Implementation of OGC SensorThings API standard for the integration of dynamic sensor data in a 3D urban digital twin

Abstract

Urban areas are home to over half of the global population and consume significant natural resources, necessitating effective city management for urban resilience and sustainability. Resilient cities are better prepared to face challenges, from natural disasters to economic downturns, and adapt to climate change, resource scarcity, and population growth. Urban digital twins, which are virtual replicas of cities integrating data from various sources, play a pivotal role in enhancing and supporting urban resilience. These digital twins empower urban planners, designers, and the public to make informed decisions, identify vulnerabilities, and respond to acute shocks in real-time. Leveraging technologies like the Internet of Things (IoT) and 3D city models, urban digital twins offer a dynamic representation of the city’s functions, allowing for better visualization, monitoring, and decision-making. However, challenges related to interoperability and data acquisition need to be addressed to maximize the potential of urban digital twins.

This thesis investigates how environmental sensor data can be collected, processed, and integrated into 3D city models to visualize the dynamic elements of a city comprehensively. It explores the use of international standards, such as the OGC SensorThings API standard to acquire, store, and manipulate real-time and historical crowd-source, sensor data in a consistent and interoperable manner. The concepts of sensor data pre-processing and interpolating are also explored through OGC standards, in the context of providing detailed spatio-temporal information for the urban environment, while the results are visualized in a 3D web application.