Modern cities suffer from a parking problem that’s hidden in plain sight: There’s simply too much of it. When we think of the issues cars present, we tend to look at automobiles in motion. But an often overlooked issue is how cities handle cars at rest. Since the invention of the automobile, parking – or, sometimes, progressive, forward-thinking restrictions on parking – has powerfully shaped the design, environment and economies of urban areas.

In this excercise, by only using satellite images of car parks taken from Google maps and Copernicus Sentinel 2, we could train artifical intelligence model to detect car parks which potentially could be used by authorities for interventions.

Parking Spot Detection using Mask-RCNN is a project of IaaC, Institute for Advanced Architecture of Catalonia developed at Master in City & Technology in (2019/2021) by:
Student: Aryo Dhaneswara
Faculty: Angelos Chronis, Serjoscha Duering, Nariddh Khean