Parking violations in Berlin based on mobile mapping data (2019) / Parkverstöße in Berlin basierend auf Bildbefahrungsdaten (2019)

“Mobile mapping data” or “geospatial videos”, as a technology that combines GPS data with videos, were collected from the windshield of vehicles with Android Smartphones. Nearly 7,000 videos with an average length of 70 seconds were recorded in 2019. The smartphones collected sensor data (longitude and latitude, accuracy, speed and bearing) approximately every second during the video recording.

Based on the geospatial videos, we manually identified and labeled about 10,000 parking violations in data with the help of an annotation tool. For this purpose, we defined six categorical variables (see PDF). Besides parking violations, we included street features like street category, type of bicycle infrastructure, and direction of parking spaces. An example for a street category is the collector street, which is an access street with primary residential use as well as individual shops and community facilities. Obviously, the labeling is a step that can (partly) be done automatically with image recognition in the future if the labeled data is used as a training dataset for a machine learning model.

https://www.bmvi.de/SharedDocs/DE/Artikel/DG/mfund-projekte/parkright.html https://parkright.bliq.ai

Daten und Ressourcen

Zusätzliche Informationen

Feld Wert
Quelle https://parkright.bliq.ai
Autor 1 Tobias Hagen
Autor 2 Elisabeth Lerch
Autor 3 Nicole Reinfeld
Autor 4 Siavash Saki
Autor 5 Petra Schäfer
Verantwortlicher Tobias Hagen
Zuletzt aktualisiert Juli 25, 2022, 14:47 (UTC)
Erstellt Juli 12, 2022, 11:58 (UTC)
DOI 10.57806/1dnf1ise