The use of solely Earth Observation, helps to derive actionable insights for many of the new business applications. With more and better EO-data by now we succeed in monitoring a very large number of outdoor objects and inform customers on relevant changes. However even when using the newest very high resolution optical and radar sensors, there is lack of validation or comments by a human. By assimilating existing quality checked (open) data like crowd sourced data and official texts (e.g. permits), we can add significant value to EO-derived information in e.g. urgency, context and implications. Through SINERGI we add value to the changes we detect on EO-data with information from Big data sources by using technology for semantic integration and object ontology standards.
Everything changes continuously on this planet. So, information outdates quickly. Causing poor decisions and misfit actions. Our clients, who make the transition to data driven decision making and data driven business, need up-to-date, complete and reliable information to follow environmental, social and corporate governance (ESG). Many policy and businesses applications lack data and actionable insights to make the right decisions to follow ESG.
The key customers segments targeted by our product are the value added service customers like Forest Law Enforcement (Nature Protection Law Enforcement), urban green and tree management, environmental Inspection Service and building Insurance.
In recent years, NEO developed the SignalEyes platform for Earth Observation (AO) based nationwide information services. The figure belo shows the SignalEyes method of information provision with EO data and its learning element.
In SINERGI we develop an ontology based method to use Big Data to add value to earth observation data and apply/test it to monitoring of trees, shrub, invasive plant species and buildings. The solutions rely on the existing SignalEyes
infrastructure, a system that enables the monitoring of geographical objects using EO data for changes relevant for a wide range of applications.
The role of the SINERGI solutions in the context of the overall system of its target users is to provide the necessary insights on properties of objects on the Earth’s surface based on the most recent relevant data extracted from EO and non-EO data sources.
The components and component elements that will be developed in SINERGI provide the SignalEyes system access to a much wider range of data sources, allowing cross-checking of data-derived insights from multiple angles, leading to more reliable, more complete and more up-to-date information.
The semantic integration of Big data using ontologies is a step that is still new to the EO-services development. The added value of an EO product increases very significantly, when this integration is achieved successfully. In the described services, the value of the EO-service increases by an estimated factor of 3-5. The value of sales to our current customer base may multiply. Much of our services are limited to The Netherlands. However NEO is an EARTH observation services company, working on 4 continents and with an increasing focus on international markets.
Through SINERGI earth observation data will be more used in business applications. Data that is now by itself not directly fit to derive actionable insights for certain customers, become valuable when combined with other data. When competitors adopt similar working methods it is likely that EO-based services as a whole become more valuable leading to sustained or increased growth (as generally predicted for the value adding sector). In our data driven world, earth observation data, if combined with other data, will play a bigger role in providing better information resulting in better decisions for a better planet.
The SINERGI activity is started 3 month ago and in this starting period focus was on the comparison and decision of RDF store implementation (Virtusoso). We finished the (choice independent) design for this. Regarding the ontology we made a start with an inventory of relevant public ontologies and started the conversion of our NEO object definitions.