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Geodata-based Machine Learning for real-time urban risk reduction systems


Description:

We propose to develop and implement an autonomous, intelligent system for real-time monitoring and multi-risk assessment in urban areas using seismic, acoustic, and remote sensing data. The project GEObyIT addresses needs of two departments of the Oslo municipality. The Emergency Department requires improved capabilities for infrastructure monitoring and detection of unusual events such as accidents, explosions, and crimes based on data collected in line with the General Data Protection Regulation that prohibits usage of “sensitive data”. The Water and Sewage Department is currently involved in building a tunnel for securing the freshwater supply in Oslo. This requires improved mapping of surface features and monitoring for small earthquakes or other events which might be able to affect water flow paths. GEObyIT will address both needs by producing new layers of information based on seismic, acoustic, and remote sensing data, which will help to quickly locate incidents and other urban events, ready to be integrated into the existing alertness plan of the city, and to identify and classify potentially hazardous features on the Earth’s surface. While the employed data sets are different in nature, each serves the purpose of risk assessment and the proposed automatic processing pipelines will use a common methodological approach based on Machine Learning. The final product of GEObyIT will be tools for urban real-time alert and risk assessment, including hazard maps for predictive and operational purposes and a dashboard-like webpage including real-time location maps of events. This concept of providing novel data layers for future city management is extremely ambitious, but there is a huge potential to yield major gains and radical breakthroughs, including knowledge transfer and applicability on the global scale. The societal impact of these new layers is broad, for example by improving emergency response of the city authorities in case of accidents and crimes.


Project leader: Volker Oye

Started: 2020

Ends: 2024

Category: Teknisk-industrielle institutter

Sector: Instituttsektor

Budget: 15700000

Institution: STIFTELSEN NORSAR

Address: Lillestrøm