REACH startup CyStellar aims to create a predictive model of increase/decrease in the different types of emergency situations

Hi CyStellar! Can you present your team and company to us?

 

CyStellar is a down-stream geospatial intelligence company that primarily targets its offering to the financial sector,  and more recently, civil protection services. CyStellar’s cloud-based data analytics platform integrates satellite  imagery, near-real time weather data, social and political data, and natural catastrophe hazard data to remotely  detect, classify, and monitor risk. The platform combines all the data into a user-friendly, customized, accessible  space, so that users can continuously and remotely monitor risk.

Utilizing historical Civil Protection data regarding mobilisations, interventions, emergency events and weather alerts  in the city of Bilbao, CyStellar’s aim is to obtain a predictive model of the probability of an increase or decrease in the  different types of emergency situations in a 15-day time frame, with the aim of providing support to managers when sizing emergency teams.

How did you learn about REACH Incubator and what made you apply?

 

At CyStellar, it is a fundamental principle that every dataset has a story to tell. When presented with the prospect to  delve into the data provided by Bilbao’s City Council, we were drawn in by curiosity to explore the data and the  desire to provide a solution for the Firefighters of Bilbao. So, we did what we do best. We understood the data for  what it was, an emotional language.

Our duty in this program was to identify what the data was trying to tell us, and render it in such a way, so that it  could genuinely help the Civil Protection Service.

In this case, the city needed a technology that could assist in the operation of the Bilbao City Fire brigade. And we  have succeeded in providing a solution to predict the location of emergency situations in a 15-day time frame. Using  our technology, we were able to apprehend what the data was telling us, highlighting where and when emergency  situations would occur, and communicate the results, simply and effectively via our platform.

You are tackling Bilbao City Council’s challenge Predictive Model Analysis. How do you think experimenting with and  solving their data value chain challenge will set the foundations for scaling your solution?

CyStellar’s technology delivers an Insurtech solution, targeting users within the financial sector. Multiple data  sources are integrated into the cloud-based platform to assist in the Risk Assessment of policies at a global scale.  Combined with the creation of a digital twin of every policy within a portfolio, the data fusion platform can identify  and deliver a remote risk assessment of each individual property and area of land in question.

Geo-spatial tools and data sources that have proven successful for risk monitoring cases required by financial service  users, are also suitable for the Firefighting intervention prediction model. The near-real time weather and  environmental data accumulated for the financial service users lays the substance for risk identification and  monitoring for Civil Protection Services. Essentially, the same data can provide a unique story corresponding to each user. This contributes to the foundation of scalability for CyStellars solution.

 

Please present your solution and elaborate on how it differentiates from the competition.

 

As one might predict from a space technology company, CyStellar develops applications and products which are  adapted from technology that was originally made for use in space and extensively uses data generated by satellites  for applications on Earth. We use that advantage point to remotely analyse, monitor and classify risk across every  location on earth.

Using our space connections and data analytics expertise, our solution delivers near real-time, data driven insight for  high-risk areas. We collate and process social, political, and environmental data obtained from satellites and deliver this information to the Firefighters. The customised data visualization models ensure they can identify high-risk  locations/properties within the city and apply this knowledge to their disaster preparation and mitigation strategies,  ensuring their resources are allocated effectively.

Do you foresee any obstacles in successfully developing and commercializing your solution?

 

We are aware that the use of data analytics and visualization models is a technologically advanced way of working  that may be new to some users. For the Solutions development to be commercialized successfully, service users  need to be open minded and embrace a new, data driven way of working. Our role will be to not only deliver an  exceptional high performing product but also to build confidence in our clients to help them advance with  technology assisted solutions. This will facilitate, reliable, informed decision making, and enhance the efficiency of  the service.