Going further than just producing water quality data, the EOSmart solution is capable of transforming global water quality data into actionable information. This massively simplifies data access for water managers, allowing them to condense data to business-relevant information and integrate EO-based measurement in routine operations.
Customers and their Needs
Customers are starting to include data sources such as remote-sensing data, but need to manage the increasing data volumes up to analysis, and report it in a more efficient way.
An easy-to-use web interface allows the customers to quickly gain an overview of the water quality in their water bodies and areas of interest in coastal regions. Comparison to in-situ measurements and the fast information about thresholds being exceeded are crucial to react to unwanted developments in the respective water bodies. Statistical analysis tools for longer time series are needed to comply with reporting duties on the water quality of lakes and rivers.
Targeted customer/users’ countries
EOSmart is useful for customers all across the globe, as the smart analytics and services are location-independent.
A specific focus is set on:
Water authorities (e.g. bathing water monitoring and alert)
Water Industry (e.g. coastal construction)
Product description
The EOSmart product and services are tailored water quality information on a global scale, from novel very-high to high spatial and temporal resolutions, served in the most automated and customer-oriented way through a commercial online platform.
The provided services are
Baseline analysis: the satellite images range back years to decades and enable us to provide a sound analysis of long-term trends and seasonality
Monitoring: daily updates of the current state of any water body in the world
Alert: automatic notifications when user-defined thresholds are exceeded
Added Value
Water quality information at your fingertips – easy and super-fast access to global water quality information. Dashboards and analysis tools transform the data into actionable information and can support decision making (e.g., an environmental agency closing a lake due to high and rising cyanobacteria observations).
Current Status
The final product “eoApp AQUA” was launched in May 2024 and now serves customers around the world. A marketing campaign and sales forces are part of the ongoing go-to-market activities to grow the market share in the close future.
The objective of the GEMSTONE product is to apply our proprietary state-of-the-art Artificial Intelligence / Machine Learning algorithms on Copernicus’ Sentinel-1 & Sentinel-2 satellite imagery to provide our customers with actionable insights on various industries. All insights will be then displayed on a user-friendly online platform.
Customers and their Needs
The key customer segments targeted by our product are as follows: banks, hedge funds, insurance companies, governmental institutions, the automotive industry, and the energy sector companies. Since GEMSTONE is planned as a very universal product, there are the following three common problems shared by all customer segments. The first of our customers’ problems is, that they currently struggle to obtain actionable insights on particular segments in a timely manner. Utilizing satellite data, the time period from an observable phenomenon happening in a particular industry, to our customer having an actionable insight, shortens rapidly. The second problem of our customers’ problems is, that they cannot fully rely on the data they use for economic indices creation. The data can be easily biased by governments, industry companies, or any other stakeholders. On contrary, the satellite imagery is an unbiased information source by nature. The last of the major problems our customers face is the accessibility of actionable insights. Currently, analysts have to actively search for the information, using a wide spectrum of data sources. With GEMSTONE, all necessary information will be accessible on a single online platform.
Targeted customer/users countries
Targeted customer segments and their needs are described in the previous section. Product description. There are already existing customers using our GEMSTONE product that are providing valuable feedback to further development. To name a few: JP Morgan Economic Research Department, Credit Suisse, Bridgewater, Exxon, or Deloitte.
The nature of our product allows us to make the solution fully scalable and sell to customers anywhere on the planet Earth.
Product description
SpaceKnow’s GEMSTONE (Global Economy Monitoring System delivering Transparency and Online Expertise) aims to develop an easy-to-use and scalable platform providing insights on the performance of selected industries and commodities supply chains based on indices computed with state-of-art algorithmic solutions on satellite imagery. With our GEMSTONE the customer will be able to get instant access to both location-specific knowledge as well as country-wise or industry type aggregated information that allows users to query activity at a ‘topic’ level (coal facilities, oil wells, etc). Clients can also provide locations of interest (e.g. the points of a specific company or product supply chain) and SpaceKnow data can be accumulated at those locations.
The key customer segments targeted by our product are as follows: banks, hedge funds, insurance companies, governmental institutions, the automotive industry, and the energy sector companies. Despite the fact that GEMSTONE is a very universal product, there are the following three common problems shared by all customer segments. The first of our customers’ problems is that they currently struggle to obtain actionable insights on particular segments in a timely manner. Utilizing satellite data, the time period from an observable phenomenon happening in a particular industry, to our customer having an actionable insight, shortens rapidly. The second problem of our customers’ problems is that they cannot fully rely on the data they use for economic indices creation. The data can be easily biased by governments, industry companies, or any other stakeholders. On the contrary, satellite imagery is an unbiased information source by nature. The last of the major problems our customers face is the accessibility of actionable insights. Currently, analysts have to actively search for the information, using a wide spectrum of data sources. With GEMSTONE, all necessary information will be accessible on a single online platform.
Added Value
The added value of GEMSTONE is practically demonstrated on the three of our customers’ main problems and their solutions in one of the previous sections. The main added value is thus in the provision of unbiased and timely actionable insights, in a user-friendly manner.
Current Status
The GEMSTONE project has come to an end. SpaceKnow has finished the development of the machine learning-based economic indices derived from satellite data, mostly from European Sentinel satellites.
SpaceKnow is happy to announce that all defined objectives of the project have been met. The quality of the final product has been evaluated and approved by stakeholders from industry, who will be the users of the product. The evaluation of the final product was very positive and that is one of the reasons why the development of GEMSTONE will continue throughout the following months. Based on market research and the latest global developments, new features and indices will be added, for instance, an algorithm for detection of ships on Sentinel-2 imagery, which enables better supply chain monitoring, a critical feature for many due to the recent global supply chain crisis.
We combine EO data and AI tools in order to identify new business cases and develop customised solutions. The AI4EO Solution Factory is a knowledge base and module repository that allows us to reuse expertise and software throughout the development process.
Customers and their Needs
Potential customers are active in one of the many application areas of EO data, such as agriculture, forestry, fishery, natural disaster forecasting/monitoring and urban planning. They have a problem statement and are looking for solutions based on EO data and AI tools. Our team will develop a business case around this problem statement and develop a solution jointly with employees from the customer.
Targeted customer/users’ countries
Due to the geographical closeness, we focus on European customers, but are also open to customers from outside the EU.
Product description
The AI4EO Solution Factory is a collaborative environment between specialists from DFKI and domain experts from our customers. With the AI4EO Solution Factory we enhance the visibility of EO solutions and provide our AI and EO expertise to our customers, thereby enabling them to discover and exploit new business cases. The central mechanism for the products of the AI4EO Solution Factory is to combine the domain knowledge of our customers with our AI and EO expertise to develop AI-based algorithms for information extraction from satellite images. We develop custom-made solutions in close collaboration with our customers and then reuse the underlying building blocks for additional products of the AI4EO Solution Factory. This approach of reusing the underlying building blocks while providing specialised solutions for each of our customers enables us to provide AI4EO solutions for all kinds of business cases and small, medium and large companies, as well as public entities alike.
Added Value
The Solution Factory joins the latest cutting-edge AI technologies with EO data to tackle identified problems within different areas of commercial focus. The key innovation is our Transfer Lab approach, where we directly involve employees of our customers in the product development. This close collaboration ensures that the developed products are a perfect fit for the customer’s business case and helps to build trust with the customers.
Current Status
We are developing customised solutions for different Earth Observation use cases within the AI4EO Solution Factory initiative. In a joint project with our anchor customers BASF, John Deere and Munich Re, we have developed a product for agricultural yield prediction.
By collecting a large dataset of ground truth data from combined harvesters, we have successfully developed yield prediction models for Germany, Uruguay, Argentina, Brazil, India and the US. The product has been successfully deployed to our customers.
The next step is to expand our work to additional application domains with new customers.
The FIPC has been specifically designed to address the growing onboard data bottleneck of EO satellite missions from a complete end-to-end systems perspective.
This is achieved primarily through a state-of-the-art Applications Development Framework which enables a software defined onboard data processing pipeline. Onboard data processing increases overall satellite bandwidth and data timeliness through direct data reduction and increased onboard intelligence. The objective is to provide users with actionable information much faster than traditional means and in turn stimulate new remote sensing applications which benefit from greater efficiency in the collection and processing chain. The framework not only enables the use of SSTL processing IP but also custom and third-party IP.
Direct data reduction techniques include data calibration, compression and image thumbnailing. Onboard intelligence includes image classification, segmentation and object detection; these enable high level information extraction for new data products and autonomy.
Additionally, the bottleneck is also addressed through a new, intelligent, high throughput data downlink system which uses variable modulation schemes and coding rates to utilise excess positive margin to increase the overall downlink throughput by up to 100%.
Customers and their Needs
The Earth Observation (EO) sector is undergoing a paradigm shift where technology advancements & new innovative payload designs are producing data with increasing dimensionality, volume and rates. In this new “Big Data” era a prominent challenge surrounds efficient and timely information delivery to end-users. In recent years, advances in satellite downlink technologies alone have not been sufficient to offset the increase in EO payload data volumes, resulting in the formation of an onboard data bottleneck.
This data bottleneck has a range of negative impacts to the customers. Principally it causes an increased delay between data capture and delivery. This delay has a wide range of knock-on impacts up and down the complete value chain, whereby data end-users can see the value of information decreased where required for time sensitive applications. Satellite operators can see increased delays in operations tasking and decision making. Satellite manufacturers need to address increased power, storage and complexity requirements of the satellite platform itself.
The bottleneck therefore can also have knock on impacts to higher level customers’ needs such as a negative impact on the customers overall business case due to increased missions costs. Costs are often increased by a need for greater ground station infrastructure or passes, increased satellite CapEx cost incurred to provide the required data volumes, resolutions or revisits.
Eliminating the onboard data bottleneck and time delay is a clear and urgent EO industry-wide opportunity, which, if successfully addressed, can provide a significant competitive advantage and stimulate new, time-sensitive or data heavy applications.
Targeted customer/users countries
SSTL primarily addresses the small satellite market; the FIPC will be included as part of SSTL’s small satellite mission offerings. The FIPC provides a significant advantage across SSTL’s mission offerings from the low cost CARBONITE series to SSTLs High Performance mission propositions, such as “Precision” and “Wide Swath”. Where previously the data bottleneck presented a significant challenge, with the FIPC customers are able to download larger volumes of data without cost prohibitive ground segment solutions. By decreasing the cost of the ground network needed to support the spacecraft, commercial business cases will become much more attractive to a wider range of customers.
Craft Prospect Limited (CPL) and their onboard Autonomy-Enabling Components (AEC) toolbox will be promoted as part of a staged product offering. Within the small satellite market, CPL will partner with SSTL to promote software AECs to enable and augment onboard data processing for SSTL smallsat missions.
CPL will offer three main tiers of products within the nanosatellite and spinout markets: the first tier will comprise the software AECs as standalone or packaged applications which can be deployed on third-party hardware or used in ground applications. The second tier will comprise dedicated hardware offerings, comprising the AECs and additional data processing functions optimised and deployed on low-power NewSpace components such as FPGAs and VPUs. The third tier will take the form of a full Autonomous Mission offering.
The University of Surrey are actively researching “big data” problems found in the context of the wider EO industry. One such avenue of research applicable to the FIPC product is the innovative use of autoencoder algorithms. Such algorithms can be deployed onboard the FIPC to extract contextualized information from EO data far quicker than traditional supervised learning approaches. The University of Surrey will demonstrate their novel algorithm developments and the new machine learning enabled customer use-cases.
Product description
The FIPC product consists of the space hardware required for; payload interfacing, data capture, data storage, real-time and offline onboard data processing and also the satellite RF downlink. However, the FIPC is more than just space hardware; the software defined onboard pipeline & applications development framework are critical components which enable end-user tailored & high performance functionality.
Overall, using a number of innovative techniques, the product is designed to maximise the achievable satellite throughput towards increased timeliness and volumes of useful payload data delivered to EO data end-users, thereby increasing value for money to the customer.
Added Value
EO data customers are increasingly demanding faster, cheaper, higher quality and greater volumes of data – this is a key challenge for EO satellites which is primarily limited by the achievable downlink throughput of the satellite. The FIPC leverages several different technological advances to increase the capabilities of the space segment and these are used to addresses customer demands from several different angles.
Firstly, an adaptive high data throughput downlink sub-system is provided to maximize the raw data transmission rate of the satellite.
Secondly, a number of new and novel approaches to onboard data processing are implemented at the front end. These can be used to reduce the volume of data needing to be sent via the downlink. Thus overall the product increases the volume, quality and timeliness of payload data to customers, providing better value for money.
Onboard data processing offerings include IP designed by SSTL, CPL and the University of Surrey.
Thirdly, The FIPC Applications Development Framework will allow customers and other third-party developers to deploy their own custom onboard data processing IP onboard an SSTL satellite allowing for highly optimised solution of EO data end-users.
In addition, the product is scalable across the range of SSTL satellite platform classes from 10s of kg to 100s of kg, whereby the FIPC offers utility across many types of remote sensing mission.
Significant inefficiencies result from the human-centric approach to satellite operations. Latency, short communication windows, and costly downlinks are all detriments to the mission’s effectiveness.
To address these problems, AIKO has developed orbital_OLIVER, an onboard automation software that augments spacecraft performance and reduces mission operations costs, opening up new opportunities in the use of space.
orbital_OLIVER analyses data from the satellite and its operational environment to devise and execute a dynamic schedule of tasks. Therefore, autonomy-enabled capabilities allow satellites to perceive and react to unexpected events, lowering operating costs and improving service quality
Customers and their Needs
The customers of orbital_OLIVER are spacecraft manufacturers and operators. Their needs include:
Optimising the use of in-space and on-ground resources
Increasing the quality of services or products to become more competitive
Reducing operations’ costs
Overcoming bottlenecks from human-in-the-loop approaches
Responding rapidly to unexpected events
Increasing the satellite lifespan.
Targeted customer/users countries
AIKO targets satellite manufacturers and operators across the world.
Product description
orbital_OLIVER is a software that enables in-space mission autonomy for satellites.
orbital_OLIVER uses event detection and pattern recognition technologies applied to payload and telemetry data to make decisions independently.
The software abstracts a simple cognitive architecture from complex space systems, providing satellites with the ability:
To sense the environment and its status (through onboard data processing);
To plan tasks according to acquired or inferred knowledge and update the mission schedule (operations planning)
To execute tasks according to the updated mission schedule (dispatching).
orbital_OLIVER has been successfully tested for x86-64 and ARM computing architectures. Moreover, processing modules are compatible with a wide range of hardware accelerators (including Intel Myriad, Google Coral, and Nvidia Jetson), resulting in several key advantages:
orbital_OLIVER enables autonomous satellite operations, overcoming the limitations of human-centric spacecraft operations. The independence from ground control paves the way for benefits such as reducing mission operating costs, increased activity lifespan, and optimised use of resources. orbital_OLIVER will also become the pillar for the logistical scalability of novel constellation architectures, in which hundreds or thousands of satellites will operate collaboratively to reach mission goals.
Current Status
The InCubed programme officially began in May 2021 and was finished in July 2023. In this timeframe, AIKO improved the technical soundness of orbital_OLIVER by testing it in real operational scenarios and gaining flight hours.
AIKO had been running the Early Adopters Program (EAP) for this endeavour. This program granted selected partners early technological access to orbital_OLIVER, ensuring the product’s compatibility with potential customers’ needs. In the context of the EAP, AIKO acknowledges the support from Tyvak International, the UK branch of D-Orbit, and UNIBAP.
The culmination of the EAP has been a series of in-orbit demonstrations carried out onboard one of the ION Satellite Carrier OTV by D-Orbit. These experiments validated orbital_OLIVER’s functionalities in an operational scenario, demonstrating its added value in improving mission efficiency and regulating the optimal usage of onboard resources.
AIKO leveraged the InCubed development resources:
To complete the product development roadmap, prepare it for commercial exploitation
To conduct required qualification and testing campaigns
To acquire flight hours and training data in diverse scenarios to optimise the machine learning models
To verify and validate the product on the use cases identified during the EAP.
AIKO has recently started commercialising orbital_OLIVER, rebranding its original name MiRAGE (Mission Replanning through Autonomous Goal gEneration) and updating the product description.
Cube4All aims at a commercial Earth data service leveraging the advantages of open standards-based datacube analytics. It makes complex EO tasks simple, keeping simple tasks simple, and unleashing EO analytics and fusion for non-EO/non-IT experts while increasing the productivity of experts.
The resulting service will stand out through:
genuine datacube services on several public Copernicus and further data offerings, including DIASs, CODE-DE and further large-scale data archives in a seamless manner.
allowing customers to either rent these services or rent their own pre-confectioned datacube service (private or public) on owned data, or any combination.
a more user-friendly approach, probably convenient for IT/EO experts and non-experts alike, from extraction to analytics without any programming.
a seamless integration of Copernicus and INSPIRE data.
providing configurable access control for data offered by customers.
offering a particularly flexible, fair, and attractive billing model.
APIs strictly based on the OGC/ISO/INSPIRE standards, WCS, WCPS, WMS, and OAPI-Coverages.
being operated on a completely open-source software.
Customers will be able to access existing archives in a massively simplified way as curated datacubes, without any programming skills. This allows users without IT and EO coding expertise to unleash the potential of the mass of EO data assets.
In any case, from a user perspective, services stand out by simplifying access to Petabytes of Sentinel timeseries, climate variables, DEMs, and thematic products for location-transparent mix-and-match without programming; proven real-time performance; wide range of common tasks prefabricated, continuously extended; versatile analytics, from standing queries to exploratory – any query, any time; fully customisable workflows; transparent and competitive pricing. Users remain in the comfort zone of well-known clients.
Customers and their Needs
Customers who need analytics of EO data in space and time, without having their own raster data.
Analytics of EO data in space and time, mixing owned and external raster data.
Customer’s need to have their own raster data easily accessible (inhouse or externally), but do not have the skills or time to maintain a server.
Customers who need to engage Big EO Data in proprietary algorithms, including proprietary data, with demanding and changing customer requirements.
Targeted customer/users’ countries
Worldwide
Product description
Approach: The existing DIAS, CODE-DE, and other public rasdaman datacube services established in research projects (ex: https://processing.code-de.org/rasdaman), with their existing WMS / WCS / WCPS capabilities, will be more user-friendly and e-commerce will be ready for rasdaman-as-a-service.
Features to be added include a prototype of a novel, interactive data analytics technique, Query-by-Dialog (QBD), which aims at enabling users to do analytics without coding, without even writing high-level queries.
Added Value
Cube4All will make complex tasks simple and keeping simple tasks simple. It unleashes EO analytics and fusion for non-EO/non-IT experts while increasing the productivity of experts.
Current Status
Cube4All has substantially helped the rasdaman SME to shape and extend its business model and hence optimised its success chances in the EO market and beyond. As of August 2024, this activity in now completed.
EO-WIDGET is a modern fully cloud-based, highly automatized offering of Web-based information services utilizing high-frequency satellite Earth observation (EO) data supplies, which CAP Paying Agencies can integrate into their Integrated Administration and Control System (IACS) set-ups to achieve individual implementations of the Checks by Monitoring (CbM), and Area Monitoring System (AMS) respectively, customized to local needs and for operations based on Service Level Agreements (SLAs).
The EO-WIDGET project carries further Sen4CAP prototyping efforts and algorithm developments based on Copernicus Sentinel EO data input into operational service provisioning via provider-managed Application Programming Interfaces (APIs). Through the APIs, a well-defined portfolio of ready-made monitoring data products is made accessible. For the consumers, this makes the need for managing very large EO data repositories and setting up of complicated processing chains a story of the past.
The monitoring data products are specified according to processing levels and are aimed to be established as de-facto standards within the domain, covering the EO derived input for all relevant use cases included in CAP and environment monitoring practices.
Furthermore, interactive, and graphical user interface elements, so-called “widgets”, are offered as Open Source Software supposed to be bundled, customized, and configured by service providers into customer-facing Web Applications (Apps). An Expert Judgement App (Parcel Explorer; Quality Assessment Tool) is provided as white label software templates for enhancing productivity of value-adders (Paying Agency contractors, value-adding resellers, farm advisors).
Also, a cloud-based data management platform is established as a service for secured, interoperable exchange and staging of the LPIS and GSAA data which are data required in the generation of the monitoring data products and in the widgets for their visualization. Additionally, EO data stemming from commercial sources (Planet Fusion products) are managed on the platform and assimilated into the monitoring data processing selectively (optimizing cost to customers), e.g., for coping with the “small parcel issue”.
Customers and their Needs
The EO-WIDGET initiative targets three main Customer Segments clearly differentiated according to their use of the EO-WIDGET System:
Customer Segment 1 CAP Paying Agencies (PAs): In the near future, PAs have to phase-in information management tasks of unprecedented complexity involving multiple-orders-of-magnitude larger Earth observation (EO) satellite data volumes to fulfill their reporting obligations (e.g. CAP). This clearly positions the PAs as the main Customer Segment for EO-WIDGET Services
Customer Segment 2 Other Agencies: A number of governmental organizations other than the PAs have similar and increasing needs of EO services for monitoring and reporting purposes: Environmental Agencies (EAs), public sectorial departments (Water, Forest, Agriculture, Urban Affairs, etc.) and the EU Court of Auditors.
Customer Segment 3 Advisors: Farm Advisors assist farmers in improving agronomic performance while reducing fertilizers cost and environmental impact. They also provide support in coping with the increasing digital requirements imposed on farmers by Farm Information Systems (FIS), and PA reporting systems.
The largest impact is expected with European Paying Agencies given the window of opportunity that arises from a legislation that requires each Member State to setup a system to continuously monitor all agricultural parcel by 2023.
Targeted customer/users countries
Global
Product description
Credits: EO-WIDGET consortium
The consortium operates and offers a set of EO-based Monitoring Products, compliant with the latest IACS and CAP regulations, rich quality assurance support within an SLA-based IT Cloud environment. The products are visualized in a versatile graphical user interface and are customized to match the local requirements of each customer.
A cost-efficient integration of VHR data and sound methods lead to a significant decrease of so-called inconclusive (yellow-marked within a commonly-used traffic light system) in comparison to previous solutions.
Data as a Service: On-demand Managed Services are provided for:
EO satellite data discovery & ingestion
pre-processing
generation of signal-based monitoring products
wall-to-wall, whole season, coverage
Widgets: mini-applications are available for:
visualization of monitoring products (expert judgment)
quality assessments
building of Web Apps
re-use (open source software) and customization
Hosting: Protected cloud workspace is offered – individualized per Paying Agency for:
deployment of Apps & tools
storing of declaration data; configurations; and monitoring products
Small parcel analysis: Cost-efficient VHR integration
based on Planet Fusion data
dedicated pricing scheme
integrated into product workflow.
Credit: EO-WIDGET Consortium
Added Value
Currently only standalone solutions, which are not scalable nor open to new developments, are available on the market. These solutions are exclusively targeting directly the end-users. So far, no supplier has been focusing on the needs of the ICT industry servicing already public end-users and the related provision of a ‘user centred’ service concept. In EO-WIDGET available services are being integrated, thus keeping the value chain open to new solutions offering flexibility and scalability. The EO-WIDGET initiative provides for the first time in the EO industry a service concept to specialized ICT providers, where end-users may access the benefits of operational EO data services within simple to embed widgets.
EO.WIDGET offers the effective implementation, integration and operations of Checks-by-Monitoring and Area Monitoring System as a contractor-operated service to European Paying Agencies and their incumbent contractors.
The EO-WIDGET consortium addressed all technical challenges and is now ready to serve Paying Agencies throughout Europe with a clearly defined set of tested and validated monitoring products, corresponding quality reports in an IT cloud setup that matches all required security standards and is still flexible to match local needs.
Current Status
The activity is closed with December 2022 and is now continuing as an operational service and expanding into the market and participating in relevant public tenders.
Re/insurance and risk modelling companies suffer a serious lack of high-quality per-building data. Nowadays, they build their analysis upon aggregated and statistical datasets, which are characterized by low quality, obsolescence, and coarse resolution. Lack of high-quality property data implies poor risk estimation, which may translate into large, unexpected losses when a disaster happens. Moreover, uncertainties in risk estimation are frequently offset by increasing the policy premiums to final customers, and thus decreasing the insurers’ competitiveness in the market. The effects of climate change contribute to exacerbating the problem, and statistics have indeed shown an increasing trend for losses at global scale. The number of events per year ramped up from 249 in 1980 to 820 in 2019. Since 1980, the total losses due to natural disasters have reached 5,200 US$ billion. Deep Property is tackling this issue by providing detailed, on-demand, high-quality building data at the global scale. It applies proprietary AI-models to geospatial datasets, like satellite and street-level images. A SaaS model is offered, leveraging an API-based infrastructure. It provides a fair pay-per-use scheme and, most importantly, a smooth integration into the customer’s environments. The entire service runs on the cloud.
Customers and their Needs
The key customers segments targeted by our product are: Insurance, Reinsurance, and Risk modelling companies. All share a high-level need, namely high-quality building-scale property data, while using the purchased data for different applications.
Insurance companies need property data for three main applications: identification of a fair and competitive price, policy renewal, and claim management. Reinsurance companies need property data to better assess the risk for a large portfolio of buildings. Risk modelling companies use detailed data to improve the quality of their models, and thus increase the value in front of their customers.
Property data include several different pieces of information. Among these, there are: area, roof type, construction year, overhanging trees, greenness index, solar panels, presence of swimming pools, flood barrier, number of floors, material, occupancy, building type, first-floor elevation, basement, maintenance status, etc. The relative importance of each element changes in accordance with the addressed geographical region, e.g. in hurricane-prone areas, information about roof type is highly valuable.
The main stakeholders involved in the activity will validate the product considering both technical and business point of view. More specifically, a set of analysis will be done to assess the added value provided by Deep Property.
Targeted customer/users countries
Deep Property can provide data about individual buildings anywhere, at a global scale. However, the main regions of interest for identified potential customers are European countries and the US.
Product description
Deep Property is a service able to derive property features by applying proprietary AI-based techniques over several geospatial datasets including satellite data, street-level images, smartphone pictures, etc.
Deep Property is offered through a pay-per-use SaaS scheme. The data is requested through an API scheme, where final users submit latitude and longitude of the property location of interest (or its physical address), together with a list of desired features. Once the submitted data is received, Deep Property retrieves the relevant geospatial datasets, then it applies the AI-based models to extract the requested pieces of information, and finally delivers the analysis results to the final customers. It should be remarked that the API-based scheme automatically updates information in the customer’s database, thus making integration of DeepProperty much smoother.
This is key to the effectiveness of DeepProperty, because the system operates in a transparent manner, and the customer does not need to learn new platforms and operations. The guiding idea is to let our customers work as usual but with better data.
An overview of the processing workflow is available in the figure below:
In the end, a full cloud-based solution allows to scale up easily when dealing with large amounts of requests.
Added Value
The lack of property data is a problem to which many companies worldwide try and offer a solution. Most of them, however, focus mainly on satellite images and drones as their sources of raw data.
Satellites provide a clear overview of the buildings from a nadiral observation point, and thus are indeed useful to retrieve some physical features of the buildings (e.g. footprint, roof-type, etc.). Yet, satellite images are insufficient to retrieve all risk-related features.
Drones provide a wide range of vantage points, but their coverage is extremely limited, and new, specific acquisitions are generally required for any new building mapping operation.
In our case, unlike our competitors, we use satellite images combined with street-level images. Whereas satellite images provide data on footprint, area and roof type, street level images cover several additional exposure-related characteristics. The combination of the two data sources guarantees a far more complete portfolio of data for our customers. Many data providers are acquiring street-level data for road sign mapping and self-driving car applications, thus guaranteeing good coverage in many urban areas as a by-product; such data can be re-used for our service.
Current Status
The activity officially kicked off on June 30th, 2021, and it has been concluded on April 30th, 2023.
Through the utilization of proprietary AI models, the service has the capability to analyze geospatial data and provide more than 10 building features in nearly real-time. The service operates entirely on a cloud-based framework and manages requests through an API-based interface. Moreover, new features can be integrated easily using a plug-and-play approach.
The Deep Property service focuses on various markets where property data is valuable. These markets include insurance, real estate, energy, and ESG rating assessments.
Mayday.ai is a centralised and artificial intelligence-based platform providing real-time and near real-time disaster and risk information services. We provide early warning and two-way communication services within the same ecosystem by leveraging Satellite Imagery (Geostationary, Polar), Camera Imagery, Audio, as well as Social Media Sentiment analysis.
Customers and their Needs
Government agencies/organisations dealing with emergencies, humanitarian and development aid
(Re)insurance industry
Utility
Citizens
Targeted customer/users’ countries
Global
Product description
A platform powered by a data-agnostic AI fusion engine that provides actionable insight for disaster and risk management in real and near real-time.
Added Value
Real- and near real-time insights, allowing proactivity and ultra-fast reactions (sometimes hours and days before disasters are reported through traditional means)
Centralised disaster management (information is consolidated, standardised and localised, from decision-making to execution levels)
Several disaster types and management phases covered
Dynamic risk management information services (risk profiles for locations are dynamically adjusted through an influx of data and machine learning)
HubCAP is an ‘Application Platform’ for the agriculture and environmental sectors. It is based on Copernicus data and the EO toolkit built by Compass Informatics Limited, making it a highly flexible system. It can be accessed directly through the platform or through a comprehensive API for monitoring via direct integration with existing administration systems.
HubCAP was envisaged as an application that would:
Provide accessible information for land use and land cover management in an accessible and repeatable manner.
Build upon ESA generated satellite imagery and applications to lower the cost of analysis and application ownership.
Provide reliable and transparent workflows for decision making.
Deliver an intuitive application that serves the business expert, rather than relying on the technical specialist.
Enable non-technical users to initiate and view land use and land use change in a wide variety of scenarios using satellite imagery.
Aid the process of validating financial claims, review land use and land use change more efficiently.
HubCAP demonstrates the ability to combine Compass Informatics’ expertise in EO, Location Technologies and GIS to deliver a user focused, application with the potential to deliver.
Customers and their Needs
HubCAP is a robust and simple platform targeting users from Government Agencies, (CAP, non-CAP Paying Agencies and Environmental Agencies) and commercial clients. Users can access the benefits of Sentinel data in a fully supported, legally recorded and compliant manner. These user types are in constant need of custom land monitoring as Europe has a wide range of types of lands and therefore different monitoring needs.
Targeted customer/users countries
All European countries
Product description
The HubCAP service consist of two modules, each with a differing start point:
1. EO Module Based upon results of other activities (Copernicus data Space Ecosystem, Agricultural monitoring needs) with an innovative EO algorithm for Grazing and Bare Soil Detection. The EO Module orchestrates the download, and processing of Sentinel-1 and 2 imageries, and the execution of markers against that data for specific land parcels. Processing is optimised for data reuse and stability.
2. Bureau Module Based on a validated user concept and actions derived from the EO module outputs. This includes a viewer to visualise the land use and land use change with interaction of several data widgets to allow visualisation of results. The Bureau module also allows users to configure processing runs in an intuitive manner, gives users transparency on progress of processing, and configuration of results display.
Overall, HubCAP is a scalable application for commercial engagement with public and private sector clients.
Added Value
The HubCAP service is:
Simple – has an intuitive UI that allows non-expert users to initiate and schedule their assessments, supported by a comprehensive API for monitoring via direct integration with existing administration systems.
Wide – has an advanced dashboard for conducting bespoke ad-hoc local analyses with below advanced features:
Upload and draw polygon functionality for individual and batch processing
Map-centric view showing national RAG status
Syncing of map and data lists, for easy interpretation
Visualisation of parcel-level signal timeseries and imagettes views in sync
User scenario analysis (stored outputs as “Layer”)
Configurable business logic to reflect national/EU schemes
Administrative access for user management, permissions and configuration
API Data services for integration into downstream systems
Audit history and metadata for process transparency and end user communication
High-performance and scalable processing, with reliable cost models
Supported – Is fully supported and customer focused.
Transaction Certified – each classification ‘transaction’ is fully recorded and certified
