Generating Digital Twins of Roads

生成道路数字孪生

• 批准号: 2598277
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2598277
• 关键词: Generating; Digital; Twins; Roads

This research aims to provide the needed semantic segmentation methods for the reality capture of digital twins' foundation data enhancing 3D reconstruction, asset certainty and overallspatial context and understanding. Spatial context offers the ability for digital twins to integrate asset data with local area knowledge provided by existing national data holdings (e.g. OS MasterMap) contributing to enhanced resilience. This project can significantly strengthen the level of maturity (complexity and connectedness) of digital twins enabling the next generation of smart applications, such as performance optimisation by real time data capture, failure prediction and future scenario modelling. Tools exist that can automatically annotate visual data using deep learning architectures. Yet the outcome is not immediately usable in our context. The generation of geometric Digital Twins requires the presence and processing of both visual and spatial data to derive sufficient meaning and generate the richest possible outcome. The challenge in this context is how to automatically annotate combined visual and spatial datasets with a sufficient degree of detail (small and large assets, asset defects, etc.) to meet road network inspection guidelines.

这项研究旨在提供所需的语义细分方法，以实现数字双胞胎基础数据的现实捕获增强3D重建，资产确定性和整体空间环境和理解。空间上下文为数字双胞胎提供了将资产数据与现有国家数据持有量（例如OS Mastermap）提供的局部知识集成的能力，从而有助于增强弹性。该项目可以显着增强数字双胞胎的成熟度（复杂性和连接性）的水平，从而实现了下一代智能应用程序，例如通过实时数据捕获，失败预测和未来场景建模进行性能优化。存在可以使用深度学习体系结构自动注释视觉数据的工具。然而，结果在我们的背景下并不可立即使用。几何数字双胞胎的生成需要视觉和空间数据的存在和处理，以获得足够的含义并产生最大的可能结果。在这种情况下，挑战是如何自动注释具有足够程度的细节（小资产和大型资产，资产缺陷等）的视觉和空间数据集以满足道路网络检查指南。