Develop and market innovative multispectral data and global variable products.
Fully or partially commercialise the ground and user segment.
Position Aerospacelab competitively in the satellite data market.
Refine offerings to meet the growing industry demand for accuracy, reliability, and regulatory compliance.
Customers and their Needs
Environmental Agencies: Seek precise multispectral data for effective environmental monitoring and reporting.
Agricultural Enterprises: Demand high-quality data to enhance precision agriculture and optimise yield management.
Urban Planners: Rely on trustworthy spatial data to inform urban development, infrastructure planning, and policymaking decisions.
Targeted customer/users’ countries
Three primary user groups will benefit from MSCM data products:
1. European institutions
2. Private commercial companies, including:
Value-Added Services providers
Major commodity traders/producers and financial services firms
3. Additional Sentinel-2 users, including non-governmental organisations, academic institutions, and research centers.
Product description
In the latest development of Aerospacelab’s MSCM Phase 2, the company is set to offer an extensive range of multispectral imaging (MSI) data and global variables, tailored to accommodate the diverse needs of its clientele. Customers have the flexibility to purchase these data products either in full or in part, ensuring that they can select the options that best align with their specific requirements. This customisable approach enhances accessibility, making it easier for users to integrate MSI data into their projects effectively.
Added Value
Customisable Access: Customers have the option to choose between complete or limited access to MSI data and associated services.
Comprehensive Data: Integrating MSI data with global variables delivers critical insights for various applications.
Intuitive Design: The ground and user segment are crafted for straightforward access and ease of use with the data.
Enhanced Competitive Edge: These features enable Aerospacelab to maintain its competitive stance in the satellite data market.
Current Status
The MSCM Phase 2, which commenced in 2022, is projected to be completed by September 2025, with the Test Readiness Review anticipated to occur by May 2025.
Deepview is an actionable agricultural commodity supply chain risk service, based on Copernicus and contextual supply chain data) that assist organizations to
Monitor progress towards commitments such as zero deforestation, and make its supply chain more transparent;
Act proactive on risk in the supply chain to reduce PR and financial risks;
Make better investment and sourcing decisions, towards more sustainable supply chains.
The service developed as part of the activity shows how deforestation risk propagates through the whole value chain, including mapping the complex relationships between producers, traders and consumer good manufacturers. With a risk analysis of this granularity companies can pinpoint the high risk organizations in their supply chain and are able to engage more targeted, while also being more transparent on what is happening and where.
The solution can be easily scaled to other commodities. The main focus of Deepview is on deforestation risk in the palm oil supply chain of the larger traders and consumer goods companies.
Deepview dashboard showing deforestation free status of a supply chain (visualizing dummy data)
Customers and their Needs
The targeted users (Consumer Goods Companies and large traders) have the following needs:
Need for more timely updates to improve engagement, and mitigate potential PR risks
Need for solutions at scale to assess their entire supply chain
Need for solutions which are easy-to-use and easy-to-understand
Need for prioritized information to better plan investments and prevent information overload
Need for transparent commodity supply chains to make better investment decisions and improve engagement
Targeted customer/users countries
Deepview is a service that covers global agricultural commodity supply chains.
During the activity the main focus is on deforestation risk in the palm oil supply chain of the larger traders and consumer goods companies. Demonstrations are carried out for the palm oil supply chain in Indonesia and Malaysia.
Deepview is now in the commercial phase with global coverage for the palm oil supply chain and will be expanded to a large number of commodities like cocoa and soy.
The coverage of Deepview as of January 2022 is shown below.
Product description
Satelligence developed and is continuously improving (as market needs are expanding and evolving) their Smart Forest and Commodity Analytics service to address sustainability related challenges of organisations in commodity value chains:
Where is deforestation happening in my area right now?
Which of my investment & sourcing areas are at risk?
What is the performance of my plantations?
With Deepview Satelligence wants to add more value than only focusingon the risk and performance of the production areas of soft commodities: we model and map how that risk and performance propagates through and affects soft commodity value chains (typically producer, trader, consumer goods manufacturers, retailers).
To carry out a Supplier / Mill / Concession supply chain risk analysis for any organization active in the supply chain, based on approaches common in industry. To get there, we need three major parts:
Real time deforestation data (based on Copernicus data and science-based algorithms)
Supply chain linkage data model (geographic locations and attributes of supply chain entities like mills, traders, manufacturers, retail and their linkage; to be collected)
A risk propagation model that a) connects deforestation data to the supply chain model and b) calculates how that risk is propagated through the supply chain.
“With our database of concessions and farms, mills and refineries, and trade linkages (Traceability to Plantation (TTP), exports), we can profile supplier risk and supplier performance to the farm or aggregate level (group, district, cooperative) and tell you if and how a deforestation alert is linked to a supply chain.”
Added Value
Companies want to assess the risks in their supply chain and find out who is responsible. With a mill / supplier / concession risk analysis companies can pinpoint high risk organizations in their supply chain and are able to have more targeted engagements, while also being more transparent on what is happening and where.
Current Status
The following activities have been undertaken as part of the activity:
Outreach to target users
Service design (UI/UX)
Start with development web-application
Feedback sessions web-application with target users
Iterative approach to improve the web-application
Launch of Deepview mvp
Signing of first Deepview contract
Development of marketing materials
Investigating and start of implementation Deepview for cocoa, soy
Launch of Deepview commercial service.
The activity was successfully concluded on December of 2021.
Floods are one of the most devastating natural disasters, accounting for the highest insured and uninsured losses annually, as well as costing many lives. With climate change possibly intensifying the hydrological cycle, the frequency and magnitude of extreme hydro-meteorological events, and therefore the risk of floods, are projected to continue to increase. This will be of devastating consequences, as it would put a greater strain on humanitarian response efforts and future financial risk of the global (re)insurance market.
Earth Observation data-based solutions currently provide a more advanced alternative to traditional ground-based flood monitoring methods or computer models, namely the ability to cover wider areas, frequent revisit times, abundant open access data and long historic image archives. However, there are still important challenges left unaddressed that compromise the quality and reliability of the data, such as the persistent cloud cover during floods, latency issues and the problem of getting abundant high-definition images under less favorable weather conditions and at night.
FloodSENS overcomes these issues by developing a flood mapping application that is capable of integrating a wider range of EO datasets and derivative data from digital elevation models using Machine Learning. This novel application being developed to market, seeks to efficiently reconstruct flooded areas under partial cloud cover in satellite images, thus creating far more reliable flood risk assessments and flood mapping during emergencies
Customers and their Needs
FloodSENS is especially important for disaster response agencies at regional, national, and international level, who are keen to utilize the proliferation of open satellite data for flood mapping during emergencies. Additionally, in the insurance and re-insurance markets, stakeholders are interested in EO data to map the flood hazard of high-impact events and on a historical basis to understand risk exposure and the changing nature of it.
In the case of the flood disaster response markets, customers often have to deal with optical satellite imagery that is partly covered by clouds during flood events, where the data resolution is too low (>30 m pixel) )to allow for local scale flood analysis and they often lack resources to deal with complex EO image analysis. This inevitably compromises the humanitarian relief efforts as it leads to incomplete estimation of flood areas and thus misrepresentation of the real impact of the flood. The (re)insurance market struggles mainly with assessing the extent of flooding for high impact events, as well as understanding the potential flood risk exposure at a local-scale. This is due to many factors, including those referred to above, along with high costs in conducting on-site inspections, using incomplete EO data archives to build historical records and prediction models, and often lack of EO specialists.
Targeted customer/users countries
FloodSENS is targeting both the humanitarian and disaster relief organizations, as well as the global (re)insurance market, with the aim of having the application work in diverse environments worldwide.
At an initial stage, as representatives of their respective customer markets, FloodSENS will have as partners and testing customers the United Nations World Food Programme (UN WFP), the National Disasters Management Institute of Mozambique (INGC) and Willis Re (re)insurance broker through Willis Towers Watson (WTW).
Product description
FloodSENS consists of a fully automated Machine Learning-based flood mapping algorithm, whose main characteristics include:
Ability to map flooding in many different biomes and therefore achieve global transferability easier
Ability to reconstruct flooding below clouds in optical satellite images of floods
The schematic below, illustrates the FloodSENS algorithm structure, the overall architecture, and the key submodules.
The added value of FloodSENS centers around two major innovations:
The ability to reconstruct flood area under clouds in optical satellite images. This allows to valorize flood images with high cloud cover and can identify potentially missing flood areas. It also builds a more accurate and reliable historical record of flooded areas.
Add custom map features via agile development with specific customers/users. This can include flood depth mapping capability, explicit map uncertainty representation, customer-led map creation and visualization., so map data are also much easier to interpret by non-experts.
In fact, for both Europe and the wider world, future Earth Observation-based and ML-powered apps would add considerable value to the existing products of the free Copernicus Emergency Management Service (EMS) and beyond. RSS-Hydro’s FloodSENS will place itself at the intersection of these two fields (EO technologies and AI/ML application tools) being at the forefront of future EO-enabled innovative solutions, to make a difference in allowing a much more effective disaster response.
Added Value
The added value of FloodSENS centers around two major innovations:
The ability to reconstruct flood area under clouds in optical satellite images. This allows to valorize flood images with high cloud cover and can identify potentially missing flood areas. It also builds a more accurate and reliable historical record of flooded areas.
Add custom map features via agile development with specific customers/users. This can include flood depth mapping capability, explicit map uncertainty representation, customer-led map creation and visualization., so map data are also much easier to interpret by non-experts.
In fact, for both Europe and the wider world, future Earth Observation-based and ML-powered apps would add considerable value to the existing products of the free Copernicus Emergency Management Service (EMS) and beyond. RSS-Hydro’s FloodSENS will place itself at the intersection of these two fields (EO technologies and AI/ML application tools) being at the forefront of future EO-enabled innovative solutions, to make a difference in allowing a much more effective disaster response.
The first half of 2019 was a devastating period for many countries in southeast Africa. After Cyclone Idai at the start of the year destroyed many places, particularly the port city of Beira, Cyclone Kenneth ravaged northern Mozambique. Entire villages were destroyed and almost one million people were at risk in the area. This partial cloud-free subset of a Sentinel-2 image of May 3 2019 shows large areas under water in Pemba, regional capital of Cabo Delgado state, which experienced more than 2 m of rain and flooding. FloodSENS will render more optical imagery like this one usable during floods by reconstructing flooded areas under cloudy skies..
Notable Outcomes
The ML model is based on the well-known U-Net architecture and uses Sentinel-2 (S-2) flood images and derivative layers from digital elevation models relating to topography and waterflow to map flooding even below partial cloud cover. The algorithm further employs a squeeze and excitation network to extract information about the importance of the different input layers. During the project, FloodSENS was trained on a large number expertly labelled S-2 flood images across different biomes, events and locations to ensure acceptable transferability, which is to become an important part of RSS-Hydro’s IPR of FloodSENS. Internal application testing and validation shows, unexpectedly, varying degrees of performance and accuracy. Overall, on average, FloodSENS performs at least as well as any robust and calibrated traditional band ratio index (>90% correct prediction), and in some cases outperforms such, and even maps below low cloud cover and correctly includes flood impact areas from dried out areas by following debris lines.
For the humanitarian stakeholder, it is clear that FloodSENS is an application they would start using since mapping consistently across different biomes given good transferability of the model will lower the number of missed flooded areas for them. For the financial risk industry, FloodSENS is appealing since the ML allows different trained models to be available for specific geographic areas where they are interested in, such as for example US, Europe, Australia and India because of a high number of insured assets in those areas. During the project, the FloodSENS app has been successfully deployed and demonstrated on the WASDI cloud processing platforms. Now that the R&D part is closed, both stakeholder segments will start the customer-led external validation on WASDI where they will test run FloodSENS and provide valuable feedback. RSS-Hydro will also organize a customer-oriented workshop with WTW to gather additional feedback to complete the best possible license subscription model.
Post-activity new steps/highlights
RSS-Hydro continues to improve FloodSENS and has some ongoing amazing new collaborations, namely:
the award of a Google Gift to help Google validate flooded areas around the world.
we used various geospatial datasets with NVIDIA’s scene-generation platform Omniverse to build realistic 3D visualizations of disaster impact that help increase resilience of vulnerable communities. #thisisnotavideogame
InfoSequia-4CAST aims to meet the needs of water management authorities and humanitarian-aid agencies by providing actionable, seasonal-scale outlooks of drought-induced crop yield and water supply failures, with the required level of accuracy, reliability, and location-specificity.
Water and food security are at risk in many places around the world, at present and even more so in the future, with significant economic and humanitarian consequences. Risk managers and decision-makers (e.g. water management authorities and humanitarian-aid agencies) can more effectively prevent harmful drought impacts if timely information is available on how the system is affected, and the probability of a system failure.
InfoSequia-4CAST combines historical and up-to-date observations of satellite-based meteorological and agricultural drought indices with climate variability indices, to generate seasonal outlooks of water supply and crop yield failure alerts. These impact-based indicators are computed using a simple, robust and easily understandable statistical forecasting-modelling framework. By making use of multi-sensor, state-of-the art satellite data fully integrated with predictive models, InfoSequia-4CAST provides locally-specific, 3-6 month outlooks and warnings of crop yield and water supply failures to end users through a simple, intuitive user interface.
The product is tailored to the needs of water managers who are looking to alleviate and mitigate impacts of forthcoming drought periods by taking strategic water management decisions, and humanitarian NGOs aiming to trigger ex-ante cash transfers with policyholders and farmer communities.
Customers and their Needs
InfoSequia-4CAST focuses on the two aforementioned customer groups.
Water managers currently face too great a delay in detection of water demand-supply imbalances to trigger strategic actions. Humanitarian-aid agencies and NGOs lack actionable information on crop yield failures at the agricultural district level, which impedes them in determining the cost-effectiveness of cash transfer programmes and activating ex-ante payments. Both customer groups deal with a very weak local specificity and reliability of seasonal climate outlooks included in current Drought Early Warning Systems, which make use of complex dynamical forecasting models. Existing satellite-based drought monitoring systems, on the other hand, are location-specific but do not provide any information on expected conditions. Forecasts on the seasonal scale cannot be provided with sufficient accuracy by current numerical weather models.
Targeted customer/users countries
Global
Product description
The proposed development is incorporated into an existing toolbox for providing Drought and Early Warning Systems, called InfoSequia.
InfoSequia is a modular and flexible toolbox for the operational assessment of drought patterns and drought severity. Prior to the activity, the InfoSequia toolbox provided a comprehensive picture of historical and current drought status and impacts through its InfoSequia-MONITOR module, based mainly on Earth Observation data. The additional module InfoSequia-4CAST, is a major extension of current InfoSequia capabilities, responding to needs that have been identified in several previous applications.
InfoSequia-4CAST provides the user with timely, future outlooks of drought impacts on crop yield and water supply. These forecasts are provided on the seasonal scale (i.e. 3-6 months ahead). Seasonal outlooks are computed by a novel state-of-the-art Machine Learning technique. This technique has already been tested for applications related to crop production forecasting and agricultural drought risk financing.
The Fast-and-Frugal-Tree (FFT) algorithm uses predictor datasets (a range of climate variability indices alongside other climatic and vegetative indices) to generate FFTs predicting a binary outcome such as crop yields or water supply-demand balance above or below a given threshold (i.e. failure: yes/no). The activity includes collaboration with stakeholders in Spain, Colombia and Mozambique, in order to establish user requirements, inform system design, and achieve pilot implementation of the system in the second project year. Generic machine learning procedures for training the required FFTs are developed, and configured for these pilot areas. An intuitive user interface is developed for disseminating the output information to the end users. In addition to development of the forecasting functionality, InfoSequia-MONITOR is upgraded by integrating state-of-the art ESA satellite data and creating multi-sensor blended drought indices.
Added Value
Key areas of innovation concern the integration of the following features:
Seasonal outlooks of water supply and crop yield failure-alerts (impact-based forecasting), updated monthly
Contextualised, actionable indicators based on the combination of robust multi-sensor drought indices and large patterns of climate variability
Higher forecasting accuracy and simplicity using simple and intuitive decision trees
User-friendly and interactive web mapping interface and data platform
Long-term solution with regular maintenance, technical support and upgrades.
Current Status
The Activity started in March 2021. A fully operational version of the InfoSequia system was developed and piloted in close collaboration with end users. Validation activities have taken place on two locations, with active and frequent contributions from stakeholders. In Mozambique, in collaboration with the World Food Programme (WFP), monthly bulletins were sent to provide updates on the seasonal outlook regarding the probability of crop yield failures, in support of early response actions. In Spain, in partnership with the Segura River Basin Management Authority (CHS) and the Regional Board of Irrigators of Campo de Cartagena (CRCC), seasonal forecasts of water supply failures were provided, focusing on expected water levels in the most important reservoirs. A new and flexible InfoSequia front-end module was developed to disseminate system outputs to a wide range of end users. The Final Review meeting of the InCubed Activity took place on March 7, 2024. Upon closure of the InCubed Activity, FutureWater is focusing on implementing the InfoSequia commercial rollout strategy with key stakeholders across the globe.
The primary aim of the service is to expedite and simplify the project approval process for developers seeking certification. By incorporating forest carbon modelling and leveraging Earth observation (EO) data, the service seeks to accelerate project operationalisation, reducing the time required for approval. This integration of advanced technologies not only enhances efficiency but also brings transparency to carbon markets and attracts project financiers. The ForestHQ platform serves as the vehicle for delivering this service, offering additional features such as Measurement, Reporting, and Verification (MRV) tools and forest change monitoring. The overarching goal is to provide a comprehensive solution that not only streamlines project approval but also contributes to the broader goals of sustainable forest management and carbon emissions reduction, ultimately fostering a more accessible and transparent carbon market. Through these innovations, the service aims to catalyse positive environmental impact and support the broader ecosystem of stakeholders involved in forest carbon projects.
Customers and their Needs
The users are in greenhouse gas (GHG) mitigation projects for AFOLU, specifically forestry carbon projects for the voluntary carbon market. This includes project financiers, project developers, landowners including communities, land managers, auditing bodies, certifying agencies, NGOs and policymakers. These projects are located globally, with particular emphasis in South America and Africa, with large-scale deforestation and degraded land. The registration of forestry carbon projects is complicated, with specific criteria thereby causing a long delay in getting them vetted and available to the carbon markets. Projects with low levels of data and trust result in lower market prices for the credits. Small landowners and communities find the process daunting and have a desire to be more involved and understand the methodologies with the help of technology.
The main SATFORCERT user needs are:
Calculate carbon stocks from EO and ground truth data
Carbon stock calculations compatible with carbon standards
Spatial information:
Up-to-date Sentinel-2 images and historical images
Forest Canopy change detection
Ability to utilise very high-resolution EO data (<0.6m)
Compatibility with carbon registry methodologies
Easy-to-understand calculations with mapping analytics
Mobile application for field data collection
Secure sharing of data between different stakeholders
Targeted customer/users’ countries
Worldwide
Product description
The product is a comprehensive technology platform designed to simplify and enhance the carbon calculation process for users engaged in forest projects. Offering a user-friendly interface, the platform facilitates precise carbon quantity calculations, complemented by detailed reporting through maps, tables, and documentation. Integration of field plot data and project boundaries is seamless, incorporating a quality control process to ensure accurate carbon stock estimations. Emphasising the importance of productive forest areas for carbon capture, the solution employs mapping techniques to exclude non-productive zones from calculations. The platform supports the integration of diverse satellite EO data, varying in resolution and enabling users to map and monitor forest regions effectively. Robust capabilities for EO data management and storage are integral to the platform’s functionality. Recognising the need for specific documentation required by registries like Verra and Gold Standard, the platform digitises templates and forms, offering a standardised service for information input. Ultimately, the product aims to empower users with a comprehensive, digitised ecosystem that optimises forest project management, carbon calculations, and compliance with industry standards.
Figure 1: SATFORCERT Overall Architecture
Added Value
Various space assets, including a diverse set of EO datasets, play a crucial role in our solution. Low-resolution Sentinel-2 imagery proves valuable for monitoring and change detection across vast areas and at the forest stand level. On the other hand, higher-resolution imagery like Pleiades enhances precision in GHG calculations and enables detailed single-tree monitoring. The choice between high and low resolution involves a tradeoff between data cost and the accuracy of carbon calculation estimations. Opting for higher-resolution data not only ensures more precise GHG estimations but also increases the value of traded credits, effectively offsetting the EO data cost. An innovative aspect of our solution lies in empowering users to assess different methodologies and choose a solution based on either cost-effectiveness or value maximisation. This flexibility allows users to tailor their approach to specific project requirements, emphasising a balanced consideration of cost and value in the decision-making process.
Current Status
The SATFORCERT activity successfully concluded in April 2024. This innovative solution integrates advanced digital tools for forest management, carbon quantification, and certification processes.
By utilising technologies such as GIS, remote sensing acquisition and analysis, and mobile data collection, the platform streamlines workflows and enhances data accuracy. These developments have been seamlessly integrated into the existing ForestHQ system, streamlining and enhancing forest data management for forest certification and carbon monitoring, reporting and verification (MRV).
Throughout its validation phase, the various features developed received an average approval rating of 77.4, surpassing the benchmark of 70. This indicates strong stakeholder satisfaction, particularly highlighted by the high scores in mapping, satellite image acquisition, and data analysis capabilities. These achievements mark significant progress in the initiative’s goal to enhance the transparency, efficiency, and sustainability of forest management and carbon certification processes.
AIX brings a new satellite as-a-service concept to the market and makes on-demand, in-orbit resources such as data and actionable information available to users.
Customers can exploit the desired custom set of components, configuring their own acquisition/processing workflow and their own system’s configuration. The system will:
Support AI-experienced users in testing their algorithms and techniques into a real EO case, with resources and payloads available on-demand.
Ease developers in on-board software implementation and re-use.
To summarise, AIX enables customer-driven capabilities such as need-focused data gathering which reduces costs and barriers to access space.
The main innovations of AIX are:
A technological framework composed of hardware, software and services. These provide a set of basic and advanced building blocks that AI applications can be built upon. In other words, a testbed for AI.
An infrastructure of on-demand space services composed of in-orbit and ground facilities, founded on Blockchain technologies.
A suite of tools supporting the commercial evolution of space and “NewSpace Economy”.
On-the-fly configurable services able to monitor areas and topics of interest, detect changes and raise alarms in response to specific data processing. This is supported in the framework by AI-based algorithms and tools.
Customers and their Needs
Space missions are rapidly evolving with the NewSpace approach, less access barriers, and huge data availability.
Customers are focused on their core business process and need the right information, at the right time, in the right place. AIX call this paradigm SpaceStream, where many steps of the EO value chain are shifted from ground to space to transform sensed data into actionable knowledge.
SpaceStream identifies a need for new operational concepts that can implement novel technologies and approaches to shorten development cycles at the mission design stage.
By making satellite assets available as a service, AIX offers customers on-board EO applications based on AI and Blockchain technologies. Customers provide their use cases and system validation by contributing to the system and service design.
Targeted customer/users countries
International
Product description
The AIX hybrid edge ecosystem includes:
An orbiting platform, based on D-Orbit’s ION Carrier, providing hosted payloads and ready-to-deploy CubeSats
A software framework infrastructure that implements services and provides an abstraction layer towards sensors and on-board resources
A catalogue of on-board resources including hardware and software components as well as complete CubeSats in standard configurations
An “app-store” catalogue of processing functions and application algorithms based on AI
On-board service configuration
CubeSats deployment on-demand
Support service to custom design needs
Ground Segment support services for operations.
These services will help users find innovative approaches in planning, tasking, data processing and communications.
Added Value
AIX provides a set of deeply configurable services that ensure it stands out from market competitors. These configurable services allow users to choose from pay-per-use to full missions as-a-service, evaluate new approaches to space missions and validate novel concepts in the real environment. Flexibility and configurability also allow for in-orbit test iteration and the fine-tuning of applications.
The AIX suite of in-orbit services offer unique features. These include tools that support EO data processing on the ground and during in-orbit tests, enabling automated and collaborative AI applications on-board. These applications are possible thanks to smart contracts and a Blockchain security infrastructure.
To conclude, AIX services provide users with technical flexibility, cost scalability, timeliness and risk reduction.
Current Status
On-going activities focus on the detailed system definition. Customer consultation workshops are ongoing, with trade-off analyses on the technological drivers and constraints running in parallel. This phase will produce the consolidated design, allowing for the high-performance computing platform prototypal implementations. It will also allow for the software framework and on-board services refinement.
SAT4EOCE provides cost effective and high performance solutions for VHR (~50 cm) imaging in minisatellites, while meeting customer needs in terms of low budget, low risk and turn-key products. It provides key technologies for the DEIMOS SAT4EO VHR satellite programme, namely the Image Product Chain (IPC) and the AOCS subsystem.
The Image Product Chain (IPC), composed of the products of the EO imager, the Instrument Processor, and the cloud based Exploitation Platform, enables a Very High-Resolution (VHR) imaging system to be incorporated on-board a minisatellite, reducing significantly the cost of the mission, enabling constellations, and providing for reliable and timely VHR (~50 cm) data for customers worldwide.
The AOCS COTS subsystem product is an engineered HW and SW solution for minisatellites, with performances enabling ~50 cm ground resolution imaging, via very high pointing stability and accuracy, and high duty cycle imaging, via agility and autonomy, all designed and developed as a qualified low mass and low cost AOCS solution, in line with market needs.
Customers and their Needs
The targeted Customers are:
Privately owned companies, deploying small satellite missions, to provide commercial services from EO applications. These may be new start-up operators, entering the EO market upstream, or existing operators that are moving to VHR imaging that is now becoming more demanded from EO product users downstream. The user needs for such IPC and AOCS products are those of high performance, and fully integrated and qualified solutions, that meet requirements in terms of cost, mass, risk and reliability.
Institutions or governments of countries with small satellites initiatives aiming at having their own EO system. Especially those in countries with minor space capabilities or limited capabilities for Earth observation and remote sensing. In such cases, the IPC and AOCS can be employed as individual products in their satellites, or as part of the overall sat4EO satellite sale.
SAT4EOCE includes such customers in the activities and product developments cycle, through both the inclusion of external small satellites developers, and through the exploitation of DEIMOS’ and Surrey Satellite Technology Ltd established commercial networks. Moreover, DEIMOS and Surrey Satellite Technology Ltd serve themselves as customers for these products, through their own satellite programmes, such as DEIMOS’ SAT4EO programme.
Targeted customer/users countries
The customers for the SAT4EO VHR satellite and SAT4EOCE products are worldwide.
Product description
The Image Product Chain (IPC) is composed of three products: the EO imager, the Instrument Processor and the cloud based Exploitation Platform. The main innovation of the IPC is that it is a fully integrated end-to-end system, providing access to VHR imagery and cloud based analytic solutions. It enables ~50 cm ground resolution VNIR imaging in the minisatellite class, with masses up to 300kg.
The VHR VNIR (Visible and Near-Infrared) imager is electro-optical imaging system providing a compact EO Imager compatible in terms of mass, volume and power consumption with the smallsat class.
The Instrument Processor, also available in a flight segment configuration, provides a customizable processing chain, qualified for the new EO imager and allows for an agile dissemination of the imagery products to the user.
The cloudbased Exploitation Platform provides a cloud-based imagery database and image analysis capabilities to the end user to optimally exploit all the data and information coming from sat4EO and other elements of the existing EO ecosystem.
The AOCS Product is an innovative AOCS COTS subsystem solution for the smallsat class.with performances enabling ~50 cm ground resolution imaging, very high pointing stability, accuracy, agility, autonomy and high duty cycle imaging.
Added Value
The IPC solution provides ~50 cm ground resolution VHR imaging in the minisatellite class of satellites,. The new payload being developed by Surrey Satellite Technology Ltd brings on advanced manufacturing techniques and a new approach required to address the price and schedule targets for SAT4EO missions. The payload boosts a state of the art multi band on chip TDI sensor provided by Teledyne ev2. Thus it enables (~50 cm) optical VHR imaging to new customers, new satellite mass ranges, and for large constellations. Moreover, while there are a number of commercial products offering solutions for EO imagers or exploitation platforms, the offering of an end-to-end Image Product Chain (IPC) for VHR EO products provides a complete product solution.
The AOCS product is an AOCS solution for VHR (~50 cm) satellites and compatible in terms of mass, volume and power consumption with the smallsat class. It is offered and marketed commercially as a COTS product, which is an innovative commercial approach towards customers, being especially suited to those in the new space market or with limited subsystem experience for high performing AOCS solutions. In addition, it is available in multiple COTS configurations and versions, to support different customer needs.
Current status
The activity commenced in January of 2020. The project is currently in a design and commercial consolidation phase. This importantly includes the incorporation of the needs and perspectives of the VHR EO Customers and End Users.
The activity will pass a requirements review milestone in Q2, after which the design and development activities commence in full. The new design, processes and techniques required to address the changing VHR EO market demands will see the first payload of the IPC ready for test early 2021, and the AOCS qualified in late 2021.
These products respond to the main needs coming from SAR antenna manufacturers, which must procure Itar free RF components for space applications, to serve the forthcoming EO market, oriented to mini and micro-satellites clusters, which is highly demanding in terms of compactness and hardware miniaturization. Same technical and logistic needs come from AESA Radars manufacturers, for both airborne and ground applications.
Customers and their Needs
The main targeted customers are Earth Observation Satellites, specifically the SAR antennae, 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), but also from United States.
Product description
The SCFEs, at X- and Ku-band, are Itar free, Highly Integrated MMICs on GaN 0.15 µm technology, which replaces three separate chips HPA, LNA and Switch in a TR module for SAR antenna and AESA Radars.
The technical specifications are studied to target the system requirements for next generations of both satellite SAR and AESA radar, as provided by the main targeted customers and users.
For the X-band, the operating frequency range [8.5 – 11] GHz covers, among others, the bandwidth required by both the 3rd generation of Cosmo Sky-Med front-ends and the new concept of AESA radars under development by one of the most important European manufacturers.
The output power level and the efficiency, 15 W and 35% respectively, have been dimensioned on the basis of the new antenna architectures and, so, of the ratio between the number of modules combined on it and the total power required for the targeted resolution. The elimination of the bulky circulator at the antenna allows to get an overall Noise Figure at the level of the GaAs based modules, lower than 3 dB.
The Ku-band, covering the range [13 – 16] GHz, addresses a new concept of SAR antenna for earth observation, which both the Italian and other European Space Agencies are looking at with increasing interest thanks, also, to the forthcoming class of mini and micro-satellites. The power level, of about 7.5 W, is dimensioned to obtain the same SNR of the X-band satellites with an average antenna power of about the 67% lower. The PAE higher than 35% is confirmed as the main priority at system level and the Noise Figure lower than 3.5 dB ensures the required receiver sensitivity.
Block diagrams of the new SCFE concept
Added Value
The proposed solution gives the opportunity to the system integrators and final users to exploit a new architecture concept at both TR modules and antenna level.
These innovative MMICs, based on the state of the art GaN European technology with very high power density and good noise characteristics, integrate in a small chip the Power Amplifier, exploited along the Tx section, and the Low Noise Amplifier, on the receiver path.
Furthermore, its robustness, both electrical and thermal, makes possible the replacement of the two main cumbersome and bulky components, the ferrite circulator and the PIN limiter. Indeed, the SCFE embeds also a Power Switch (SPDT), capable to isolate the transmitting and the receiving paths, respectively, during the Tx or Rx period. This fulfils, together with the more robust LNA, the robustness’ requirements of the receiving chain.
Figure below compares the block diagrams of a classical front-end with the new SCFE concept.
Thanks to this new concept a classical TR module, e.g. those boarded on SAR antenna of COSMO-SkyMed, may reduce its footprint of at least 50%. This leads to a mass reduction of about 20gr per module, with a huge benefit at antenna level, which, in the 2nd generation, embeds 1280 T/R modules for each polarimetric channel. The SAR antenna will reduce its weight of 51 Kg.
Current status
In February 2023, the project was successfully concluded with the final review. The main objectives, to develop two Single Chip Front End MMICs, in X- and Ku-band to be exploited as RF front-end in the TR modules boarded either on future generation of SAR antennae for EO satellites or on AESA radars, have been achieved. In detail: The X-band SCFE is characterized, in the frequency band [8.5 – 11.5] GHz, by an output power of about 20 W and a PAE higher than 35%, during the transmit mode, and by a Noise Figure lower than 3 dB and a linear gain of 25 dB, in receiving cycle. The Ku-band one, in the band [13 – 16] GHz, provides a transmitting power of about 10 W with 30% of PAE, while the Rx chain is characterized by a NF lower than 2.8 dB and 24 dB of gain. These results confirmed that these products target the actual needs of the system integrators to develop a new generation of SAR antennae for Earth Observation and highly integrated AESA radar, based on a drastic simplification of the architecture and, so, of mass and costs, as well as an improving of the overall performances.
High resolution satellite data has enormous potential, but is expensive and not widely available. With limited budget and resources one is often left with data that suffers from haze, distorted colours, and sub-optimal resolution. These problems severely limit the usability of the data.
in1, utilising Artificial Data Enhancement (ADE), provides a solution. Based on state-of-the art software technology, the in1 toolkit can be used to enhance low value satellite data at low costs. Due to the increased resolution, the enhanced data has improved usability and unlocks exciting new use cases for existing datasets and satellite programmes. For example, users will be able to perform more accurate data analyses.
The in1 tools are offered as part of an application programming interface (API) that allows for real-time data enhancement. The API is compatible with different programming languages and will become available as on-premises software in the future, enabling unlimited use.
The image below illustrates the enhancement process from low- to high-resolution data using a few lines of coding for the API.
Customers and their Needs
in1 responds to the needs of three types of customers:
Data providers: with in1, satellite data providers and resellers are able to perform data enhancement on large volumes of data. This increases the impact on their user communities. Data providers can offer their customers enhanced data as a standard product or provide the choice to apply ADE algorithms to the provider’s raw data. For data providers, it is important for in1 to be able to process large volumes of data in a short amount of time.
Analytics providers: better data leads to better analytics. Analytics using big data is growing rapidly due to the availability of on-demand computing power, data storage and more satellite data. With in1 they have low-cost and real-time access to high-value data products in large quantities, improving the quality of the analytics. It is important for in1 to be easily integratable into the applications and data pipelines.
Value adding service providers: companies and organisations which focus on platform-enabled value-adding services for security, agriculture, energy or otherwise, can increase the value of the platform. Using in1 their customers can obtain more useful insights. The operations of these platforms and tools require high throughput without latency in any region of interest.
The image below illustrates the product positioning:
Targeted customer/users countries
The in1 solution targets customers worldwide with a focus on the European Union, Canada and the United States.
Product description
in1 provides a solution to problems with satellite data, such as haze, distorted colours, and low resolution. Based on state-of-the art software technology, the in1 toolkit can be used to improve the value of satellite data. The artificially enhanced data is carefully validated, ensuring that it has the characteristics of measurement data and that the physics governing the acquisition process is preserved.
The in1 tools are offered as part of an API that allows for real-time data enhancement. The in1 architecture makes use of the most recent scalable infrastructures that enable on-demand use. The API is compatible with different programming languages and the processing module will also be available as on-premises software in the future, enabling unlimited use.
Added Value
The benefits of enhanced data quality are:
Improved spatial resolution, which allows for new types of use cases that were previously impeded by limited resolution.
An increased number of usable products or data points due to the removal of haze. This means more frequent observations with an increase of up to 20%.
A competitive edge in the market due to the delivery of higher-value data as part of a platform or service.
Saved costs by using relatively cheap data that is brought to the level of higher-priced data (e.g. replace Spot 6-7 data by Sentinel-2 data).
Decreased processing costs due to not having to run computationally intensive and maintenance-heavy haze removal algorithms.
Current Status
The ADE activities have been concluded mid-July 2021. Our marketing and outreach efforts gave us unique insights into the EO market dynamics. As a result we have decided to look for partnerships with EO data providers such as satellite operators rather than offering in1 as a service to anyone working with EO data, because the time to market is too long for us. We developed relevant metrics and quality assurance parameters for the AI generated images to give users the confidence that the highly appreciated radiometric accuracy of the Sentinel-2 BRGI are preserved, whilst enhancing the pixel resolution of the original Sentinel-2 images by 4.
B and D represent enhancement and fidelity respectively of the in1 super-resolved Sentinel-2 visible bands in sub-figure A (left is bilinear upsampling, right is in1 super-resolved). The results are further corroborated by the spectral value histograms (C), whereby the distributions follow similar trends. The spectral histogram displays the frequency distribution of pixel values per spectral band for both the original Sentinel-2 product and the one processed with in1. Values have been normalized between 0 and 1 for comparison purposes. Slight variations are observed for urban landscapes due to the enhancement of the lines around objects.
The SpaceCloud Framework (SCFW) revolutionises satellite software development, converting purpose built custom space hardware into flexible and reusable compute nodes. This allows for simplified space missions, providing an all-inclusive solution for in-orbit data processing and on the ground management software. Like cloud computing on ground, it also allows orchestration of operations each node (or satellite) will perform, including approving execution of applications and upgrading the SCFW software.
The products value proposition includes:
Maximised satellite utilisation
Faster Earth Observation insights
Reduced maintenance costs
Enables low latency data products
Simplified satellite management
Reduced effort and time to launch
Secured data from intrusion
Customers and their Needs
Payload manufacturers and satellite manufacturers/integrators are target customers for both SpaceCloud Hardware (iX5 and iX10) and the SpaceCloud Framework and related software. The demand for on-board processing is increasing, and Unibap can provide both the hardware and software for a flexible infrastructure. The Satellite operators, ground segment providers, and service providers are target customers for the upcoming SpaceCloud applications.
Targeted customer/users countries
Europe, North America, South America, Israel
Product description
The SCFW uses OS-level virtualisation to sandbox and deploy applications on satellites, similarly to popular cloud technologies. Application development for satellites no longer requires highly skilled embedded developers using low level programming languages. With SpaceCloud Framework enabled satellites, developers can choose one of the many supported programming languages, preferred machine learning framework and develop applications as they would normally do in any of the popular cloud infrastructure providers. SCFW redefined the satellite development workflow, reducing the risks, the skills, and the effort it takes to build applications for in-orbit processing. Develop, test, package and deploy – four steps that used to take months, are now reduced to a few days, even hours.
Developers can download the SCFW-SDK and submit their applications to the SCFW AppStore. Users can login to their preferred SpaceCloud provider and request to execute applications directly on a satellite, download data from that mission or even get a notification as soon as an event is triggered on the satellite.
Features include:
CPU, GPU, VPU access for computation
Concurrency of multiple applications in a sandboxed environment
Persistent and Temporary isolated storage space for each account
Unified sensors API
Unified Communication API
Added Value
SpaceCloud, with its unique radiation tolerant x86 architecture, offers the following added value compared to other on-board computer solutions:
Developed using common programming languages and popular machine learning frameworks
Cloud architecture allows for containerised applications to seamlessly run on the ground or in-orbit
Re-use of applications with limited modifications
Low-latency data points
Current Status
The project was concluded with a successful final review. Core functionality of major system components has been developed and is running in the experimental setup of the ODE hardware-in-the-loop. Simple applications have been tested and the system has been validated internally by the project developers. The API and SDK has been developed and third party developers have started building applications for testing the framework in terms of performance and capabilities.
Several partnerships have been formed during the activity, most notably with Amazon Web Services and Swedish Space Corporation. The plan going forward is to perform an in-orbit demonstration to demonstrate the SpaceCloud capabilities.
To know more:
SpaceCloud Framework Datasheet – presented by Unibap
