Sitowise and Zero Gravity selected for Mobility Lab Helsinki’s new pilot.
Forum Virium Helsinki and Business Helsinki’s Mobility Lab Helsinki project expedites companies’ creation of new business activities, products and services by promoting the development of the City of Helsinki’s digital twin for traffic, information models and data resources and supporting their effective utilisation.
Street information modelling is an essential part of the development of Helsinki’s city information model, which is why our pilot is carried out in cooperation with the Helsinki City Executive Office. The broad aim is to share the lessons learned with businesses, researchers and developers and increase understanding of the potential of the new version of the standard.
The transport networks of different modes of transport can be described at different levels of detail, from simple line diagrams to precise three-dimensional data models showing street segments in great detail. The aim of the spring 2023 pilots is to test the new standard (CityGML3 Transport module) in information modelling of the Helsinki street environment in selected test areas. CityGML3 enables the representation of the street environment as a variety of information models at different levels.
Winners of the call
We selected two companies for the pilot. In both pilots, source data will be produced from selected street areas in Jätkäsaari and on Esplanadi.
Sitowise Oy will pilot information modelling of the source data with the FME tool (Feature Manipulation Engine). The FME enables various modifications of geospatial datasets and data integration in an efficient way.
The aim is to test how the existing geospatial datasets of the city could be combined and improved to produce a model of the transport environment as automatically as possible. The development of these kinds of data processing methods will allow new applications to be built cost-effectively on the basis of the data already being maintained.
Zero Gravity Oy will pilot machine vision solutions to identify elements of the street space, such as road surface markings and lane types. The source data includes satellite images, traditional orthophotos and more detailed orthophotos of the street environment measured with a drone.
Image analysis based on machine learning could enable very efficient data production over large areas, based on aerial images of the city, for example. This would make it easy to regularly update the current status of road markings, among other things.
Photo: Jussi Hellsten
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