Archives: Activities Portfolio

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: 

  1. Extension of existing (data) models, storage and interfaces.
  2. Development of new modules that will compose the Carbon estimation pipeline.
  3. 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’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.

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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

  1. an overall increase in the timeseries quality, increases the accuracy of the added value output service.
  2. 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
  3. Availability of timeseries where there would be no data otherwise, increases the spatial consistency of the added value output service
  4. 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.

microDRCE

Objectives of the Product

Nowadays, most of the applications that the customers need require high responsiveness capability and extended areas monitoring. These needs can be satisfied by deploying a SAR satellite constellation system with multi launch capability to optimize both the launch strategy and costs. Therefore, it is key to evolve the present radar technologies to improve both the SAR instrument flexibility, in line with the customers’ request, and its compactness for microsatellite configuration for the constellation purpose. The relevant TAS-I product is the SAR Micro Satellite Constellation (SMSC).

The intention is to develop a micro SAR instrument which implements all the standard operative modes (Stripmap, Spotlight, ScanSAR) to operate on a micro platform; for such reason an advanced and highly flexible digital SAR electronic subsystem section is a key feature. The innovative SAR instrument is conceived with an electronic digital section developed at board level to be compatible with the lightweight microsatellite solution concept, targeting a satellite product for the deployment of constellations with a variable number of satellites.

Customers and their Needs

Typical Customer needs are summarized in the following table, highlighting for each Need the “Pain” and “Gain” offered by the SMSC product.

Targeted customer/users countries

Institutional and commercial entities distributed worldwide which intend to improve their national services and space capabilities.

Product description

The microDRCE is a key element of the digital section of the SAR electronic subsystem of the SMSC product, an End-to-End Earth Observation System consisting of:

  • Space Segment organised in constellation with medium or large number of SAR satellites with a mass <200kg;
  • Radar Ground Segment composed of the Core Ground Segment and the Image Analytics Segment.

The key features of the SMSC product are:

  • High responsiveness capability;
  • Regional or global access capability, able to operate in inclined or Sun-synchronous orbit;
  • Satellite architecture optimized for a product perfectly suitable, in terms of mass and volume, to be easily deployed in large constellation;
  • Fast orbital replacement thanks to the compatibility with several launchers
  • X-band multi-mode SAR Instrument (Spotlight, Stripmap and ScanSAR) based on an active planar phased array antenna relying on an innovative highly integrated architecture.

The SAR Microsatellite constellation product is able to serve at the maximum extent all the emerging Space Services with “quasi real time” applications and high performances.

Added Value

Nowadays, most of the applications that the customers need, require high responsiveness capability and extended areas monitoring. The actual SAR Earth Observation systems mainly rely on high-end systems, that provide high image quality capability with poor temporal performance due to a reduced constellation size. On the other hand, in the last years, several emerging companies have deployed (or are in the process to deploy) large constellations of SAR microsatellites, providing commercial services which address specific fields of applications but do not cover all the customer needs. The new SMSC product is able to satisfy all the market needs coming from present and potential future customers.

The distinctive features of the SMSC product are:

  • High responsiveness capability;
  • Regional or global access capability;
  • Satellite architecture optimized for multi launch to be easily deployed in large constellation;
  • Fast orbital replacement thanks to the compatibility with several launchers;
  • X-band multi-mode SAR Instrument based on an active planar phased array antenna to cover all the customer needs.
Current Status

Kick-Off meeting held on April 12, 2023.

Requirements Consolidation phase completed in June 2023.

Design phase completed at begin 2024. The development  of the digital section is ongoing: manufacturing and testing at board level completed; integration at subsystem level in progress.

SiteObserver

Objectives of the Product

Offshore wind is an attractive energy alternative, however rigorous environmental legislation, a harsh and dynamic environment and more remote and widespread locations require thorough and meticulous planning and continuous operational monitoring frameworks of the wind sites and the dynamics of the surrounding water bodies.

However, the majority of the data and information needed to plan, construct and operate offshore wind farms is costly, time consuming and risky to collect, requiring rigorous field campaigns and expensive equipment. Collectively, such logistics is associated with high operational costs and hence limitations in terms of the frequency of inspection and total site coverage.

The high temporality and spatial resolution of modern EO satellite infrastructure combined with novel analytical frameworks and AI provides a cost-efficient and effective means to continuously track and monitor the status, change and dynamics at remote and widespread locations, such as offshore wind farms. Collectively, the effective utilization of these new satellite data sources provides a powerful, low cost and efficient tool to monitor seabed dynamics and movement, coastal dynamics, vessel traffic, turbine direction, sedimentation patterns, individual objects and several other parameters relevant for offshore wind operations and maintenance.

The Siteobserver project aims to assess existing data and information gaps in the offshore wind sector and identify the core user needs and requirements, as well as operational requirements, to make best use of such satellite-based surveillance frameworks. With a vantage point in these requirements, a prototype platform for operational application will be built in close dialogue with key stakeholders.

Customers and their Needs

The overarching goal of the project’s user engagement was to ensure that a user base was built for the SiteObserver solution while it was being developed, and that the system infrastructure and tools were designed to meet the needs of this user base. To this end, a series of more specific objectives to the user engagement was formulated as follows:

  • To ensure that technical developers in the project team understand user requirements and allow these to guide the development of the SiteObserver solution;
  • To elicit relevant information about existing relevant IT infrastructures and processes that the tools will need to be deployed within;
  • To ensure users have the opportunity to contribute to the quality assurance of the SiteObserver solution and tools to ensure their usability.

To assess the user needs and requirements, a series of semi-structured interviews and informal meetings were held with key stakeholders, within the offshore wind industry, in the initial phase of the project. Some user needs and requirements have been further refined based on relevant experiences from stakeholders that DHI has gathered during 30 years of working with the offshore wind industry.

DHI engaged the potential end-users either directly or through the existing DHI network of business relationships and the initial outreach activities were conducted through a combination of email and teleconference calls.

Dialogue was established with several key stakeholders from the offshore wind industry and related sectors, and face-to-face (virtual) meetings were conducted with each of these stakeholders. Qualitative methods using semi-structured interviews were applied during these meetings to obtain a deeper understanding of each stakeholder, their associated work and activities, their use of geospatial data, perception about data gaps and needs and their view and knowledge about the potential of EO to address existing data needs. Additional information was collected more informally, in the margin of various events and associated meetings. All user meetings were conducted as open-ended interviews with the least amount of restriction to ensure unbiased insight to the user needs and potential market gaps. A series of questions were prepared before the meetings, but other questions were generated spontaneously based on the response of the participants. After the user needs have been identified, the process involved translating those needs into specific requirements across different segments of the offshore wind industry stakeholders.

Some of the key insights from the interviews with the stakeholders can be summarised as follows:

  • Assessing the on-site situational conditions is key to allow quick responses to any changes or emergencies and ensuring the safety and smooth functioning of the wind farm. This includes understanding current weather, environmental factors, vessel traffic and the state of infrastructure. Ideally such service should be delivered through automated triggers and early warning systems based on dynamic satellite-based monitoring products.
  • Monitoring vessel traffic and patterns to gain insights into the movement and behavior of vessels around the OWFs. Understanding vessel traffic frequency, routes, and purposes helps in optimizing safety protocols, preventing potential accidents, and ensuring that wind farm operations aren’t disrupted by maritime activities. Additionally, the need for post incident investigation was mentioned – i.e., the ability to apply EO based frameworks to uncover the details of a specific incident causing damage to a sensor buoy.
  • Insights into seabed dynamics and mobility surrounding OWFs is key to uncover the state and dynamics of the seabed structure to anticipate any changes that might affect the stability of wind installations. Accessing satellite-based data and analysis helps in identifying potential risks to infrastructure stability, allowing for proactive measures to maintain the integrity of the installations. Users seek insights into seabed changes to ensure the long-term durability and reliability of OWFs.
  • Assessing marine habitats and biodiversity is important in order to evaluate the impact of offshore wind installations on marine life and biodiversity. Users seek tools that utilise high-resolution satellite imagery and analysis to monitor changes in marine flora and fauna. Understanding the effects of wind turbine foundations on supporting marine life is essential. Users aim to ensure that wind installations are not adversely impacting the surrounding marine ecosystem, while potentially exploring the beneficial role these installations might play in supporting biodiversity.
  • Sea ice mapping is pivotal in assessing the workability and de-risking of operations, ensuring site accessibility, and minimising uncertainties during surveying and operational phases. Both historical insights into sea ice extent and trends as well as near real time monitoring of sea ice and icebergs would enable better planning of OWFs in polar regions and operational decision making. It offers the potential to minimize disruptions caused by adverse sea ice conditions and enhances the overall safety and efficiency of OWF operations.
Target customers/users countries

Tapping into a global market, target customers and users are global.

Product description

While user feedback indicated that continuous streams and easy access to raw near-real-time satellite data were not essential requirements, emphasis was put on the provision of high value EO derived information products on demand.

Four target products/services was identified based on the user requirements analysis:

  1. Post incident intelligence gathering products.
  2. Marine and environmental habitat monitoring products.
  3. Sea ice mapping products.
  4. Coastal zone analysis products.

Examples and mock-ups of such high value EO products and system integration within these four domains are exemplified by the examples below below.

Post incident intelligence can be related to natural disasters but also to vandalism, such as a buoy near a wind park that is intentionally destroyed or vandalized. Information about what vessel was nearby at the time of incident could be of interest.

Example of initial SiteObserver service for Near Real Time monitoring of vessel traffic of a OWF site near Western Jutland, Denmark. The Copernicus Sentinel-1 image shows the OWF site and by using the combined signal from the image and AIS data, vessel can be identified, and dark vessel detected.

Provision of information about marine and environmental habitat with different substrate types, with historical data to define a baseline before installation of the OWF infrastructure, and recent data to document either restoration of the habitat or to plan best the installation considering habitat health.

Example of SiteObserver service for environmental habitat monitoring of an offshore wind park near Rødsand, Denmark. Submerged aquatic vegetation was mapped into sparse and dense categories using Copernicus Sentinel-2 optical data.

Sea ice mapping products are highly relevant for assessing operational risks, planning maintenance schedules, and ensuring the safety and accessibility of OWFs.

Example of SiteObserver service for sea ice mapping and monitoring. Sea ice concentration in this example is estimated using Copernicus Sentinel-1 SAR data.

Coastal zone analysis products are relevant before constructing infrastructure but also during operation to monitor health of the marine ecosystem.

Example of coastal zone monitoring product based on time series analysis of a hypothetical landfall cable zone near Anholt, Denmark. The Copernicus Sentinel-2 time series images are used to assess coastline and sandbar movement.
Added value

Currently, stakeholders in offshore wind are to a large extent relying on more site-based intelligence gathering (with helicopters/drones/ships/etc.) which is both costly and risky. Most stakeholders do not have up to date knowledge on relevant EO based assets or how to access these – therefore satellite data is to some extent underutilized. SiteObserver bridges a technology gap by providing easy access to relevant and context specific satellite data for the exact areas required by the key stakeholders. They get access to the latest satellite derived services and analytics relevant for their sites only and do not need to navigate through, or be familiar with, the vast quantities of different EO assets which are not relevant for their applications.

Current Status

SiteObserver was completed in December 2023.

SaferPlaces

Objectives of the Product

The proposed solution consists of a cloud-based platform to assess and forecast pluvial, fluvial and coastal hazard and support communities with high resolution, timely and accurate flood risk intelligence. The cloud platform provides a digital copy, so called ‘twin’ of the urban catchment, where users can quickly and cost-effectively generate flood hazards and risk maps, allowing the test of the effectiveness of multiple flood mitigation measures, and supporting early warning and flood emergencies.

With the integration of Earth Observation satellite data, the SaferPlaces platform can automatically generate more accurate inputs to the existing algorithms, in terms of flooded areas, rainfall data and DEM generation. By performing calculations in almost real time, with the ability to include climate risks and adaptation plans, SaferPlaces supports multiple stakeholders in improving preparedness and climate resilience.

Customers and their Needs:

Multiple stakeholders, public and private, are involved when flood events happen. They can benefit from punctual, location-explicit and timely information provided by the SaferPlaces platform to understand, respond and plan appropriately against flooding. Specifically, SaferPlaces can help:

  • Local Administrations, like cities and urban planners as they can benefit from improved resilience and mitigation/adaptation plans, to adequately prepare against flooding in their territory;
  • Insurance and Reinsurance companies, in filling the gaps in flood risk scoring and in data availability at high resolution (parcel level) for every location worldwide;
  • Multi-utility and transportation companies, in improving the resilience of their infrastructures;
  • Civil Protection and Emergency Agencies, in supporting their first-response and early warning plans/activities;
  • Climate Tech Companies, in providing high-resolution data with global coverage;
  • Finance institutions (commercial and investment banks), as they need to adjust their risk assessment models with no prior specific knowledge on climate.

The common main challenges faced by all the stakeholders is the uneven coverage of flood data and risk maps at global level, as well as the lack of in-house tools and expertise to deal with flooding.

Targeted customer/users countries

Europe, US, Worldwide

Product description

The SaferPlaces platform already delivers on-demand timely risk assessment data for pluvial, fluvial and coastal flood hazards, by creating a digital twin of any urban flood watershed environment.

The innovation consists in integrating within a cloud computing framework the availability of big open data (climate, satellite, economic exposure and elevation model) repository (Copernicus (Sentinel), Capella Space, ICEYE, COSMO, UP42 Airbus, Google Earth Engine and Amazon) and innovative AI-based flood hazard and damage models with proprietary IPR. The users can generate the required flood risk intelligence with global coverage through a few easy steps and with competitive economic costs.

Within the framework of ESA Incubed, we are developing new functionalities able to integrate satellite data and automatically generate three fundamental spatial layers:

  • High resolution Digital Elevation Models (DEM)
  • Flood water map extension
  • Rainfall Intensity Maps, improving the precipitation data blended with ground data.

These layers are merged within the elastic and scalable cloud framework of the SaferPlaces platform, with the aim to ensure global high-resolution coverage and provide more accurate input data to the existing model component algorithms. In addition, they enrich the platform with the possibility to map in quasi-real time flood hazards, for supporting flood emergency and disaster management.

Added Value

SaferPlaces differs from other products already existing because it offers not only static maps, but a cloud web platform performing calculations in almost real time, with the ability to include climate risks and adaptation plans. In addition, it exploits high-resolution data, fills data gaps when present, and addresses multiple climate and mitigation scenarios.

One of SaferPlaces unique characteristics is accessibility: high-resolution flood risk maps with no need of complex and resource-intensive models. Nevertheless, compared to existing solutions SaferPlaces has other unique advantages: first of all, it allows global coverage, solving the needs of those investing and operating in remote locations; second, SaferPlaces is cost-effective, requiring no computing power or significant pre-existing expertise. Last but not least, SaferPlaces provides climate and resilience-related insights: it offers the customers the chance to see whether flood risk mitigation options work under dynamic urban and climate conditions.

Current Status

SaferPlaces is proud to announce the successful completion of the 18-month ESA InCubed Project, culminating in an enhanced Global Platform, supporting users worldwide in assessing flood risk and making data-driven, flood-smart decisions. By integrating Earth Observation (EO) and climate data with AI-based models, SaferPlaces provides invaluable insights into flood risk.

Innovations and Integrations

During the ESA InCubed Project, significant research efforts focused on integrating Earth Observation satellite data to enhance input layers such as flooded areas, terrain information (Digital Elevation Models – DEM), and rainfall data. This integration has led to the development of three EO-based modules:

  • Safer.001: automatically extracts flood water masks.
  • Safer.002: produces DEMs from Sentinel-1 images using advanced algorithms.
  • Safer.003: creates rainfall intensity maps using PERSIANN-PDIR NOW retrieval workflow, providing near real-time rainfall estimates from satellite data.

These advancements were validated with flood events that happened in the pilot areas of Vietnam, Cesenatico, and in the Emilia-Romagna region, Italy, during the severe flood event in May 2023. The platform demonstrated its effectiveness in generating accurate flood risk assessments and the possibilities for enhancing disaster response capabilities.

The Future in Flood Risk Intelligence

The SaferPlaces platform represents a significant leap forward in flood risk management, offering a robust tool for cities and communities to better prepare for and respond to flood events. The integration of advanced EO data ensures that the platform provides precise, actionable intelligence, enabling stakeholders to make informed decisions that enhance climate resilience, protect people and assets.

IcySea

Objectives of the Product

Navigation in the Arctic and Antarctic can be a hazardous undertaking in a harsh, remote and yet very fragile environment. Finding the safest route through ice-infested waters requires searching for, collating and understanding data from a variety of sources often scattered across the internet. These data are often in formats useful for scientific analysis but not easily usable for nautical staff; file sizes are also prohibitive for the low-bandwidth internet connections common in the polar regions.

IcySea solves this big-data problem by bundling near-real-time scientific sea-ice data into an easy-to-use app in which the user can download and visualise sea-ice information applicable to their current situation on demand and optimised for low-bandwidth connections.

IcySea therefore turns data into useful, relevant and timely information.

ESA InCubed allows the broadening of the capabilities of IcySea to include more datasets and extend the coverage of current datasets, to add useful navigational features, and to incorporate products resulting from previous and ongoing research projects. These additional capabilities will help to make IcySea the essential sea-ice information platform on any ice-going vessel.

Customers and their Needs

IcySea’s customers and users include any party navigating or operating in the polar regions. Such parties include expedition cruise operators, cargo shipping, fisheries, as well as research institutes and surveying companies. IcySea was developed in an open and collaborative manner: everyone in the value chain has an influence on the features provided by the app and any feedback received from end users flows back into the app as, for instance, feature enhancements or extensions.

Because sea-ice information is scattered across the internet and is often in a form difficult for lay people to understand, IcySea simplifies collection and display of relevant sea-ice information to make it useful for navigation in ice-infested waters. Customers thus save time and can focus better on their task of navigating in hazardous conditions.

By having up-to-date near-real-time information, crews can find faster, safer routes through the ice thus saving not only time, but large amounts of fuel and money. Also, better information reduces contact with the ice, thus decreasing hull erosion and reducing the need for costly repairs in the dry dock, as well as reducing insurance costs.

Target customers/users’ countries

All countries involved in shipping in the Arctic and Antarctic.

Product description

IcySea is a progressive web app designed specifically to support polar maritime activities with data from space. It bundles sea-ice information into an easy-to-use app in which users can download and visualise relevant sea-ice information on demand; all optimised for low-bandwidth internet connections.

IcySea’s main innovations include animated ice drift forecasts, automatic ice classification, automatic route optimisation, navigational-support features, and a highly automated backend data processing system.

The customer interacts with IcySea via an app installed on their device (such as a mobile phone or tablet) or via the IcySea web page on a desktop or laptop computer.

Basic structure of the IcySea service:

Added value
Current Status

IcySea is already available and in use by customers in the field. In addition to the data sets present at the beginning of the activity (6km resolution sea-ice concentration data, high-resolution radar images and machine-learning-optimised ice-drift forecasts for the region surrounding Svalbard) we have integrated a 3km sea-ice concentration data product and have extended coverage of radar images to important parts of the Arctic and Antarctic. It is now possible to export ice drift forecast data in either GeoJSON, KML format or as .png files. A measurement tool for route planning is now available as well as Arctic-wide sea-ice drift forecast trajectory information. As of 2024, this InCubed activity is now completed.

IGV

Objectives of the Product

Spire aims at leveraging the latest improvements in the field of signals intelligence and the existing high-performance computing devices onboard its LEMUR constellation to develop processing algorithms enabling the geolocation of radio frequency (RF) signals.

These software-based solutions will be deployed and tested on Spire’s satellites. They will also include ground-based infrastructure for a low-latency reporting of detected discrepancies.

Once they are demonstrated and operationalized, the resulting datasets will be made available through our existing API or a dedicated data feed. It will enable Spire customers to confirm the accuracy and the validity of the geolocation information broadcasted by a given asset. The solution will be completed by an alerting system that will identify and highlight suspicious behaviors. In turn, this will enable them to optimize their operations (e.g., maritime domain surveillance, environmental protection, ship insurance, etc.) based on this information, resulting in lower operational costs and higher operational efficiency.

Customers and their Needs

The main customer segments target by this activity are the following:

  • Law Enforcement Agencies / Anti-Piracy Maritime Security and Sanctions Enforcement / Coast Guards: Perpetrators of illicit activities, notably at sea, will typically spoof or manipulate the AIS or ADS-B tracking system, causing an asset to appear in a different location than it actually is.
  • Environnemental agencies: Overfishing occurs often in areas beyond national jurisdiction. It threatens marine ecosystems and is linked to major human rights violations and even organised crime. Protected-area designations are almost meaningless unless they are backed up by effective surveillance and enforcement.
  • Shipowners and operators: Shipowners and operators are facing increased regulation under OFAC and OFSI, which they must comply with to avoid being sanctioned and facing heavy fines. They must also ensure that their suppliers and customers comply with the rules.
  • Air Navigation Service Providers: More and more geographical areas might be impacted by GNSS jamming and spoofing that could negatively impact air traffic operations.
    • Loss of ability to use GNSS for waypoint navigation
    • Loss of area navigation (RNAV) approach capability
    • Inability to conduct or maintain Required Navigation Performance (RNP) operations

Potential airspace infringements and/or route deviations due to GNSS degradation.

Target customers/users countries

This Service addresses customers throughout the world.

Product description

Spire proposes the innovative development and implementation of a suite of RF geolocation methods onboard its constellation of nanosatellites.

Spire owns and operates the largest constellation of multipurpose nanosatellites, with a unique capacity of running high-performance computing frameworks in orbit that will be used to quickly deliver this geolocation capacity, strengthening its global offering with an independent source of geolocation data.

Beyond the innovativeness of the very techniques used for signals geolocation, which will be adapted for their application in orbit, the proposed developments will create a unique infrastructure. Indeed, the newly developed signal geolocation solution will be ported on an existing constellation of satellites, rather than dedicated formation flying satellites.

This will lead to the commercialization of a unique, highly competitive one-stop-shop solution enabling global and regional analytics of RF signals, that would benefit in the future from an alerting system to highlight any suspicious event. Spire would be the only player collecting and offering RF signal-derived information in an integrated manner.

It should be noted that Spire is the first organisation to have demonstrated that nanosatellites can track global maritime and aerial activity in real time. The collection of AIS, ADS-B, and other RF signals along with its unique constellation puts Spire in a position of strength to train robust and efficient AI/ML-based geolocation models enabling near-real-time monitoring and alerting of suspicious behaviours.

Added value

To date, few small satellite RF geolocation missions have been attempted. The SAMSON mission, supported by the Israeli space industries, (P. Gurfil et al., 2012), was originally planned to be launched in 2018 but has been delayed since then. That same year, a first mission was conducted by HawkEye 360 Inc. In January 2021, they announced that their second cluster of three small satellites had been deployed into orbit, opening the idea that such technology is feasible, but still using small satellites (about 30 x 30 x 45 cm) (HawkEye 360, 2021), leaving the path to nanosatellite and CubeSats missions still unexplored. The Pathfinder mission is undoubtedly a high-performance system, but with it comes high costs. In addition, it relies on propulsion systems to maintain its cluster together. On a similar note, since 2020, Kleos Space and Unseenlabs have been competing for the RF reconnaissance market providing maritime situational awareness only. Taking advantage of our experience, Spire has the ability to rapidly provide a cost-efficient and less complex system that addresses the needs for RF signals geolocation with the right compromise on refresh rate and sensitivity. In addition, Spire’s existing infrastructure offers a quick and easy path to scalability, from demonstration to an operable system in a short time.

Current Status

All the hardware and software licences for the on-orbit deployment of existing methods have been procured and set up. The ground-based algorithm has been successfully ported to a virtual environment. An initial standalone test was also successfully completed for both environments.