UrbanIoT-Anomaly: Multimodal Smart City Dataset for Urban Safety (2025)

Overview

The UrbanIoT-Anomaly dataset is a multimodal smart city dataset designed for anomaly detection and urban safety research, integrating diverse IoT sensor streams with labeled anomalous events.

Data Sources and Modalities

• Environmental sensors: Air quality (PM2.5, PM10, CO, NO2), temperature, humidity, and noise levels from distributed monitoring stations.
• Traffic sensors: Vehicle flow, speed, and occupancy data from road-embedded sensors and cameras.
• Infrastructure sensors: Smart streetlight status, parking occupancy, waste bin fill levels, and utility consumption.
• Event logs: Timestamped records of incidents, maintenance activities, and public safety events.

Anomaly Types

• Environmental anomalies: Air quality spikes, abnormal temperature/humidity patterns, excessive noise events.
• Traffic anomalies: Congestion, accidents, unusual flow patterns, and parking violations.
• Infrastructure anomalies: Sensor malfunctions, streetlight outages, and abnormal utility consumption.
• Multi-source anomalies: Correlated events across multiple sensor types requiring fusion for detection.

Dataset Characteristics

• Real-world data collected from a large urban area over an extended monitoring period.
• Ground-truth anomaly labels derived from official incident reports, expert annotations, and automated validation.
• Temporal resolution ranging from minutes (traffic, environmental) to hourly (infrastructure) depending on sensor type.
• Spatial coverage across multiple urban zones enabling spatial correlation analysis.

Use Cases

• Anomaly detection: Developing and benchmarking multimodal anomaly detection algorithms for smart city applications.
• Urban safety: Early warning systems for environmental hazards, traffic incidents, and infrastructure failures.
• Data fusion: Research on integrating heterogeneous IoT sensor streams for improved situational awareness.
• Smart city analytics: Understanding urban dynamics and cross-domain correlations in city operations.