City of Roeselare receives 1.7 million euros for smart technology

The city of Roeselare can further expand the road to a smart city. As a leader, she submitted a number of projects together with Bruges and Knokke-Heist via the ‘City of Things’ call and received good news, the applications were fully approved.

On the one hand, machine learning will be used much more to map the public domain, in the broad sense of the word, more quickly. On the other hand, the project on mobile sensor units should help facilitate this.

“Under the leadership of the city of Roeselare, the three cities will join forces to collect data and make it available based on machine learning technology. In this way we can tackle societal challenges in a smart way and we develop a joint platform for exchanging knowledge. The three cities will then make this platform available to all local authorities, so that we will continue to play a pioneering role in smart local authorities,” said alderman Matthijs Samyn.

“As a city, we are very pleased to receive this subsidy. It ensures that our ambition to pursue a ‘smart’ policy can be accelerated. The subsidy shows that the efforts and pioneering role of recent years are valued and recognized by Flanders”, it continues.

postal trucks

Roeselare was already leading the machine learning project to increase road safety in collaboration with bpost. The postal trucks drive around and simultaneously generated photo and/or video material. This material enabled faster and more adequate policy decisions. The main focus of the project was the repair or replacement of road signs, and the automatic development of a complete inventory of more than 12,000 road signs, road markings and others necessary to increase road safety.

The subsidized projects go a step further and will investigate how sensors in vehicles can collect data in addition to video cameras. Just think of heat sensors, sensors that measure traffic in certain locations, measuring air quality, etc. Through machine learning based on aerial and satellite photos, our public domain is accelerated and mapped fully automatically.

This will ensure more targeted decisions about mobility interventions, green spaces, etc., but also data about drought in agricultural zones, possible flooding and other social challenges.

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