Scalable AI-Assisted 3D City Model Reconstruction from Multiple Sourced Systems

研究的主要目标是为多源系统的融合开发一个可靠和实用的人在环框架，并创建有用的3D城市表示。目前，操作系统中涉及到大量的手动工作，并且只能在非常本地的范围内提供，因为这些实践的劳动密集型限制了可伸缩性。该项目旨在利用人在环路的方法来收集制定流程所需的知识，这些流程可以节省运营成本，处理更大的区域/城市/国家，允许更好的时间和资源管理，更快的测量(例如，通过飞行/街道无人机)和高精度的后续检查和测量。它将培训一名在人文、深度学习、程序建模和3D几何处理方面具有适当背景的博士研究员。

The key objective of the research is to develop a reliable and practical human-in-the-loop framework for the fusion of multi-sourced system and create useful 3D City representation. At the moment, there is a lot of manual work involved in OS and are only made available at a very local scale because the labourintensive nature of the practices limits scalability. The project aims to leverage the human-in-the-loop approach to gather the knowledge required to develop processes that could save operational costs, handlea much larger region/city/nations, allow better time and resource management, faster surveying (e.g., via flying/street drones) with high accuracy for subsequent inspection and measurement. It will train a PhD researcher with the right background in humancentredness, deep learning, procedure modelling and 3D geometry processing.