High-Temporal-Resolution Urban Activity Monitoring Using Public Webcams and Deep Learning: A Case Study from Leipzig, Germany

Urban public spaces are highly dynamic systems where traffic patterns, pedestrian flows, and human activities vary strongly across temporal scales. Capturing these dynamics at high temporal resolution remains challenging, particularly using low-cost and reproducible observation methods. In this study, we present an automated workflow for continuous urban activity monitoring based on publicly available webcam imagery and deep learning–based object detection.

A public webcam overlooking Augustusplatz, a central urban square in Leipzig (Germany), is continuously accessed, and still frames are extracted from the video stream at one-minute intervals. Each frame is processed using the YOLO11 object detection model to identify and count relevant object classes, including passenger vehicles and pedestrians. The detection results are converted into structured JSON records and enriched with metadata such as timestamp and geographic location. All data are stored in an InfluxDB time-series database and visualized and statistically analyzed using Grafana.

This setup enables near-real-time and long-term analysis of urban activity patterns across multiple temporal scales. Distinct signatures of recurring and episodic events can be identified, including daily commuting cycles, evening rush hours, road closures, public celebrations, and large seasonal events such as Christmas markets. The minute-scale resolution allows for detailed investigation of short-term dynamics, while continuous operation over longer periods enables comparative and trend analyses.

The presented approach demonstrates how publicly available visual data and open-source tools can be combined into a scalable and transferable framework for urban monitoring. Potential applications include event detection, urban mobility analysis, validation of traffic models, assessment of public space usage, and integration with other environmental or socio-economic datasets. The method provides a cost-efficient complement to traditional urban sensing infrastructures and offers new opportunities for data-driven urban and environmental research.