Archives: Activities Portfolio

Twinspector

Objectives of the Product

Twinspector is designed to provide reliable and independent access to very high-resolution satellite imagery that helps utilities better understand and manage critical infrastructure and the environment.

The product addresses a growing need for timely, accurate and scalable information to monitor assets such as railways, power networks, pipelines and surrounding land areas, especially as infrastructure systems become more sensitive and exposed to environmental and operational risks.

The primary objective of Twinspector is to deliver consistent and very high-quality Earth observation data that enables early detection of changes, supports risk assessment, and improves decision-making for infrastructure operators and public authorities.

By offering both standard and three-dimensional stereo imagery, the product allows users to gain deeper insights into terrain, vegetation and asset conditions over large geographic areas.

Customers and their Needs

Twinspector addresses the needs of infrastructure operators and public-sector organisations that rely on continuous, accurate and scalable geospatial information to manage spatially distributed assets.

Primary customers include operators of linear infrastructure such as railways, electricity transmission and distribution networks, pipelines and transport corridors, as well as national and EU authorities responsible for infrastructure oversight, environmental monitoring, and regulatory compliance.

Infrastructure operators require regular access to very high-resolution optical imagery to detect small-scale changes, assess risks, and monitor asset conditions along narrow corridors over large geographic areas. Stereo imagery and three-dimensional information are essential to support height-related risk assessment and vegetation-related hazards.

Customers also require predictable revisit rates, consistent imaging conditions, and reliable long-term data availability to integrate satellite-based insights into operational planning and maintenance workflows.

Across all customer groups, seamless integration of data products into existing geospatial systems and long-term service continuity are key requirements for sustainable operational adoption.

Targeted customer/users countries

Europe (including Germany and wider EU/EEA), United Kingdom, North America (USA/Canada), and selected growth markets (e.g., Asia-Pacific, including Australia and Africa).

Product description

Twinspector is a dedicated Earth observation mission based on two identical optical satellites flying in a trailing formation, enabling the acquisition of very high-resolution mono and stereo imagery for infrastructure and environmental monitoring. 

The product offers panchromatic imagery with sub-meter spatial resolution (≤ 50 cm GSD) and multispectral imagery with meter-scale resolution (≤ 2 m GSD) across four spectral bands (blue, green, red, near-infrared). Stereo imaging is enabled through controlled off-nadir acquisitions with defined stereo geometry, supporting the generation of three-dimensional information products, such as Digital Surface Models, with high geometric consistency. Typical swath widths of > 14 km allow efficient coverage of narrow corridors and long linear assets.

Intelligent onboard processing clips imagery to the corridor Area of Interest and applies cloud masking to reduce downlinked data and accelerate delivery.

On ground, the processing chain produces analysis-ready products (including stereo-derived 3D information) that feed LiveEO’s risk analytics services (Treeline and SurfaceScout). Users interact via a web application and API, with export formats (e.g., geospatial layers and reports) for integration into enterprise asset and work management systems.

Added Value

Twinspector strengthens the value of LiveEO’s existing analytics products, Treeline and SurfaceScout, by providing a dedicated and reliable source of very high-resolution mono and stereo satellite imagery tailored to infrastructure monitoring needs. Treeline supports utilities and railway operators in identifying vegetation-related risks along linear assets, while SurfaceScout focuses on detecting third-party activities, terrain instabilities, and other threats in the surrounding environment.

By ensuring predictable data availability and consistent imaging conditions, Twinspector significantly increases the scalability of these products, allowing customers to monitor larger networks more frequently and with greater confidence.

By reducing dependency on manual field inspections and emissions-intensive aerial patrols, Twinspector helps lower operational costs, improve safety for field personnel, and support more sustainable infrastructure operations. As a dedicated and scalable data source, Twinspector strengthens the long-term reliability and continuity of LiveEO’s services, enabling customers to embed satellite-based insights into routine asset management workflows.

Current Status

The Twinspector activity is in the definition phase. User needs, mission objectives, and system-level requirements are consolidated and aligned with LiveEO’s existing products and target use cases. Initial concepts for the space and ground seg-ments, including stereo imaging and data processing, are defined, providing a solid basis for progressing into detailed design and implementation.

Current work focuses on system engineering, risk management, and procurement planning for critical payload and platform elements, including long-lead items. Coor-dination between the prime (service and mission owner), platform supplier, payload supplier and electronics partner is established to ensure consistent design and veri-fication approaches across the space segment.

TASCNET

Objectives of the Product

The request for new satellite-based observations and data management is largely dependent on centralised ground-based operations and constrained by the availability of the ground station, where data and commands are exchanged. These data are only made available for further analysis after many hours.

This limits the possibility to implement approaches such as ‘tip and cue’, since the ground segment needs to plan monitoring acquisitions, wait until the data reach a ground station, and only then process and analyse the data. In case something interesting is present in the acquisition (e.g. wild fire, dark vessel or illegal phishing), a follow-up acquisition can then be planned to gather more data, but only hours later.

The large delay between these actions limits the effectiveness of the monitoring actions. High-performance computing units and inter-satellite link connections through satellites are planned for many future constellations, and exploiting those assets will dramatically change the operational scenario: Earth observation data can be immediately processed, with AI algorithms used to extract relevant features and support autonomous decisions through AI agents, just using on-board resources and allowing an almost real-time monitoring, which would be impossible otherwise solution using transmitters based on vacuum tubes, like TWT, or a set of discrete solid-state components.

Customers and their Needs

Leonardo acts as the anchor partner and reference early adopter, with direct involvement in defining the system needs and mission integration constraints, particularly in view of its future Earth observation constellation.

In parallel, Leonardo has initiated preliminary technical discussions with Satlantis (Spain) and D-Orbit (Italy):

  • Satlantis, a vertically integrated smallsat operator and payload manufacturer, has expressed their interest in testing on-board autonomy features for responsive Earth observation scenarios. They represent a relevant user profile due to their dual role as integrator and operator of high-resolution optical payloads.
  • D-Orbit, as a platform and in-orbit service provider, is evaluating TASCNET for integration on its ION Satellite Carrier as part of future mission architectures with embedded edge computing.
Targeted customer/users countries

Spain and Italy

Product description

The TASCNET product will integrate the typical capabilities of the control ground segment with autonomous capabilities allowed by the growing availability of high-performance computing resources installed on the satellite platform and the possibilities offered by AI algorithms and agents.

The main goal of the product is to migrate some of the control functionalities currently performed the ground segment to the space segment. This change allows a constellation with required capabilities to autonomously update the mission plan if specific conditions are found, enabling the possibility to implement complex scenarios, such as ‘tip and cue’, without the need of support from ground operators, greatly reducing the latency.

TASCNET combines the elements from two main components into a hybrid mission control:

  • On-board software components. These components are installed on satellites with computing resources able to run complex algorithms required to process the payload data, identify the main features within the image, autonomously decide the following operations, and operate with other satellites of the constellation.
  • On-ground control ground segment components. These components have the responsibility to build the mission plan for each satellite of the constellation, but also to control the complex algorithms and the criteria for more complex tasks, such as ‘tip and cue’..
Added Value

TASCNET activities focus on three main pillars:

  • On-board scheduling and autonomous tasking: it enables satellites to autonomously generate observation schedules and dynamically adjust them in orbit to react to new information, ensuring optimal use of platform resources without ground intervention.
  • On-board Earth observation processing: TASCNET includes a library of AI models able to process payload data directly in orbit, extracting relevant features (e.g. changes, targets, anomalies). This unlocks new use cases such as intelligent alerting and automated re-tasking.
  • Next-generation ground segment: traditional control segments must evolve to integrate hybrid planning logics. TASCNET supports this evolution with a multi-master planning architecture that brings together autonomous onboard planning with strategic ground-based oversight.

We foresee the following main added values:

On-board EO Processing and Autonomous Planning

  • Detects opportunities and anomalies in real time, adjusting plans without waiting for ground commands.
  • Enables ultra-low-latency reaction chains, such as ‘tip and cue’.
  • Reduces bandwidth consumption and operational overheads by prioritising only high-value data for downlink.

AI-based Mission Optimisation

  • Intelligent agents.
  • Observation planning to improve responsiveness and reduce redundancy.

Hybrid Ground Segment Adaptation

  • TASCNET enables coexistence of on-board and on-ground control logic.
  • Enhances user interaction with the mission via predictive dashboards and dynamic plan reconciliation tools.
Current Status

The project has started identifying the most relevant constellation characteristics that can enable added value services, such as ‘tip and cue’ without ground segment assistance, the most relevant use cases and the associated mission planning challenges. The requirements review has been successfully held, and the team is working on the preliminary design review preparation.

SARTO

Objectives of the Product

The SARTO products are:

  • X-band High-Efficiency High-Gain 18-W Power Amplifier, for synthetic aperture radar (SAR) antennas;
  • X-band High-Efficiency 30-W High-Power Amplifier (HPA), for Earth observation satellite data down-link.

These products have the aim to offer a highly efficient, green and economical solution to system integrators of TR modules for SAR antennas. Being the HPA the most power-consuming of the RF line-up, the target is to develop a monolithic HPA, with the embedding of the driver function, leading to a huge increase of the power efficiency and a saving of up to 40% of the power consumption of SAR antennas.

This strongly increases the competitiveness of Earth observation satellites companies, which will offer a meaningful extension of the service to end users. In addition to this HPA, with the same objective of saving energy and room, a fully monolithic and very efficient HPA in X band for the data down-link is under development, in replacement of the current solution using transmitters based on vacuum tubes, like TWT, or a set of discrete solid-state components.

Customers and their Needs

The main targeted customers are Earth observation satellites, specifically the SAR antennas, and airborne and ground-based AESA radar integrators.

The product provides a solution to their need of better performances, higher integration and cost reduction with respect to the actual exploited solutions.

The main end users and potential customers provided their support for the definition of the product main requirements.

Targeted customer/users countries

The main users/customers are from Europe (Italy, France, Germany).

Product description

The product consists of a chipset of advanced High-Power Amplifier MMICs capable to provide the optimisation of the next X-band SAR Antennas for Earth observation in terms of power consumptions and encumbrances. In detail, two different monolithic circuits are proposed:

  • An X-band High-Efficiency High-Gain 18-W Power Amplifier, to be exploited as the final element of the RF frontend in the TR modules for SAR antennas.
  • An X-band High-Efficiency 30-W High-Power Amplifier, to replace the SSPA currently used on Earth observation satellites for data downlink.

When optimised for the maximum efficiency operating point, the GaN technology selected for the proposed activity is credited to deliver an output power of about 3.5 W/mm.

Based on these characteristics, a preliminary analysis identified the following baseline architectures for the two MMICs, respectively, targeting the required levels of Output Power, PAE and Gain.

The following figures show the preliminary architectures defined for the two HPAs.

18W pulsed X-band HPA: Baseline architecture
30W CW X-band HPA: Baseline architecture.
Added Value

The breakthrough for both amplifiers, with respect to the current solutions, is the exploitation, at X-band, of the 0.15 µm GaN/SiC technology usually optimised for applications at 30 GHz.

The high available gain, as well as the improved efficiency and the usual high-power density level of Gallium Nitride processes, allow to reach the required output power with a very high efficiency, leading to a notable reduction of both power consumption and dissipation.

Furthermore, the well-known robustness of this technology gives the advantage to satisfy the tight reliability constraints required by space applications with limited scaling of the overall performances.

For the two specific cases, the request of the High-efficiency Amplifier comes from the need to replace 1-to-1 the MMIC currently used in the RF frontend of the X-band SAR TR modules, but with the objectives to significantly reduce the power consumption, operating with a very-high efficiency, and, at the same time, to remove the buffer amplifier now used to amplify the power at the output of the beamformer, possible by integrating in the proposed single MMIC up to 4 stages of gain.

For a direct comparison, with respect to the amplifiers used on the TR modules of COSMO-SkyMed Second Generation (CSG), the proposed solution allows twice the power (18 W) with no more than 30% of additional power consumption (33 W).

Current Status

The activity is in its first preliminary phase.

FieldFinder

Objectives of the Product

FieldFinder addresses the challenge of generating consistent agricultural field boundary maps across diverse geographic regions. Existing field delineation models are often trained on regionally limited datasets and show reduced performance when applied to agricultural landscapes with different field structures.

FieldFinder extends an existing field boundary detection model with additional ground-truth data from multiple global regions. This improves model generalisation and reduces geographic bias in delineation performance.

The activity integrates multi-regional training datasets and scalable processing workflows using satellite imagery, primarily from Sentinel-2. The resulting service provides globally consistent agricultural field boundary datasets. These datasets support applications including supply-chain transparency, sustainability certification, agricultural monitoring, and policy support. Customers access the product through data delivery, API integration or tailored analytics. The activity builds on previous ESA-supported developments and extends them into a globally applicable field boundary mapping solution.

Customers and their Needs

Target customers include sustainability certification bodies, supply-chain transparency platforms, agricultural monitoring providers, public institutions, and climate and environmental analytics companies. These organisations require consistent agricultural field boundaries to monitor production areas, assess land-use change, and support regulatory compliance.

Existing field boundary datasets often perform well in specific regions but show reduced performance when applied to agricultural landscapes with different field structures, crop types and management practices. This limits the scalability of monitoring solutions and increases manual mapping efforts.

Customers require globally consistent field boundary datasets that perform reliably across diverse agricultural environments. FieldFinder addresses these needs by improving model generalisation through multi-regional training data and scalable processing workflows.

Customers engage through pilot projects, validation exercises, and early adoption of the generated datasets.

Targeted customer/users countries

Worldwide, with primary focus on the EU, the UK, and Switzerland as key customer and decision-making regions. Additional target markets include major agricultural production areas in South America (especially Brazil and Colombia), Southeast Asia (Indonesia and Malaysia), South Asia (India and Vietnam), and selected African countries such as Kenya, Ghana, Uganda, and Egypt, where demand for scalable agricultural monitoring and land-use transparency is growing.

Product description

FieldFinder is a satellite-based AI service that extracts agricultural field boundaries at scale. The system processes Earth observation imagery, applies deep learning segmentation models, and generates structured field boundary datasets.

The product architecture includes:

  • Satellite data ingestion (Sentinel-2 and complementary datasets)
  • Data preprocessing and tiling
  • Deep learning field boundary detection
  • Post-processing and geometry optimisation
  • Validation using ground-truth datasets
  • Data delivery via API and geospatial formats

The activity focuses on improving model generalisation by integrating multi-regional training datasets. This reduces geographic bias and improves performance across diverse agricultural landscapes.

Customers interact with the product through geospatial data delivery, API access, or tailored analytics depending on integration requirements.

Added Value

FieldFinder improves the consistency of agricultural field boundary mapping across different geographic regions. Existing solutions often rely on regionally limited training datasets and therefore show reduced performance when applied to new agricultural environments.

By integrating multi-regional training data, FieldFinder reduces geographic bias and enables globally consistent field delineation. This supports scalable applications in sustainability monitoring, supply-chain transparency, land-use analysis, and agricultural monitoring.

The activity builds on previous ESA-supported developments and extends them into a globally applicable service. This reduces development risk and increases technical maturity.

The resulting datasets reduce manual mapping effort, improve consistency, and enable large-scale monitoring across diverse agricultural landscapes.

Illustration: Field delineation comparison in the same area.
Left: model trained on different region data. Right: FieldFinder with improved boundary detection. Credit: Marple
Current Status

FieldFinder builds on an existing field boundary detection model and datasets developed in previous activities. Initial models and ground-truth datasets are available and provide a solid foundation for extension to additional geographic regions and agricultural landscapes. The activity expands multi-regional training datasets, improves model generalisation, and validates performance across selected pilot regions. Processing pipelines and system architecture are operational at prototype level and undergo iterative refinement. Customer engagement and pilot use cases support validation, performance assessment, and alignment with user requirements.

TychoBoB

Objectives of the Product

Satellites generate petabytes of raw data every day, yet traditional downlink-then-process workflows create delays and drive mission cost. TychoBoB solves this bottleneck with a general-purpose, FPGA-based edge computer that is installed directly inside a small satellite.

The platform is built for ease-of-use and affordability, enabling research institutes, SME payload developers and mission owners to run custom, high-performance applications in orbit with only a few engineering hours.

This is achievable with:

  • Remote access & hardware leasing.
  • Seamless external IP integration.
  • Developer-friendly toolchain.
Customers and their Needs

Early-stage Earth observation mission owners, university smallsat labs, and SME payload/algorithm developers need an affordable way to validate high-throughput onboard processing before they lock in a satellite bus or raise full mission funding.

Today, they either buy costly engineering models late in the schedule or rely on software emulators that hide hardware bottlenecks. Both options create re-design risk, months of delay and significant extra cost.
TychoBoB addresses these gaps by letting a two-or-three-person team load its own Linux services onto a flight-representative board in days, instead of months. A cloud-hosted FlatSat and short-term EM leasing remove the up-front CAPEX barriers, while a built-in functionality and Yocto flow reduce integration surprises later. During this InCubed activity, NTNU (research) and S&T Norway (SME) act as pilot users, feeding real requirements, IP cores and test cases into the development process.

Targeted customer/users countries

Norway, Sweden,  Denmark, Finland, Germany, Netherlands, France, Spain, United Kingdom, Italy, Greece, Portugal, Belgium, Austria, Ireland, and Switzerland.

Product description

TychoBoB is a Xilinx UltraScale+-based FPGA/ARM edge computer tailored for small satellites.

The compact board hooks directly to a wide range of sensors via LVDS/CameraLink, 1 Gb Ethernet, CAN, UART and SPI, ingests high-rate data streams, and executes sophisticated AI/ML workloads to detect events and alert end-users with minimal latency.

Key innovations:

  • Developer-friendly toolchain – reproducible Yocto build flow, automated CI tests and step-by-step tutorials that cut setup from weeks to hours..
  • Remote access and hardware leasing – prototype on a cloud-hosted FlatSat or with a leased engineering model before committing to flight hardware.
  • Seamless external-IP integration – architecture refined through continuous user input from algorithm developers, ensuring painless drop-in of third-party processing IP.
Added Value

TychoBoB cuts mission cost at two critical points: (1) it slashes the amount of raw data that must be downlinked, and (2) it lets operators process data in-orbit instead of paying for large cloud clusters on the ground.

A 2025 third-party market study comparing a “traditional” Earth observation satellite stack with a TychoBoB-enabled stack found:

  • Data-download charges: A decrease from € 230 000 to € 115 000 per satellite, an estimated 50 % reduction.
  • Ground IT processing & hosting: A decrease from € 50 000 to € 5000, an estimated 90 % reduction.
  • Overall system-hosting cost: From € 300 000 to € 250 000.

Total estimated savings are around € 210 000 per satellite, per year, without sacrificing data quality or latency.

Beyond direct OPEX gains, TychoBoB delivers:

  • Faster revenue-to-orbit: remote access and leasing mean payload teams iterate faster.
  • Lower technical risk: reproducible Yocto builds, automated CI and proven IP-integration pipelines cut integration failures.
  • Greater mission flexibility: users can push new AI models after launch, keeping the satellite relevant over its lifetime.

These combined advantages translate into shorter payback periods and higher ROI for commercial, governmental and research missions.

Current Status

As of July 2026, the activity is progressing towards a pilot test in October. Hardware is being sent to production and the software development is nearing an MVP. In terms of customer interest, the product is garnering increasing amounts of attention. Several large customers are interested, despite the original business hypothesis. However, they require additional radiation tolerance updates to hardware and software. The original smaller customer segments are also interested. TychoBoB is a likely candidate for two upcoming IOD opportunities. The deadline for go/No-go for these opportunities await confirmation.

HiCitySat

Objectives of the Product

A digital platform that combines GEOSAT’s Very High-Resolution Earth Observation satellites with other data sources to provide up-to-date visualisations and insights for local government city management. It offers two main product levels:

  • City digital models: High-resolution (40 cm) imagery from GEOSAT-2 is used to create detailed and accurate urban representations for GIS-based operations. Depending on subscription, users can access varying coverage areas and update frequencies, potentially reaching intra-daily updates. A 3D version is also available by integrating satellite imagery with in-situ data and stereo imaging techniques.
  • Tailored management insights: Delivered as a value-added service on the same platform, this level uses AI/ML and automated processing to provide advanced applications. These include land use classification and change detection, route optimisation for large vehicles and emergency scenarios, and monitoring the health of green urban areas.
Customers and their Needs

The platform primarily serves municipal authorities, supporting urban planning, cadastre, mobility, civil protection, and green area management. It has been already used in collaboration with multiple municipalities in Portugal and Spain.

It also targets intermunicipal and regional authorities, including regional coordination and development bodies, autonomous governments, and cartographic institutes, as well as administrations in island and southern regions. National mapping agencies are key users, such as geographic institutes, land and territory management directorates, and national statistics and geography organisations. Engagement is also beginning with national public administration entities.

The platform enables continuous land use monitoring, including tracking urban expansion, detecting changes in residential, industrial, and service areas, and mapping green spaces for distribution, preservation, and carbon capture potential. It also provides detailed road infrastructure characterisation, including route classification and critical point identification, while supporting cadastre management and assessing urban intervention impacts.

Key challenges addressed include maintaining up-to-date urban classification, optimising routes for heavy and non-standard vehicles, automating green space health assessment and monitoring sustainability through carbon capture evaluation, supporting more efficient and sustainable urban management.

Targeted customer/users countries

The approach is to first demonstrate the value proposition and project outcomes through a pilot project with Portuguese municipalities, followed by engagement with the private sector. In a subsequent phase, the target audience will expand to the Iberian Peninsula, including Portuguese regional and national authorities, as well as Spanish regional and national authorities. In the next step, the geographical scope will extend to Europe and Latin America, targeting regional and national governments. Finally, international expansion is foreseen, enabling engagement with regional authorities worldwide.

Product description

GEOSAT is developing a dynamic urban operations management tool delivered via a digital platform that integrates its Very-High-Resolution (VHR) Earth Observation satellites with other data sources, providing up-to-date visualisations and actionable insights for city management. Its first phase focuses on short-term priority needs of municipal services.

The platform has two main components:

  • City digital models: Using GEOSAT-2’s 40 cm resolution imagery, it generates detailed, accurate urban representations for GIS operations. Subscription options offer variable coverage and update frequency, with potential intra-daily updates via the full Atlantic constellation. 3D models are available by combining satellite imagery with in-situ data through stereo-pair techniques.
  • Tailored management insights: Delivered as a value-added service, these AI/ML-powered applications cover land use classification, change detection, pathway optimisation for large vehicles and non-standard scenarios, and green space health assessment.

Building on existing VHR imagery and processing methods, the platform will evolve to integrate new satellite data, automate visualisation, use satellite and in-situ data, and adapt models to municipal-specific needs, including customised land use categorisation and quantification of green areas. It addresses critical problems: inconsistent or outdated information, lack of easily accessible tailored insights, inability to model scenarios, and scattered data from multiple sources.

Added Value

The GEOSAT platform is a single, integrated solution combining multiple data sources with advanced tools to analyse information and generate actionable insights. It addresses customer-specific needs through a new product focused on visualisation and insight generation, with short-term capabilities like land use classification, change detection, pathway optimisation, and green space assessment, and medium- to long-term developments such as 3D model generation, scenario simulation, and pollution monitoring.

The platform directly tackles the challenges of urban data analytics, the requirements for connected urban data, and the operational needs of local governance. Unlike traditional GIS tools or static mapping services, it provides recurrent monitoring of urban environments, enabling real-time detection of changes in land use, infrastructure, and green spaces.

Its flexibility and scalability through subscription-based coverage and update frequencies, including potential intra-daily updates, ensure municipalities have the most current and precise data. The integration of 3D city models and fusion of satellite with in-situ data allows simulation of interventions, optimised routing for large vehicles, and assessment of non-standard scenarios.

By centralising fragmented data and delivering tailored, automated insights, the platform reduces operational workload, accelerates decision-making, and supports sustainable urban management, offering a level of precision, timeliness, and operational relevance unmatched by existing solutions.

Current Status

The activity has just kicked-off.

LandOS

Objectives of the Product

Portuguese municipalities manage complex territorial challenges, including urban sprawl, wildfire risk, invasive species, water quality and regulatory compliance, but lack the technical capacity to access and interpret the Earth observation data that could inform their decisions. Most of Portugal’s 308 municipalities have no GIS specialists as staff.

LandOS addresses this gap with an AI-powered conversational platform. Municipal staff ask questions in natural Portuguese, such as “How has urban sprawl changed in our municipality over the last five years?”, and receive evidence-based answers in the form of maps, charts, and downloadable PDF reports. The platform combines Sentinel-2 satellite imagery with Portuguese national datasets (CAOP administrative boundaries, COS land cover, REN/RAN ecological reserves) through an agentic AI system that autonomously selects and executes the appropriate geospatial analysis.

The project develops, deploys, and validates this platform in a 12-month product development cycle, piloting with three Portuguese municipalities (Fundão, Odemira, Mértola) and targeting TRL 7 readiness for commercial launch.

Customers and their Needs

Target users: Municipal staff in Portuguese municipalities, specifically:

  • Urban planning technicians (‘técnicos de urbanismo’): need to monitor urban expansion, validate development permits against REN/RAN protected zones, and support PDM (municipal master plan) revisions with evidence
  • Environmental officers (‘técnicos de ambiente’): need to track water quality in reservoirs, monitor invasive species spread, and report on environmental indicators
  • Civil protection coordinators: need seasonal fire risk assessments cross-referenced with ICNF wildfire records to prioritise prevention actions
  • Municipal managers (‘gestores’): need high-level dashboards and reports for decision-making and compliance reporting

Key challenges: These users are domain experts but not GIS specialists. They cannot use traditional remote sensing tools. They need information delivered in Portuguese, in formats they already understand (reports, maps, simple dashboards), without requiring training in satellite data interpretation.

Targeted customer/users countries

Country: Portugal (pilot municipalities: Fundão, Odemira, Mértola). Expansion target: Southern European municipalities

Product description

LandOS provides a cloud-based Software-as-a-Service (SaaS) platform accessible through a web browser. The system has three main layers:

  1. Conversational Interface – Municipal staff type questions in natural Portuguese. The system understands municipal terminology and context (e.g., parish names, regulatory references, land-use categories).
  2. Agentic AI Engine – An AI based orchestration system automatically determines which data sources and analytical tools are needed, retrieves the relevant data, executes the analysis, and synthesises the results into a clear Portuguese-language response with supporting maps and charts.
  3. Analytics & Visualisation – Five core analytics modules are delivered as part of the minimum viable product: urban sprawl detection (COS change analysis), water quality monitoring (Sentinel-2 spectral indices), fire risk assessment (multi-factor: NDVI, topography, weather), invasive species detection (Random Forest ML on Sentinel-2), and regulatory compliance checking (REN/RAN spatial intersection). Results are displayed as interactive maps, charts, and branded PDF reports.

The platform is built on Project Zeno, a proven open-source geospatial AI framework, and runs on cloud infrastructure that ensures EU data residency and GDPR compliance.

Added Value

LandOS relies on Copernicus Sentinel-2 multispectral imagery as its primary Earth Observation data source, complemented by the European Digital Elevation Model (EU-DEM) from the Copernicus Land Monitoring Service.

Sentinel-2 enables four of the five core analytics: water quality monitoring through NDWI and chlorophyll-a spectral indices; fire risk assessment through NDVI vegetation health analysis; invasive species detection through spectral signature classification; and urban sprawl analysis through multi-temporal land cover comparison. Without satellite-derived data, these analyses would require costly and infrequent field surveys that most Portuguese municipalities cannot afford.

The space added value is the ability to provide consistent, repeatable, municipality-scale environmental monitoring at a frequency (every 5 days with Sentinel-2) and spatial coverage (all 308 Portuguese municipalities simultaneously) that no ground-based alternative can match. By combining this EO capability with AI-powered natural language access, LandOS removes the technical barrier that has historically prevented municipalities from benefiting from the Copernicus programme.

Current Status

The project kicked off in March 2026 following contract signature with ESA under the InCubed “EO for Municipalities” call. The Kick-Off Meeting took place on 24 March 2026.

The first development phase (Months 0–2) focused on building the foundations of the platform: the cloud environment that hosts the service, the secure sign-in system for municipal users, and the full Portuguese localisation of the user interface and AI agent.

Three pilot municipalities (Fundão, Odemira, Mértola) are confirmed for the activity through signed Letters of Support. The first on-site visit took place in Odemira on 7 May 2026, and included a programme of interviews with municipal staff across urban planning, environment, civil protection and management. On-site visits to Fundão and Mértola are scheduled in the coming months. LandOS also attended the Portugal Smart Cities Summit in Lisbon (12–14 May 2026).

The Requirements Review, the first contractual milestone, took place on 9 June 2026.

ClearBNG

Objectives of the Product

ClearBNG is a scalable, AI-driven Earth observation (EO) insights solution designed to address the complex challenges of Biodiversity Net Gain (BNG) compliance and long-term monitoring. Delivered by Aspia Space, ClearBNG integrates satellite imagery with ground-truth inputs with advanced analytics to provide a trusted, automated, and regulatory-aligned biodiversity assessment tool. At its core, the solution leverages EarthPT, Aspia’s multimodal spatio-temporal foundation model for geospatial data. This foundation ensures that biodiversity insights are scientifically validated, scalable, and seamlessly integrated into existing planning and environmental workflows.

ClearBNG delivers trusted, high-resolution habitat classification and Biodiversity Net Change metrics, derived from detailed, map-based outputs tracking habitat classification, condition and change over time. It is designed to be aligned with Defra’s Biodiversity Metric 4.0, Natural England frameworks, and UKHab classifications for audit-ready reporting. It supports local planning authorities (LPAs) in assessing biodiversity uplift feasibility, developer compliance and enforcement.

ClearBNG established a replicable model for:

  • Automating biodiversity net gain monitoring across LPAs, reducing costs and administrative burdens.
  • Enhancing spatial planning and policy enforcement through high-resolution EO insights.
  • Increasing confidence in EO-derived insights, allowing for a measured transition away from expensive, manual survey reliance.

By ensuring seamless adoption by LPAs, developers and environmental agencies, ClearBNG sets the benchmark for scalable, EO-powered biodiversity monitoring.

Customers and their Needs

Initially, the target customers will be local and national government organisations (e.g. county councils, LPAs, agencies), as well as enterprise-level institutes with ESG and/or stewardship responsibilities (e.g. large food producers, agri-tech companies, real estate developers) in the UK, scaling to other geographies in due course.

Targeted customer/users countries

UK, scaling to Europe and beyond.

Product description

ClearBNG is a data product that informs users of past and current habitat makeup and condition of a given area of interest. ClearBNG is derived from satellite data and exploits two state-of-the-art and proprietary GenAI technologies: ClearSky, an algorithm that predicts cloud-free multi-spectral optical imagery from Sentinel-1, and EarthPT, a geospatial/temporal foundation model for EO data. EarthPT is fine-tuned to dynamically predict habitat type aligned with the UKHab hierarchical classification system. It significantly reduces the burden of biodiversity monitoring and reporting by providing a consistent, validated, remotely-sensed approach.

ClearBNG outputs are designed with interoperability in mind in order to maximise uptake and utility. From GIS-ready data products such as high-frequency habitat zonal polygons, to summative reporting and interactive dashboards. ClearBNG quantifies, for any area of interest:

  • Habitat demographics over a multi-year baseline and on-going monitoring.
  • Statistical quantification of habitat changes and direction relative to baseline, identifying significant loss or gain in biodiversity.
  • Indication of relative habitat ‘condition’ where appropriate.

ClearBNG enables organisations to meet compliance with BNG legislation, monitor local nature recovery strategies, and deliver actionable biodiversity insights to guide planning and policy. It offers high-quality analytics as a service and meets customer needs for scalable, validated tools for environmental monitoring.

Added Value

ClearBNG differs from existing offerings in its use of innovative, proprietary GenAI:

ClearSky: Aspia’s proprietary technology eliminates the challenges of cloud cover that affect 70% of optical EO imagery. By integrating Sentinel-1 Synthetic Aperture Radar (SAR) data with AI, ClearSky produces consistent, cloud-free multi-spectral optical images, ensuring uninterrupted data availability. In commercial use for over four years, ClearSky has supported more than 15 million individual image downloads in the UK. ClearSky is the bedrock technology that allows ClearBNG to deliver regular, reliable and consistent remote sensing monitoring of the ground.

EarthPT: As the world’s first GPT-based Large Observation Model (LOM), EarthPT leverages cutting-edge AI to translate complex, multimodal, and temporal geospatial data into actionable insights. Scaling to more than two billion parameters, it is already delivering answers to complex challenges. Recently, it achieved >98% performance (AUC-ROC) in dynamic tree classification compared to the National Forest Inventory, even distinguishing individual large trees. This classification capability is central to ClearBNG’s habitat classification requirements at the heart of biodiversity tracking.

For Biodiversity Net Gain (BNG) monitoring, Aspia combines AI, cloud-free EO imagery, and multi-source data to deliver trusted, scalable, and cost-effective biodiversity assessments. This innovation supports compliance with the UK’s mandatory 10% BNG legislation.

A key additional outcome for this project is its application to support the monitoring and stewardship of forestry and woodlands. Predicting tree types, density and change on a national scale, significantly reducing effort in manual inventory surveys.

Current Status

Project achievement to date:

  • Engagement with key stakeholders to identify pain points, assemble subject matter expert ground truth/validation data across 9 key biodiversity sites across Cornwall.
  • Workshop with David Langton and Prof. Duccio Rocchini to establish a credible technical approach and identification of challenges/risks, as well as appropriate ecological informatic methodology.
  • Assembly of UK-wide training data samples, including UKHab-aligned Priority Habitats Inventory, National Forest Inventory and Trees Outside Woodland datasets.
  • Preliminary training of fine-tuned EarthPT habitat classification model and first analysis of results.

SKYWARD VIGIL

Objectives of the Product

Government agencies, civil protection authorities, security forces, and critical-infrastructure operators face increasing difficulty in monitoring and managing vast and complex territories.

In Portugal, for example, ensuring continuous surveillance of the 3.8 million km² Extended Exclusive Economic Zone, detecting illegal activities, assessing environmental risks, and responding to disasters such as recurrent wildfires remain major challenges.

Current solutions like manned aircraft, satellites, and ground-based systems suffer from high operational costs, limited persistence, and insufficient real-time responsiveness. These gaps lead to delayed detection, reduced situational awareness, and suboptimal decision-making during emergencies.

The proposed product addresses these limitations through a novel unmanned, hybrid high-altitude aircraft capable of long-endurance, energy-autonomous operation.

Combining lighter-than-air buoyancy with high-altitude aerodynamic performance and a multifunctional solar-hydrogen energy system, the platform can remain on station for multiple months while carrying communication, sensing, and surveillance payloads.

By deploying a small network of such High-Altitude Pseudo-Satellites (HAPS), users gain persistent, wide-area monitoring capability that supports maritime surveillance, environmental protection, disaster response, border security, and digital-connectivity expansion. This product provides a cost-effective, flexible, and rapidly deployable alternative to satellites and manned aviation, enabling continuous, real-time awareness over large regions and enhancing national and civilian resilience.

Customers and their Needs

The Skyward VIGIL platform targets a broad range of customers across four main sectors: public safety agencies, telecommunications operators, Earth observation and scientific research institutions, and government and defence clients.

  • Public safety agencies require continuous monitoring capabilities for search and rescue, wildfire detection, flood management, crowd control and traffic regulation.
  • Telecommunications operators need persistent coverage solutions for rural and remote areas, disaster recovery communications and remote sea and ocean coverage.
  • Research and scientific institutions require Earth observation capabilities including precision agriculture, deforestation mapping, air quality monitoring, urban heat island measurements and meteorological data recording.
  • Government and defence clients demand reliable surveillance for maritime and border monitoring, offshore platform oversight, critical site monitoring and robust communication nodes for high-speed datalinks and combat net radios.
Targeted customer/users countries

The primary target market is Portugal, with expansion potential to European and international customers.

Product description

The product under development is a next-generation unmanned HAPS designed to provide persistent, wide-area monitoring and communication services. It is a hybrid aircraft combining lighter-than-air buoyancy at low altitude with efficient fixed-wing aerodynamics in the stratosphere, enabling stable, long-endurance operation.

The system’s innovative aspects include:

  • Hybrid aerodynamic-aerostatic design with a multifunctional structural integration of solar and storage elements, and a fully renewable, closed-loop energy system.
  • Payload agnosticism designed to accommodate a wide range of sensors and communication payloads for Earth observation, telecom relay, and surveillance applications across government, environmental, and commercial users.
  • Rapid deployment from existing airfields, with minimal operational footprint.

Customers, such as environmental agencies, maritime authorities, emergency services, and communication providers, interact via a ground mission control system to task the aircraft, manage payloads, receive real-time data, and adjust coverage areas remotely, delivering a flexible and cost-effective alternative to traditional aircraft or satellites.

Added Value

The Skyward VIGIL platform differentiates itself through its hybrid configuration, combining buoyancy and aerodynamic lift. Existing competitors rely exclusively on either aerostatic platforms, limited in maneuverability and vulnerable to wind disturbances, or aerodynamic platforms, which require continuous propulsive power and are constrained by structural weight.

By leveraging the advantages of both approaches, the hybrid configuration reduces the lift power required while providing maneuverability and stability, resulting in longer endurance, greater payload capacity and lower energy consumption than purely aerostatic or aerodynamic competitors.

Current Status

On-going activities focus on the Conceptual Design of the Skyward VIGIL hybrid HAPS. Customer consultation and supplier engagement are ongoing, with trade-off analyses on the hybrid buoyancy and aerodynamic lift configuration, energy budget and payload integration running in parallel.

This phase will produce the consolidated system architecture, allowing for the definition of the energy production and storage solution and the refinement of the platform’s operational and monitoring services.

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