Urban digital twins are rapidly emerging as transformative tools for real-time city monitoring, simulation, and decision-making. They employ crowd-sensing technique to acquire, integrate, and use diverse urban data sources such as mobile devices, IoT sensors, and wearables to enhance situational awareness and optimise urban management. This offers several advantages to achieve higher accuracy, adaptability, and responsiveness in support of critical applications such as traffic flow optimisation, air quality assessment, public safety management, and disaster response. Yet, crowd-sensing solutions suffer from several challenges including data quality and reliability, system heterogeneity and integration, and computing infrastructure scalability and constraints. This chapter delves into the expanding role of crowd-sensing in urban digital twins and examining its significance in enhancing urban intelligence and real-time decision-making. It provides an in-depth analysis of various crowd-sensing methodologies and data acquisition techniques and highlights how they facilitate the collection of large-scale, human-centric data for urban modelling. Additionally, the chapter emphasises the diverse benefits of crowd-sensing applications, such as improved environmental monitoring, optimised traffic management, and enhanced citizen engagement in smart city initiatives.