agriKOPA

Objectives of the Product

Currently, many farmers are unable to access credits to buy inputs such as seed and fertilisers. Women and young people are particularly disadvantaged. With the agriKOPA service, agriBORA creates a credit score which provides more objective access to loans.

Working together with our agriHUBs, agriBORA already provides farmers with inputs, as well as linkage to the markets through contracts with agri-processors. We provide farmers with advice throughout the season, based largely on satellite EO data. But access to credit is generally acknowledged to be a major problem for farmers, negatively affecting their ability to make a living.

Since agriBORA’s major revenue stream comes from commission charged on transactions which take place over our platform, farmers’ lack of access to credit is also a problem for agriBORA.

The agriKOPA product produces a credit-score which enables Financial Service Providers (FSPs) to lend with confidence. In addition, during the growing season, EO-based yield forecasts help the lenders to constantly monitor their risk.

Customers and their Needs

We have three main customer groups for our product: smallholder farmers, agriHUBs and FSPs.

Smallholder farmers often lack money for high-quality seeds and fertilizers and also for services like ploughing and soil-testing. With our concept of earmarked loans, we provide farmers with financial resources exactly when they need it.

For agriHUBs and their managers, enabling loans means that the farmers have more money to spend on the products and services provided by the hubs. The agriKOPA service helps the agriHUBs retain their customers and build sustainable businesses.

FSPs see the smallholder farmer sector as risky and inefficient to service. Obtaining the basic data before even considering a loan is difficult and costly. Estimating the creditworthiness is a problem, with women particularly disadvantaged. FSPs also need information on the development of the harvests in regions where they have granted loans, in order to monitor their risks.

The picture below shows an agriBORA “agriHUB”, being visited in connection with the requirements definition phase of the project.

Targeted customer/users’ countries

The agriKOPA product will be first introduced in several of counties in Western Kenya. Thereafter, the product will be made available throughout Kenya, with expansion into other African countries also in the planning.

Product description

The agriKOPA product is innovative in many aspects. The application of EO data and the compilation of immutable transaction histories using information stored in the agriLEDGER is ground-breaking in the agricultural sector in Kenya. With EO data, agriKOPA monitors crop development and can give timely insights to banks and farmers regarding problems arising. The farmer interacts with this software-as-a-service platform through a phone, via a USSD-code menu, removing the need for a smartphone.

With banks, the interaction will be through an API interface, connecting agriKOPA’s software to the software of the bank. The diagram below shows the service to allow FSPs to monitor their risks.

Added Value

AgriKOPA’s main competitors provide farming inputs and services at different types of farmer service centres. They provide credit, often linked to insurance policies. Credits are financed directly by the company, or in cooperation with banks.

We bring added value through our innovative credit scoring algorithm and field monitoring which is enabled by Earth observation, machine learning and artificial intelligence.

AgriBORA is the only company which provides a true end-to-end service, ensuring that contracts are in place with agri-processors, thereby providing a guaranteed access to market. This in turn greatly reduces the risk of bad debts for FSPs.

Current Status

The final presentation took place online in October 2024 and was attended by over 30 people from a variety of different organisations interested in the project results. The final validation of the agriKOPA processes and the systems that support them revealed, in general, a high level of satisfaction among the different stakeholders (farmers, agriHUBs and the bank).

AgriBORA learned a number of vital lessons during pilot operations and validation, which we would not have learned without the agriKOPA project. This has led to some changes to the processes surrounding both lending and repayment.

As a next step, agriBORA plans to run agriKOPA during the long rain season in Kenya, starting in March 2025, involving substantially more farmers and agriHUBs, the first step in a planned rapid upscaling.

The most important lesson learned from agriKOPA was that many farmers are reluctant to sell directly after harvesting, since prices are generally lower. Instead, they store the product on their own premises, which usually leads to considerable loss. Harvest loss is a recognised problem in Kenya and the government has introduced a Warehouse Receipt System as a countermeasure. AgriBORA is now planning to open the first private sector-run warehouse in the country, in the Uasin Gishu County (December 2024), with support from the International Finance Corporation of the World Bank, the Agricultural Finance Corporation of the Kenya Government and the Warehouse Receipt System Council.

I*STAR

Objectives of the Product

The overarching objective of I*STAR is to minimize latency time between an event occurrence and the submission of a feasibility request. Leveraging advanced artificial intelligence techniques, I*STAR demonstrates the ability to:

Build User Profiles: Aggregating user preferences on data and acquisitions, I*STAR creates comprehensive user profiles to understand better individual needs.

Automatic Monitoring of Near-Real Time Events: Using artificial intelligence algorithms, I*STAR autonomously monitors near-real-time events, ensuring timely responsiveness.

Data Discovery: Identifying data of potential interest for users, I*STAR enhances the availability of pertinent information.

Automatic Gap Filling: Employing automation in the ground segment, I*STAR seamlessly fills programming gaps with new acquisitions tailored to user interests.

Cost Minimization: I*STAR minimizes operational costs in the ground segment, introducing efficiency and reducing manual intervention.

By applying artificial intelligence algorithms, I*STAR models user preferences, encompassing satellite platforms, themes, areas of interest, types of acquisitions, and suggested events. This enables to offer tailored recommendations, aligning precisely with users’ business needs. The specific outputs include suggestions for suitable catalogue products for download and intelligent mission programming.

The intelligent tasking capabilities not only optimize missions by utilizing orbit gaps left unused in standard programming but also significantly reduce operational costs through fully automated order scheduling, maximizing satellite capacity utilization. This adaptable solution can be delivered either as a full platform or as a service, offering flexibility to cater to diverse user requirements.

Customers and their Needs

Targeting EO constellation operators, I*STAR aims to streamline user interaction with the mission ground segment, optimize on-board resource utilization, reduce mission planning times, and enhance automation. With a focus on providing data and value-added services, I*STAR seeks to maximize data sales volumes for its customers. The primary challenges faced by I*STAR include unsaturated constellation capacity and missed opportunities for tasking. I*STAR proposes revenue growth by offering users tailored data through its monitoring services, tightly integrated with intelligent feasibility, thereby unlocking higher commercial opportunities for mission providers.

Targeted customer/users countries

Target customers are EO constellation operators who:

lack mission planning skills.
Need mission planning time optimization.

Product description

I*STAR is presented as an as-a-service application, aiming to enhance the capabilities of end-user platforms such as ground segments or any service platforms acting on behalf of users. The primary objectives include elevating user experience by suggesting relevant archived products and potential new acquisitions, automating end-user platform operations to reduce operational costs, improving mission efficiency by recommending new acquisitions to fill gaps in the planning process, and leveraging information from social and institutional portals to address acquisition needs based on events like floods or earthquakes.

To meet these objectives, I*STAR focuses on several key capabilities:

Collecting user and mission operator preferences to build personalized user profiles.
Gathering relevant events from social networks and institutional services within the earth observation domain.
Discovering archived data of potential interest for users.
Automatically filling programming gaps with new acquisitions tailored to user interests.

The as-a-service configuration of I*STAR provides access to functionalities and integrates with external entities, such as the Control Ground Segment, through APIs. It is designed for instantiation in a multi-tenant environment, allowing each Ground Segment to have a dedicated instance of I*STAR. Each instance is multi-mission, meaning that if a Ground Segment hosts multiple missions, all satellite platforms will be managed by the same I*STAR tenant in a multi-mission operating mode.

Added Value

To the best of our knowledge, there is currently no comparable solution on the market. Presently, each mission relies on a dedicated solution for satellite tasking, lacking a comprehensive view of satellite acquisition to effectively meet customer needs. I*STAR addresses this gap by introducing a unique data access pattern in Earth Observation, a novel approach not previously deployed for satellite data acquisitions. By guaranteeing a unified data access pattern to mission providers and usersh, I*STAR ensures the delivery of products and acquisitions tailored to user profiles, highly reducing the need for specific expertise on individual missions. This streamlines activities and delivers significant value to both service providers and end-users.

Notably, our solution is inherently mission and sensor agnostic, allowing easy configuration to support various missions and data sources. The adaptability of our AI algorithms ensures they automatically adjust to diverse needs, avoiding the need for specific development for a particular class of products or acquisitions. I*STAR adopts a micro-services architecture, packaged as Docker Containers, and is designed to run on Kubernetes or equivalent platforms. This “as-a-Service” infrastructure can be seamlessly instantiated and operated on any cloud provider or platform, providing flexibility and scalability for users.

Current Status

The project has reached a successful conclusion and is now accessible both as a standalone service and integrated within EASE-ground, a digital ground segment product within the Telespazio digital portfolio. This milestone marks the availability of the project to users in various deployment options, providing flexibility in choosing the most suitable approach based on their specific needs and preferences. Whether accessed independently as a service or seamlessly integrated into the comprehensive EASE-ground platform, the project aims to cater to a diverse range of users within the emerging New Space Economy.

SKAISEN

Objectives of the Product

Earth Observation missions generate a vast amount of data, which often presents key information for decision makers. These missions commonly collect hundreds of gigabytes per day, making it impossible to download back to Earth in a cost-effective way. However, up to 90% of this data is noise lacking any added value and thus cannot be further utilised by the end users. SKAISEN has the capability to detect, analyse, and prioritise objects of interest within non-cloudy images. This enhancement addresses two critical challenges in satellite operations:

Bandwidth Optimisation: By identifying both unusable (cloudy) data and non-relevant clear imagery, the system will dramatically reduce unnecessary downlink volume, preserving precious bandwidth for truly valuable data.
Latency Reduction: By performing object recognition directly onboard the satellite rather than waiting for ground processing (which typically occurs hours or days later), SKAISEN will significantly reduce the time between image capture and delivery of actionable intelligence to end users.

SKAISEN is designed to be highly reusable and independent of the sensor and processing unit selected for the mission. This ensures its availability for any Earth Observation mission and its highly competitive price. By leveraging the AI-driven SKAISEN solution, customers can significantly reduce their costs associated with the ground segment infrastructure while maximising the valuable data downlink, as well as reducing the time to get to actionable data to the end-users.

Customers and their Needs

The key customer segments targeted by SKAISEN are Mission Owners, System Integrators, Payload Developers and Mission Operators, Governmental and Commercial Terrestrial Customers, and Maritime Security Operators.

The main challenges of these customers in data access are:

Downlink capacity is wasted with unusable data instead of transmitting actionable information
Wasted downlink resources increase latency and delay end-user access to valuable data
Raw data from optical sensors is often contaminated by unusable pixels or scenarios, which could be mitigated
Mission managers and operators spend significant time on human-centric operations that could be automated
Slow access to critical data
If the AIS system is turned off, it is not simple to perform checks on illegal, security or unregulated activities.

SKAISEN aims to bring a customer-friendly solution for acquiring valuable data only, reduce mission costs and time to delivery.ly solution for acquiring valuable data only, reduce mission costs and increase mission profits.

Targeted customer/users countries

Mission Owners, System Integrators, Payload Developers and Mission Operators all over the world, focusing on Earth Observation missions, as well as Governmental and Commercial Terrestrial Customers, together with Maritime Security Operators.

Product description

SKAISEN is a comprehensive decision intelligence service for satellite operations, transforming raw imagery into actionable insights directly in orbit. The system combines advanced AI algorithms with optimised hardware implementation to enable real-time data analysis and selective transmission, addressing critical challenges in bandwidth optimisation and latency reduction for Earth Observation missions.

SKAISEN operates as an integrated system with focus on processing data in real-time aboard satellites and providing infrastructure for receiving, decrypting, and integrating the resulting insights. This dual architecture enables flexible deployment across diverse satellite platforms and ensures access to relevant information in minutes.

Added Value

The principle of how SKAISEN works is described in the picture below. After the satellite collects imagery, SKAISEN processes the data and extracts valuable data only, which is then sent to Earth. This approach optimises downlink by minimising unnecessary data transmission and reducing latency.

There are several views on how to optimise data transfer to Earth. The most common approach is processing data before transportation. There are already available onboard data processing products in the market. The difference between them and SKAISEN lies in their reusability.

SENSOR INDEPENDENCE: While the existing products are operable, e.g., in the visible spectrum, SKAISEN is sensor independent, regardless of whether the optical sensor operates as monochromatic, RGB, multispectral or hyperspectral. The flexibility in choosing the right camera for the mission remains with the customer.

DPU INDEPENDENCE: Whether a single software solution or the entire payload is needed, SKAISEN offers both.

HERITAGE: SKAISEN demonstrated its first capabilities during the VZLUSAT-2 mission in the summer of 2022.

Current Status

SKAISEN has completed the first phase, focusing on onboard cloud detection, and achieving Initial Release Status with validation in laboratory environments with two strategic validators. In the next phase, the product will be developed to present a comprehensive decision intelligence solution, addressing the growing demand for real-time actionable insights in latency-sensitive applications.

Tri-Band Monopulse Antenna

Objectives of the Product

EO and RS applications for collecting an ever-increasing volume of data from images and sensors, or in the event of natural hazards and disasters, have become extremely important for emergencies caused by global warming, pollution, continuous erosion, and destruction of the natural environment.

To monitor real weather or land surface conditions in near real time, national emergency services, private utility companies, governments need high resolution visual imagery.

This results in a huge volume of data requiring broadband for transmission to the receiving earth station gateways.

These wide bandwidths can no longer be provided by traditional X-band even when both polarizations are exploited, and the remedy for this limited bandwidth is to exploit K- or Ka-band.

Most antennas on the market operate in one or two frequency bands at a time, and even Dual-Band antennas that typically operate in the X and S bands have bandwidth and data rate limitations for new services, which can be managed by Banda Ka. The ITZ-TBMA-1.0 represents the development of one of the most advanced products in terms of flexibility of satellite bands on a single antenna (S – X – Ka), data reception capacity and bandwidth for EO – RS – IoT and compactness.

Customers and their Needs

The target customer who is also a strategic partner for this project is Telespazio. It has shown interest in the product (as have other users) as it is interested in the expected performance of the antenna system with reference to ground stations and more specifically in the expected capacity to fully satisfy the demand for an ever-increasing need for data reception volumes for EO-RS-IoT from satellites. Telespazio was involved in the project by assigning fundamental tasks such as the validation of specifications in accordance with market needs as well as the final verification of the product.

Targeted customer/users countries

Italy, Finland, and Germany.

Product description

The project represents the development of a Tri-Band (S-X-Ka) antenna with S-band that supports Rx/Tx mode for TT&C and X and Ka bands in receive mode. The design includes Monopulse tracking techniques for unprecedented precision, which is critical for receiving high-bandwidth data across all frequencies. The Telespazio customer has the task of validating the technical specifications of the product in accordance with market needs and verifying the final product.

Added Value

The proposed solution brings numerous and substantial advantages as it allows first of all to reduce the number of antennas by concentrating multiple bands in the same antenna, to offer new service scenarios as well as accuracy in satellite tracking and pointing thanks to the use of multiband monopulse satellite tracking and guarantees optimized management of latest generation compact multiband antennas and systems.

Compared to the identified competitors, the proposed solution is more advantageous because it offers a tri-band for antennas up to 13 m (7m, 9m, 11m, 13m). Thanks to the experience gained over the years, we can offer a wide range of equipment such as antennas, feeds, tracking receivers, antenna control units and therefore we are able to offer modular solutions up to a complete solution.

Furthermore, we can adapt our solutions to existing antennas. Unlike large companies that offer standard products, we tailor to customer needs and provide a customized solution

Current Status

During the first months of activity, thanks to a market analysis, consultation with stakeholders as well as the collaboration with the partner (and first customer) Telespazio, the product requirements were identified and subsequently translated into technical specifications. Based on the specifications, the preliminary design of the RF and the mechanical parts is starting.

Illegal construction detector

Objectives of the Product

Countries where high real-estate taxes exist also have issues with illegal constructions, because some people try to avoid paying the respective tax. The solution helps municipalities find owners who are not properly paying real-estate taxes by detecting buildings from satellite images.

Based on satellite data, the solution identifies new construction activities in an area. It filters out unlawful activities based on building registry data, enabling the automatic filtering of illegal activities from all detected construction activities. The web application not only detects constructions not paying the (proper) real-estate tax but also helps municipalities to contact the real-estate owners so they can start paying the tax properly.

We provide this solution by recognising buildings from high-resolution satellite images. To do so, we utilise machine learning algorithms on satellite data, to recognise construction activities and combine this data with building register data for our target use case.

Customers and their needs

Municipalities face substantial issues with undeclared constructions, resulting in lost tax revenue. The main issues include unpermitted swimming pools and extensions in private properties. Apart from the financial issue, illegal constructions also create a security problem, especially with pools that can be very dangerous when built on higher floors. In this case, they could potentially damage the structure of the building. Such cases are also more difficult to discover through manual inspection because officials have limited access to buildings.

Current methods, such as relying on citizen complaints and manual inspections, are inefficient and labor-intensive. Municipalities recognise the income loss from manual systems and are seeking more efficient methods.

Target customers/ users’ countries

Target customer: local municipalities
Primary focus: Spain

Product Description

The core of the product is the capability to detect objects of interest (buildings and pools) from 30 cm-resolution satellite data with sufficient precision and recall to support our business logic. Detections are compared with information on registered buildings to identify unregistered and misregistered buildings.

From the customer’s perspective, detections will be served to the client via a web application that has supporting features for follow-up actions regarding misregistered buildings (such as sending notices).

Added Value

Several companies in the market offer solutions for infrastructure monitoring based on data from satellites. One aspect, that sets our offering apart from the companies already operating in the market, is that our solution foresees integration with building registers, which enables automatic filtering of illegal actives from all detected construction activities.

Current Status

The following tasks were covered in the de-risking activity:

1) validation and improvement of the requirements specification.

2a) utilisation of machine learning algorithms on satellite data, with sufficient precision and suitable to offer construction surveillance services in Spain.

2b) combination of satellite data with building register data for our target use case.

The Product Development phase will most likely continue, where the web application of the product will be developed.

Satelligence Biomass

Objectives of the Product

Satelligence Biomass is a report- and platform-based solution that helps companies understand their Scope 3 emissions and where the biggest issues occur in their supply chain.

The service shows changes in aboveground biomass for different supply chain levels (from farm to aggregators like mills and suppliers) to be able to make more targeted sourcing decisions. Satelligence Biomass is a new module/feature on our existing solution platform. The information can be used as part of the Science-Based Targets Initiative (SBTI) reporting or internally to make sourcing/investment decisions.

For SBTI reporting, additional information on Scope 1, 2 and other Scope 3 data needs to be integrated through partners or our clients, hence dissemination should be also flexible (API, exporting capabilities etc.). Before the project started, we already developed our carbon (aboveground biomass) algorithms for a number of regions and landscapes in pilot projects, with corporates like Mondelez International, Cargill and Rabobank, but more work was required to make this a scalable off-the-shelf solution.

With Satelligence Biomass we now service all multinationals that have net-zero commitments with reliable, science-based results.

Customers and their Needs

The key customers segments targeted by our service are fast-moving consumer goods companies and large-sized traders active in soft commodity supply chains (e.g. palm oil, cocoa, soy).

Satelligence Biomass offers companies active in soft commodity supply chains a better understanding of their Scope 3 emissions due to land use change and agricultural expansion. This allows our customers to make better sourcing decisions and report progress towards compliance and reporting frameworks.

We have identified the following key pains:

Not being able to account for and report on Scope 3 emissions towards stakeholders.
Being at risk of unexpected carbon emissions due to forest loss in the supply chain.
Being at risk of unexpected exceedance of emission rights.
Having to compensate for unexpected carbon emissions in the supply chain.
Not being able to account for Scope 3 on emission-related statements and metrics (third party verification).

And the following key gains:

Access to carbon risk and storage potential data of the supply chain.
Being able to prioritise resources for net-zero commitments based on all scopes.
Insight in the spatial distribution of the risk of emissions over the sourcing landscapes.

Targeted customer/users countries

Our target customers and users operate globally. Monitoring mainly happens in the (sub)tropics because of our commodity focus.

Product description

The functional blocks of the Carbon estimation model that need to be developed are:

1.1 Satellite data processing: Landsat & Sentinel-1 and 2 (already developed).
1.2 Satellite data processing: GEDI data processing and filtering.
1.3 Satellite data processing: ICESAT data processing and filtering.

2. Allometry selection and application module.
3. Auxiliary data processing and look up module.
4. Analysis Ready data module.
5. AI carbon estimation training, tuning and prediction module.
6. User interface module.
7. Scaling: carbon estimation at global scale.

The model builds on results of the already operational satellite image distributed processing framework (DPROF).

The main (functional) modules are described in the table below. The modules can be separated into three main categories:

Extension of existing (data) models, storage and interfaces.
Development of new modules that will compose the Carbon estimation pipeline.
Designing and building end-user products.

Innovation versus the market:

Available solutions to measure net-zero commitments are models of consultancies.
Available solutions to measure carbon by satellites often focus on the offsetting market (carbon credits) due to supply chain complexities.
Many companies providing carbon services don’t have market access with corporates like Satelligence has.

We can bring together those elements and have a first-of-its-kind solution on the market.

Added Value

Access to carbon risk and storage potential data of the supply chain.
Being able to prioritise resources for net-zero commitments based on all scopes
Insight in the spatial distribution of the risk of emissions over the sourcing landscapes

Current Status

Satelligence Biomass had its kick-off on 6 July 2023.

The Satelligence team had several meetings with demonstration partners. Next, Satelligence also had a full day session with a consultancy to discuss a potential partnership.

We also started setting up an approach to sell Satelligence Biomass, including pricing options. These were tested throughout the project phase. The Final Review took place on 18 October 2024. By the project’s end, four customers signed up to the Satelligence Biomass service, totaling over € 250.000 in annual revenue.

The service is now ready to be commercialised at scale and we have a significant pipeline for the short- and mid-term periods.

Saturnalia

Objectives of the Product

Key Features:

Daily monitoring: receive daily updates on crop conditions, with high-resolution images and data.
Health analysis: utilise NDVI (Normalized Difference Vegetation Index) and other spectral indices to assess plant health and detect stress early. It includes a proprietary algorithm to compare different crop vintages.
Growth tracking: monitor growth stages based on continuous data collection and analysis.
Weather impact assessment: understand the impact of weather events on crops with historical and real-time weather data integration.
Damage assessment: provide accurate and timely damage assessments to support insurance claims, using satellite imagery and advanced analytics to quantify the extent of crop damage.
Enhanced risk knowledge: historical satellite time-series to assess risk level associated to every single crops.
Customised reporting: get tailored reports that meet the specific needs of growers and insurance companies, with actionable insights and recommendations.

Customers and their Needs

As the agriculture industry shifts towards precision farming, the demand for accurate and timely data is increasing. In addition, governments are pushing towards precision farming techniques by providing funds to invest in new technologies.

Crop insurance companies are looking for innovative solutions to enhance their services and reduce risks, making Saturnalia a perfect fit.

Saturnalia serves two core markets: agricultural producers and agricultural insurance companies. Producers use our service to optimise daily operations such as irrigation, fertilisation, and crop health monitoring. Insurance companies benefit from our technology to better estimate risks, manage claims, reduce fraud, and assess damages from natural disasters. The potential for transformation in these traditionally conservative industries is immense.

Target customers/users’ countries

Farmers targeted so far are located all around the world. To this day, more than 140 pilot cases have been completed across Italy, France, Austria, Germany, Portugal, Australia, US, South Africa and Argentina. The market for crop insurance companies is again global, with main contacts in Italy and Germany.

Product description

Saturnalia aims to enhance agricultural productivity and resilience, thereby improving lives through better resource management and crop protection.

A various set of heterogeneous data sources is organised and harmonised to offer easy access. Daily images and weather data are automatically ingested and processed every day.

Growers can benefit from all these datasets by using a web platform, available for all devices. Insurance companies may leverage results via API or via a web platform.

Added value

Saturnalia’s daily satellite crop monitoring service addresses critical needs in the agriculture and insurance sectors, offering a sophisticated, reliable, and user-friendly solution. By providing near real-time data and actionable insights, we empower growers to optimise their operations and insurers to manage risks effectively, driving overall productivity and sustainability in agriculture. Many competitors in this space often rely on lower resolution data and do not prioritise the interaction with the final users. On the other hand, for insurance companies specific damage models will allow them to save time of loss adjusters operations in the field.

Current Status

This activity is completed. The current service automatically ingests daily satellite data and distributes them via a simple web interface. A mobile app is also available on App Store and Play Store. It allows to run comparisons on the different datasets, to compare with what users might see with their own eyes and also to collect geolocated reports in the field to keep track of what is happening.

Insurance companies can benefit of our monitoring services as well as models to detect anomalies. More than 140 case studies have been completed across Italy, France, Austria, Germany, Portugal, Australia, US, South Africa and Argentina. Several crops have been tested, such as grapes, tomatoes, rice, corn, artichokes, apples, etc.

SIAMaas

Objectives of the Product

The Copernicus Programme and its Sentinel satellites generate more open and freely available satellite data than any other space programme ever before, but the key challenges are the same: converting big Earth observation data into information using transferable and automated methods that can also be used by non EO-experts. The Satellite Image Automatic Mapper (SIAM) software is an existing expert system that performs fully automatic, near real-time, preliminary classification. It categorises multispectral EO images using a physical model and produces semi-concepts (i.e. preclasses) at several levels of detail. SIAM has linear algorithmic complexity and is scalable to allow for processing big data volumes. Unlike unsupervised clustering routines or machine learning/deep learning approaches, the service produces stable, comparable, application-independent spectral categories with known semantic associations using a physical-model from EO imagery without the need for any sample data. We bring SIAMas- a-Service (SIAMaaS) into a scalable container-based infrastructure in the cloud where the data are located (e.g. DIAS), therefore making it accessible for everyone, not just EO experts. Users select images on an area-of-interest and timeframe, and SIAMaaS converts the data into information. Our approach takes care of the entire processing chain, i.e. data preparation, analysis and access to results. It will flexibly enable further online processing of results, thus establishing SIAMaaS as an anchor point for big EO data analysis, enhancing user’s workflows.

Customers and their Needs

The key customers segments targeted by our service are: (1) Public entities/authorities; (2) Scientists/research organisations in general, especially non-EO experts; and (3), Support/improve existing and future (Copernicus-related) services in a b2b business model to support geospatial services of private companies (incl. Copernicus service providers) and midstream providers, like ARD providers. User’s needs:

Big Earth observation (EO) (time series) data themselves do not have value if they are not transferred into information; however, may state-of-the art methods require training samples or application-specific design/algorithms and can be difficult to operate.
Updates and new versions of services are only available at some (longer) intervals (e.g., several years). Speed-ups of workflows using automatically obtained intermediate products are required: Repeatable, standardized analysis of Sentinel-2 data as an initial pre-processing step for automated EO based services. Stratify services (e.g. atmospheric correction) on reliable preprocessing, create fully automated change detection including alarms for suddenly changing environments on reliable pre-processing, increase the convergence of evidence and improve accuracies of existing services (e.g. LULC) based on reliable pre-processing.
Integrating EO data in analysis workflows requires skills and time (to learn), due to: Required knowledge of EO (physics, …), Sensor architecture (bands, …), Data formats. This is a hurdle to use EO data as non-EO experts.

Target customers/users countries

Worldwide, but initially identified users are located in Europe.

Product description

The Satellite Image Automatic Mapper (SIAM) software is an existing expert system (prior-knowledge-based decision tree) that performs fully automatic, near real-time, preliminary classification. The software produces semantic enrichment that contains different granularities (i.e. different number of spectral categories), as well as additional data-derived information layers (e.g., multi-spectral greenness index, brightness).

Innovation: We developed SIAM-as-a-Service (SIAMaaS) into a scalable containerbased infrastructure in the cloud (e.g. DIAS) making it accessible for everyone, not just EO experts. Users select single or multiple images based on an area-of-interest and timeframe, and SIAMaaS converts the data into information. Our approach takes care of the entire processing chain, i.e. data preparation, analysis and access to results. Itwill flexibly enable further online processing of results, thus establishing SIAMaaS asan anchor point for big EO data analysis, enhancing user’s workflows.

User interaction: Easy to use online web-interface and a CLI for batch processing. The web-interface is accessible under https://app.color33.io . While SIAMaaS has been developed for Sentinel-2, the 33 colors stand for the 33 spectral categories which can beautomatically derived for every calibrated multispectral-sensor allowing cross-sensor analysis beyond spectral reflectance values and number of bands. “color33” will be the name of the service when exposed to the users.

Added value

No existing service automatically classifies any user-selected Sentinel-2 data on-demand with similar depth in near real-time without user interaction, nor has a similarly proven application record. Machine-learning-based services exist, but require sample collection,
user interaction, and have not proven transferability to other geographic areas globally or to other sensors. Similar approaches are not ready for big data volumes, with respect to computation time, transferability, analyst subjectivity (e.g. samples, parametrisation) etc.
Paired with a broad / global application range these are USPs of SIAMaaS. Products built on top of SIAM are additional business opportunities and a factor for competitive sustainability. Ongoing R&D allow us to react to upcoming competitors. The original idea won the Copernicus Masters Prize Austria 2020.

Current Status

The activity is closed with November 2023 and is now continuing as an operational service and expanding into the market for creating added value services on top of it and participating in relevant public tenders.

AgriTrack Full DSS Stack

Objectives of the Product

The Full DSS is an innovative tool providing highly valuable insights obtained from the correlation of several data sources to support AI processes that can provide suggestions and better communication for the farmers inside the value chain.

As a matter of fact, the product is an advanced Decision Support System (DSS) able to combine modelling and remote sensing historical information to diagnose hot spots of vegetative stress, highlighted by remote sensing, pests and disease detection models.

The ambition is to overcome the two main problems of agricultural DSS:

Information overload.
Lack of spatial information and practical prescription.

In fact, satellite data collected and elaborated by AgriTech platforms are usually able to identify general issues (such as areas with reduced vigor within each field) but cannot provide precise diagnosis of the seriousness of the vegetation vigor issues and information on its possible causes. On the other hand, traditional DSS can provide specific information on single issues, such as field water or nutrients needs, pest risks etc., which usually refers to the whole field, without differentiating between areas of the field. With the Full DSS, all information can be integrated, providing tempestive feedback on what is happening in the field, where it is happening and why, giving some agronomic advice.

Customers and their Needs

AgriTrack Full DSS Stack can directly support a wide range of stakeholders with direct and indirect benefits. The targeted customers are:

Farmers, which could improve farm profitability through optimised use of inputs and reduce environmental impact.

Farmers’ associations, which could collect useful data and information to provide agronomic support to their consociates.

Cooperatives, which could monitor multiple fields and timely identify hot spots. Consortia, which could obtain an easy-to-use tool for providing agronomic support to numerous farms.

Agronomists, either independent professionals or within the above-mentioned organisations, can benefit from an effective tool for field monitoring, immediate intervention and communication with clients.

Facing an ever-evolving environmental and economic landscape, farmers, consultants, and other actors in the agronomic sector increasingly require identifying critical areas within the field and plan new and immediate intervention action remotely and timely.

On this regard, the AgriTrack Full DSS stack can respond to their needs to take agronomic decision-making based on reliable and spatialised information, improve the monitoring and data collection from several fields and farms, and be able to obtain tangible and reported results on farm’s agronomical performance. This is completed by the possibility provided to users to have spatial and detailed alert information and its causes.

Lastly, from the interviews carried out with target users, the urgence to be trained and supported in the use of innovative and technological tools strongly emerged. Therefore, the commercial offer will include a module of one-to-one training on the use of the tool, while videos and tips have been included within the platform.

Target customers/users countries

Italy and Spain.

Product description

The Full DSS is a functionality to be added to an existing cloud based FMIS. It will use all existing and new model outputs and sensed data (e.g., satellite, in-situ sensors) to obtain a probabilistic diagnosis of the abiotic agronomic issues affecting specific areas within the identified critical field. This information will be further enriched with alerts on pests and disease risk level.

This will be achieved by testing different Machine Learning and data-fusion approaches adopted as predictive tools within the large dataset that has been created by Agricolus in several years of smart farming experience. The AgriTrack Full DSS Stack consists of two modules. The first is a web application called Visualization Module, that displays sets of geospatial information and alerts, being the main interface and interaction point with the user. The second is the Computational Module, which produces such information and alerts combining different data sources into specific algorithms.

Added value

AgriTrack Full DSS is a new solution which brings four core innovations:

critical area diagnosis from interpretation of satellite images: the Full DSS is the only solution able to automatically detect real vigour issues within the field, inform the users and provide information about its causes.
Spatial model outputs on specific issues at sub-field level: the Full DSS is able to provide information about field’s criticalities, such as nutrient and water needs, pest risks, thermal stress, waterlogging and heat waves. Moreover, information is elaborated differentiating between field specific areas, allowing precise management of the issues.
Integration of several data sources into a single information: the Full DSS models will integrate information from a range of different sources (satellite, sensors, user data etc.) to provide the simplest and most useful information to the user, which will no longer have to retrieve data from different sources.
Sharing and aggregation of information at different levels: the Full DSS allows data exchange between hundreds of farms, fostering insights and communication at a large scale. Full DSS makes possible real-time and forecast data-based reporting among connected farms and other stakeholders, and agronomic management of the entire ecosystem with activity and support in optimising agronomic practices.

Current Status

The project activities are successfully concluded. The product reached TRL 9 and it is ready for commercialisation.

All WPs have been successfully concluded, and the product performances reached the identified KPI to fulfill the Service Requirements identified during WP2000. The validation activities with users have been successful, confirming the novelty and market adequacy of the product.

SENTINELS AnyTIME

Objectives of the Product

Persistent cloud coverage significantly impairs the frequency of EO observations required by the end users in the Potato sector, and thus harming confidence by the broader customer pool in the AgroFood.

The ultimate goal is to remove the harmful effects of cloud cover in the EO imagery and be able to provide continuous (daily) monitoring services required by AgroFood (served by AgriTech) and the land monitoring EO sector as a whole.

Customers and their Needs

Our customer are users in every parts of the EO domain which are in need of continuous monitoring. The highest demand is given in the agriculture domain.

Specifically, the most important agricultural seasons in Europe are, on the one hand, the growing period in spring, and on the other, the harvesting months in fall. This reality, impacting the EO monitoring capacities in the Potato sector, manifests itself with all major crops, especially since early season crop detection and vegetation monitoring is one of the most valuable services in the industry. For example, the identification of emergence which is needed an input for yield modelling, such as maize and soybeans among many others, which have similar low precision and recall values in early season crop type predictions. Those regions located mid-to-high latitude in Europe suffer from consistent cloud coverage in these 2 important periods, which is statistically evident in the Sentinel-2 data archive records and further substantiated by the continuing failure of the Copernicus Third Party mission suppliers to deliver cloud-free VHR coverages within one year of tasking.

Target customers/users countries

Nearly all sectors in the EO Value chain can benefit from cloud-free imagery, the target customer are mostly EO Value Adders with existing value chains that aim to improve their services.

Product description

SENTINELSAnyTIME is a service that allows the user to retrieve cloud-free, radar corrected optical Sentinel-2 data through a subscription model. The product is divided into two separate services:

1. Sentinel-2 cloud-free bands
2. Sentinel-2 cloud-free (pre-defined and custom) indices and biophysical variables.

Both services will make use of advanced AI technologies in both the temporal and spatial domain to allow for an efficient cloud-detection, removal, and ground reflectance reconstruction. This is achieved by temporal S-2 time-series reconstruction (Bayesian approaches, gaussian processes and recurrent NN models) and integration of auxiliary data (Sentinel-1) in a spatial context (Convolutional NN) for the detection of optically hidden sudden events. These include for example landcover changes such as harvesting events or natural disasters that cannot be predicted through interpolation techniques.

Both services are presented to the customer as an easily accessible and user-friendly Graphical User Interface. Furthermore, all service elements can be accessed through an API which facilitates tasks that need to be carried out automatically by the end user or to allow for derived products/services that are built on top of SENTINELSAnyTIME (e.g., agricultural monitoring or environmental services).

**SENTINELSAnyTIME** Services: Cloud-free band data (left) and cloud-free (custom) Indices

Added value

The added value is manifold and very central for most EO applications.
A continuous (gap free) timeseries of bio-physical indices, time span & time step selectable will lead

an overall increase in the timeseries quality, increases the accuracy of the added value output service.
Increase in temporal continuity (gap free), leads to a more granular and better decision-making points.
A spatial reconstruction time series of bio-physical indices
Availability of timeseries where there would be no data otherwise, increases the spatial consistency of the added value output service
Increase in spatial continuity (gap free), leads to a higher data completeness, and avoids blind spots in decision-making process.

Allow for continuous monitoring, despite cloud condition. The future is hybrid!

Current Status

A prototype version of Sentinels AnyTIME is available and goes through various testing stages and is adapted to our latest requirement specification. A public demonstrator will be available in 2023.