Enhancing Decision Making along the Detroit Riverfront using Remote Sensing and Computer Vision Tools

Detroit, Michigan, USA

The Detroit RiverFront Conservancy (DFRC) has partnered with the University of Michigan to use data collected by remote sensors and security cameras to better understand how people are currently using the Riverfront and to inform strategies to enhance the use of under-utilized areas.

Health Theme Infrastructure Theme Social Theme

Funding Source

Urban Collaboratory

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About the Project

The Detroit Riverfront is a popular destination for Detroiters and visitors alike, attracting approximately 3 million people each year.

The Detroit Riverfront Conservancy (DFRC), which is coordinating development of the Riverfront area, is interested in better understanding the ways in which visitors use their recreational spaces. Parks are one of the most essential ingredients to the success of any vibrant city because they offer a recreational venue where patrons can commune with nature while enjoying a rich set of social experiences. The value of parks is well established with benefits ranging from being engines of economic growth to improving public health.

 

This project partners with the DRFC to explore the development of a data-driven approach to measuring usage of the park and trails. The DRFC has struggled to fully understand how the park space that they manage is used. The overarching goal of this project is to develop and deploy new approaches to collecting data to quantitatively assess how patrons use city parks and trails including mapping their trajectories within the space and identification of patron activities. This allows park managers to make more informed, data-based decision on how to improve the experience of park patrons while also informing communities on the performance of their parks.

 

There is currently an extensive network of security cameras in use along the Riverfront. Using human activity recognition tools on the video obtained from the security cameras and the data collected by other remote sensing devices, researchers from the University of Michigan are able to distinguish between different activities that people engage in along the Riverfront. For example, the human activity recognition software classifies human activities into one of four categories: gestures, interactions with other people of objects, actions like walking or running, and group activities.

 

By understanding how areas of the Riverfront are used, Lynch’s team is able to provide recommendations to the DRFC about where to add amenities like benches and lights and to provide access. When changes are implemented, they can then test in real time the effectiveness of these changes.

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