Multimodal smart city dataset combining IoT sensor data (environmental, traffic, infrastructure) with ground-truth anomaly labels for urban safety applications, anomaly detection, and multi-source data fusion research in smart cities.
Real-world parking occupancy and capacity data from NCP-operated car parks in Birmingham, UK, collected every 30 minutes from October to December 2016 for time-series occupancy prediction.
City-scale environmental IoT dataset with more than 24 million temperature measurements from SmartSantander sensors deployed across Santander, Spain, including spatial, temporal, and device metadata.[web:113][web:115][web:118]
Collection of real-world smart city IoT datasets from the CityPulse project, including vehicle traffic, parking occupancy, and weather data from Aarhus (Denmark) and other cities.[web:111][web:114][web:116]
Large-scale real-time air quality monitoring dataset from Dhaka, Bangladesh with 155,406 records. Captures CO, NO2, SO2, O3, PM2.5, and PM10 using IoT sensors with Arduino integration. Ideal for environmental analytics, pollution prediction, and smart city air quality management.
Two years of continuous IoT-based smart parking lot usage data collected via ThingSpeak platform. Features IR sensors and ESP32 boards monitoring slot availability, occupancy patterns, peak hours, and parking duration for urban parking management optimization.
Multimodal smart city dataset combining environmental sensors (temperature, humidity, gas, vibration, noise, motion) and surveillance images with binary anomaly labels. Designed for edge computing, urban anomaly detection, and real-time city monitoring research.
Comprehensive dataset capturing cybersecurity threats and sustainability metrics in smart city IoT and edge networks, including communication behavior, energy consumption patterns, and attack scenarios.
High-resolution traffic flow data from 470 sensors across Glasgow covering 4 years (Oct 2019 - Sep 2023). Captures traffic patterns during COVID-19 pandemic with 15-minute intervals.
Simulated smart city environment capturing multimodal data from distributed urban sensors and surveillance sources. Includes synchronized environmental and visual data for edge computing and urban anomaly detection research.
High-resolution temperature data from dense IoT sensor networks in urban areas for monitoring and analyzing Urban Heat Island (UHI) effects. Integrates data from meteorological offices, environmental authorities, and citizen-generated sensors.