis Drive, is a central repository for discovering, managing and consuming spatial data across teams, organisations and workflows. Clients pay a monthly/quarterly/yearly fee to use Ellipsis Drive to search, access and leverage their EO/spatial datasets as high performance and interoperable web services (storage-based PaaS model). The Ellipsis Map Engine is an extension to this existing solution, adding the option to apply flexible and high-performance analytics to the spatial data that customers are hosting (adding a fee for use of Processing Units under the same Paas model).

Added Value

Users can automatically run arbitrary logic on any EO/spatial data in a distributed and spatially aware way and use results in both GIS and non-GIS systems instantaneously. This unique capability allows them to get answers to ad hoc spatial analysis challenges easily and time efficiently, just like they are used to when analysing table-native data with regular tabular engines. Thus, this tool creates value for Data Scientists, Actuaries and Geo Engineers who work with EO and EO-derived (raster) data, allowing these professionals to apply complex – and ever changing – analytics to spatial/EO data in a highly time-efficient way.

For Data Scientists, consultants, and actuaries in P&C/Ag. (re)insurance, this directly supports highly effective pricing, (aggregate) risk mapping, portfolio management and claims operations (all processes relying on EO/raster data and ad hoc spatial logic).

For Data Scientists, Consultants and (Geo)Engineers in civil engineering this means getting the answers to their client’s questions much more quickly and accurately, including for governments, environmental agencies as well as energy and utility companies. For Data Scientists and Consultants working at EO value-adding companies, providing flexible and ad hoc EO-powered data services is much easier and more cost efficient.

Current Status

The activity has just kicked off. The project team is working to deliver on its first milestone, currently conducting market research for requirement definition and prioritisation, creating and fine-tuning the pricing model, and building fitting B2B go-to-market alliances and channel partnerships. Soon we will also start with technical storage and compute design, product development and the creation of detailed user stories.

FANTOM

Objectives of the Product

FANTOM supports the Department for Environment, Food & Rural Affairs ambition “to make our air purer, our water cleaner, our land greener and our food more sustainable. Our mission is to restore and enhance the environment for the next generation, and to leave the environment in a better state than we found it.”

FANTOM builds a high spatial and temporal resolution UK-centric database of agriculture and biodiversity markers with deep thematic content and context to support schemes aimed at the UK’s Agricultural Transition as well as supporting Net Zero and climate change activities.

These markers, schemes and impact assessment layers will be available to all government agencies, associated arms-length bodies, and commercial companies to be able to monitor and measure the progress of their sustainability activities and interventions.

Initially focused on delivering markers to support schemes for agricultural Paying Agencies, the aim is to ensure that the markers and schemes have a wider commercial need across government, non-government and commercial markets.

Furthermore, it will change the current perception of Earth observation-based land monitoring that not only looks at the asset but also its relationship with neighbouring assets to support landscape-level sustainability activities across the wider landowning community.

Customers and their Needs

FANTOM’s primary intended customers are agricultural Paying Agencies in the United Kingdom and the European Union.

First, these organisations deal with a huge number of landowners and require ongoing information on their customer base. This assessment demands automated and reliable satellite data analytics to ensure that subsidies and interventions are made correctly and can be monitored.

Second, there are a wide range of users interested in monitoring their land to evaluate its habitat quality and biodiversity value. This includes Paying Agencies but also other government bodies such as Environmental Ministries plus commercial actors such as developers, infrastructure owners and more. These users face the challenge of understanding the impact of their actions on the wider landscape.

Targeted customer/users countries

UK, EU

Product description

FANTOM delivers high spatial and temporal resolution markers to support agri-environmental schemes through the analysis of EO data and its integration with ancillary information.

The main subsystems and components of FANTOM are:

1. Data Ingestion and storage: handled primarily by Earth-i’s existing satellite and in situ data processing workflow with necessary customisation for FANTOM.

2. Data Processing:

Depending on the marker requirements:

  • Processing scripts capture the workflow and procedures to be applied to the data to realise the markers and evaluations;
  • An AI toolset provides the facility to analyse imagery using tools such as computer vision, machine learning and neural networks;
  • Scheme scripts will combine information about multiple markers to perform scheme assessments.

3. Data Analysis: analytics performed using a combination of sophisticated computer vision techniques and machine learning models to pick out specific identifiable features in the source satellite data. Outputs the markers required for the individual scheme assessments.

4. QA/QC Pipelines: QA/QC pipelines allow both manual and automated intervention.

5. Data visualisation: using existing platforms and open-source tools, visualisation will allow the user to overlay the resulting markers on the map, chart the outcomes and make rapid statistical assessments of marker outcomes and scheme results.

Added Value

FANTOM will reduce the amount of time that it would otherwise take Paying Agencies to generate a full and comprehensive set of monitoring indicators for the new Environmental Land Monitoring approach being rolled out across the UK as the successor to the EU Common Agricultural Policy. This means they will be able to monitor compliance and effectiveness of new policies right from the outset. The data products themselves represent value in the form of the customised, rigorously tested algorithms developed and implemented, along with a robust quality control regime, providing data that can be relied upon and can be verified with physical observations in the field. The markers and scheme assessment outputs scale reliably to address the Impact Potential, Technical Feasibility and Financial Viability of the monitoring.

FANTOM can promote the integration of ecological knowledge about the landscape into a data product, and model the change of the Habitat Quality and Biodiversity Potential so that customers can consider how a scheme is influencing changes over time and integrate this into policy development.

Current Status

The FANTOM project kicked off in March 2025. The first phase of the project is focused on consolidating the requirements baseline through workshops with key end users and stakeholders. The workshops explore the technical, performance, timeliness, quality and product format requirements.

HABTRAIL

Objectives of the Product

HABTRAIL addresses the challenges posed by Harmful Algal Blooms (HABs), which impact aquaculture, public health, tourism, and insurance sectors. HABs contaminate seafood, disrupt aquaculture operations, cause economic losses, and pose risks to human health. Traditional monitoring methods rely on costly, time-consuming water sampling and lab tests, often leading to delayed responses.

HABTRAIL provides an AI-driven early warning system that accurately predicts HAB occurrences and bivalve toxicity up to three days in advance. The platform integrates satellite and in situ data with advanced machine-learning models to deliver real-time insights.

Users access the predictions through a web dashboard, where they can visualise HAB severity, receive species-specific interdiction forecasts, and configure personalised alerts. Aquaculture operators can optimise harvesting schedules, public health offices can make informed safety decisions, and tourism and insurance entities can manage risks proactively.

By offering predictive analytics and automated alerts, HABTRAIL shifts HAB management from reactive to proactive, reducing financial losses, improving food safety, and ensuring sustainable marine resource use. The platform’s expansion will enhance its accuracy, cover more geographic regions, and incorporate additional environmental factors to support decision-making and regulatory compliance further.

Customers and their Needs

HABTRAIL targets aquaculture entities, national regulatory bodies, public health offices, tourism businesses, and insurance companies. These customers face significant challenges due to Harmful Algal Blooms (HABs), which disrupt operations, endanger public health, and cause economic losses.

  • Aquaculture entities (fish and bivalve farms) rely on HABTRAIL’s predictive insights to minimise losses by adjusting harvesting schedules and ensuring seafood safety. Their challenge lies in obtaining timely, cost-effective, and reliable data to mitigate production risks.
  • National regulatory bodies oversee seafood safety and environmental monitoring. They need accurate, real-time data to issue interdictions and ensure compliance, but current monitoring methods are reactive, costly, and slow.
  • Public health offices must prevent foodborne illnesses from toxic seafood. They require predictive tools to safeguard consumers and optimise response measures.
  • Tourism businesses (beach operators, tour agencies) suffer from reduced visitor numbers when HABs affect water quality. They need timely forecasts to adjust plans and avoid reputational damage.
  • Insurance companies rely on environmental risk assessments to price policies and process claims. A lack of reliable historical data makes risk evaluation difficult.

HABTRAIL directly involves these stakeholders by providing an AI-powered web dashboard with real-time predictions, automated alerts, and risk assessments, enabling data-driven decisions to mitigate the impact of HABs.

Targeted customer/users countries

HABTRAIL initially targets Portugal for deployment, focusing on its coastline and estuarine zones. Expansion is planned for Spain, France, and Italy in Europe (Year 2), followed by the USA and Chile in the Americas (Year 3), and later Japan and South Korea in Asia (Year 4-5). These countries have significant aquaculture industries, regulatory bodies for marine monitoring, and coastal tourism sectors affected by Harmful Algal Blooms (HABs).

Product description

HABTRAIL is an AI-powered web-based platform that predicts, monitors, and issues early warnings for Harmful Algal Blooms (HABs). It integrates satellite data with machine learning models to forecast HAB severity and bivalve toxicity with over 90% accuracy for the next three days. Users interact with the platform through a web dashboard, where they can visualise HAB severity maps, access species-specific interdiction forecasts, and configure custom alerts via email or SMS.

Innovative Aspects
  • AI-Driven Predictions: Unlike traditional reactive monitoring methods, HABTRAIL uses deep learning to analyse satellite imagery and identify patterns in HAB development.
  • Multi-Algorithmic Approach: Combines clustering, pixel segmentation, and deep neural networks to improve detection accuracy and reduce false positives.
  • User-Friendly Interface: Provides real-time, interactive dashboards tailored for aquaculture operators, regulatory bodies, public health officials, and insurers.
  • Scalability & Regional Adaptation: The model is designed to expand to new coastal areas with minimal additional data collection.
User Interaction
  • Aquaculture entities optimise harvesting schedules and reduce economic losses.
  • Regulators receive interdiction forecasts and cross-validate decisions.
  • Tourism and insurance entities monitor risk levels to adjust policies and business operations.

By shifting from a reactive to a proactive approach, HABTRAIL ensures safer, more sustainable management of marine resources.

Added Value

HABTRAIL brings significant added value compared to traditional monitoring methods and existing competitors by offering a proactive, AI-driven approach to Harmful Algal Bloom (HAB) prediction.

Key Differentiators
  1. AI & Multi-Algorithmic Analysis – Unlike competitors that rely on threshold-based methods, HABTRAIL uses deep learning to detect patterns in satellite imagery, reducing false positives and increasing predictive accuracy.
  2. Real-Time & Predictive Alerts – While current monitoring systems rely on costly, delayed lab testing, HABTRAIL delivers three-day advance forecasts, allowing aquaculture businesses and regulators to act before contamination occurs.
  3. User-Centric Dashboard & Automation – Unlike government-provided raw data portals, HABTRAIL offers a visual, user-friendly dashboard with customisable alerts, making it accessible to non-experts.
  4. Regional Adaptability & Scalability – Many existing solutions are site-specific or limited to post-event analysis. HABTRAIL’s AI models are scalable and adaptable to new coastal areas without requiring extensive new data collection.
  5. Comprehensive Stakeholder Support – Unlike competitors that focus solely on aquaculture or research, HABTRAIL supports national regulators, public health, tourism, and insurance sectors, integrating multiple industry needs into one solution.

By transitioning from a reactive to proactive strategy, HABTRAIL minimises financial losses, improves food safety, and enhances environmental protection better than any existing alternative.

Current Status

HABTRAIL is in an advanced development phase, with key achievements including the successful implementation of AI-driven predictive models, achieving over 90% accuracy in forecasting HAB severity and bivalve toxicity. A functional web dashboard has been developed, providing real-time data visualisation and customisable alerts.

Current work focuses on expanding geographic coverage from the initial L7C2 (Algarve) test zone to the entire Portuguese coastline, refining species-specific interdiction forecasts, and enhancing model precision using additional environmental data.

The User and Technical requirements were validated by Aquaculture Entities and Tourism Entities, gathering feedback for system optimisation, and integrating new features such as pixel-based HAB, SST, Salinity and CHLOR_A predictions, as well as Pollution, Sediments, Tide Levels, and Beach Information. Future expansion plans target Spain, France, and Italy in the next phase.

The project is progressing on schedule, with ongoing validation efforts moving on to the System Design and Data Integration phase, ensuring operational readiness for commercial deployment.

HySpex MSC

Objectives of the Product

The Methane Satellite Camera (MSC) from HySpex addresses the urgent need for accurate and cost-effective monitoring of methane emissions. Methane is a highly potent greenhouse gas, and is the second greatest contributor to global warming, only second to carbon dioxide. To limit emissions of methane, upcoming regulations demand effective emission tracking. By optimising our camera for satellite use, we will provide a reliable solution for industries to identify and reduce methane leaks at large scales, contributing to global efforts to mitigate climate change.

HySpex MSC allows for rapid deployment of methane detection capabilities as it aims to be a space-proven commercial-of-the-shelf solution, building upon our decades-long experience building hyperspectral cameras.

Customers and their Needs

Target customers for HySpex MSC are Earth Observation (EO) satellite service providers within the energy and environmental sector that operate their own satellites and constellations.

Currently, if a satellite service provider wants to offer methane detection as part of their services, their only option is to design and build their own instrument capable of detecting methane. This is a complex task requiring substantial financial investments over several years, involving multiple stages of critical research, development, and testing. The process carries a high level of risk, with no guarantees that the system will work. Such development typically falls outside the core competencies of these companies, and they would prefer to avoid such high-risk projects. Our HySpex MSC provides a cost-effective, ready-to-use solution that eliminates the need for service providers to develop such custom instruments, thereby reducing both financial risk and time to market.

Targeted customer/users countries

Global, but with specific focus on Europe and the US, who implement strict methane regulations.

Product description

HySpex MSC is a near-commercial off-the-shelf (COTS) instrument designed for methane detection from satellite platforms. Within the broader system and service ecosystem of its target users, HySpex MSC enables customers to launch and operate satellites capable of delivering high-resolution methane emission maps over large areas. This empowers end-users with actionable data for monitoring and mitigating methane emissions.

HySpex MSC incorporates our proven proprietary designs to achieve its mission. At its core it is a cutting-edge spectrograph design, integrated with a newly developed telescope and a diffraction grating optimised specifically for methane detection. Together, these components form a highly stable push-broom camera architecture.

Key performance highlights include:

  • Ground Sampling Distance (GSD): 60 meters
  • Spectral Sampling: < 2.5 nm in the spectral range of 1340–2500 nm
  • Signal-to-Noise Ratio (SNR): Optimised for precise methane detection

This innovative combination delivers unparalleled accuracy and reliability in methane mapping, while providing a scalable, efficient solution for satellite missions.

Preliminary overview of the HySpex MSC
Added Value

HySpex MSC is the first commercially available, off-the-shelf (COTS) hyperspectral camera capable of detecting methane from space. This innovative instrument empowers less integrated Earth Observation (EO) service providers to access methane monitoring capabilities without the need for complex in-house development.

Currently, providers without such access face an overwhelming challenge: building their own methane detection payload. This process demands substantial financial resources, years of research and development, rigorous testing, and carries significant risks, with no assurance of success. HySpex MSC eliminates these barriers by offering a ready-to-deploy, low-risk alternative.

By bridging this gap in the EO value chain, HySpex MSC accelerates the adoption of methane monitoring globally. This directly supports efforts to combat climate change by enabling broader and faster deployment of EO services for methane tracking.

HySpex MSC’s streamlined approach significantly reduces costs, risks, and time-to-market, delivering unparalleled value over in-house development while fostering innovation across the EO industry.

Current Status

Building upon the existing SWIR640 camera, HySpex MSC leverages HySpex proven spectrograph design, allowing for a short development cycle of only 12 months. Optical design and mechanical design of the new camera is well underway and will be completed early in the first quarter of 2025.