Indicative Data: Extracting 3D Models of Cities from Unavailability and Degradation of Global Navigation Satellite Systems (GNSS)

3-城市的三维（3D）模型对于许多应用是有益的，甚至是必不可少的，包括城市规划/发展、能源需求/消耗建模、紧急疏散和响应、照明模拟、地籍和土地使用建模、飞行模拟、定位和导航（特别是对于城市峡谷中的自动汽车和需要无障碍的残疾用户）以及建筑信息建模（BIM）。尽管3D模型很重要，但对于许多地区/城市来说，它们不可用或经常更新。这可能是由于生成和更新的过程（通过当前技术，如LiDAR（光检测和测距））在计算和财务上是昂贵的，耗时的，并且由于城市的动态性质而需要频繁更新。该研究金将提议并实施一种基于众包的方法，利用全球导航卫星系统免费使用和全球可用的数据创建准确的三维模型。城市特征（如建筑物和树木）对GNSS信号的影响，即信号阻塞和阻碍以及衰减，将有助于通过应用统计、机器学习和人工智能技术来识别城市特征的形状、大小和材料。使用可在任何现有安卓设备上获取的免费获取的全球导航卫星系统原始数据，将能够以免费或低成本制作最新的三维模型，这对目前无法获得这些模型的发展中区域特别有价值。全球导航卫星系统是最广泛使用的定位技术，因为免费使用，隐私保护和全球可用的信号。然而，GNSS信号可能被物体阻挡、反射和/或衰减，例如树木、建筑物、墙壁和窗户。虽然全球导航卫星系统信号的阻塞、衰减和反射在城市峡谷和室内很常见，使定位不可靠、不准确或不可能，但受影响的接收信号可以作为周围环境结构的指示器。这意味着，例如，如果信号被阻挡或衰减，则可以理解障碍物的大小和形状或信号穿过或反射的介质/材料的类型。这需要卫星和接收器的精确位置，以及每个位置和时间的预测信号强度水平。基于众包的框架，即用于数据捕获的移动的应用程序和用于上传GNSS原始数据的网络地图应用程序，将使该项目能够在空间和时间上拥有分布良好的数据。这将最终导致更高质量（空间和时间上更准确，完整，精确）的3D模型。然而，由于数据的复杂性，无论是接收移动的设备还是广播卫星都是固定的，一些新的数据挖掘技术，基于现有的统计，ML和AI技术，需要在这个奖学金期间开发。它们将以高准确度处理全球导航卫星系统时空原始数据的大量、变化速度和复杂性。时空模式将用于创建和更新高细节级别（LOD）的城市3D模型，即近似于反射或穿过信号的立面和建筑材料，例如窗户。这些3D模型将用于3D地图辅助GNSS定位（并与WiFi等其他信号集成），最终可以在城市峡谷和室内提供更连续、更准确的GNSS定位。这项研究金将提供一个新的视角，将数据的缺乏和退化视为一个“指示性”数据来源，可供其他学科重新应用。这次奖学金的成功将帮助我成为空间数据科学领域国际公认的领导者。

3-Dimensional (3D) models of cities are beneficial or even essential for many applications, including urban planning/development, energy demand/consumption modelling, emergency evacuation and responses, lighting simulation, cadastre and land use modelling, flight simulation, positioning and navigation (particularly for autonomous cars in urban canyons and disabled users requiring accessibility), and Building Information Modelling (BIM). Despite the importance of the 3D models, they are not available or being updated frequently for many areas/cities. This can be due to the process of generating and updating (by current technologies such as LiDAR (Light Detection and Ranging)) being computationally and financially expensive, time-consuming, and requiring frequent updates due to the dynamic nature of cities. This fellowship will propose and implement a crowdsourcing-based approach to create accurate 3D models from the free to use and globally available data of Global Navigation Satellite Systems (GNSS). The effects of urban features, such as buildings and trees, on GNSS signals, i.e. signal blockage and obstruction, and attenuation, will help to recognise the shape, size, and materials of urban features, through the application of statistical, machine learning (ML) and artificial intelligence (AI) techniques. The use of freely accessible raw GNSS data, which can be accessed on any current Android device, will enable the production of up to date 3D models at no or low cost, of particular value in developing regions where these models are not currently available. GNSS is the most widely used positioning technique because of free-to-use, privacy-preserving, and globally available signals. However, GNSS signals can be blocked, reflected and/or attenuated by objects, e.g. trees, buildings, walls and windows. While blockage, attenuation and reflection of GNSS signals are common in urban canyons and indoors, making the positioning unreliable, inaccurate or impossible, the affected received signals can act as an indicator of the structure of the surrounding environments. This means, for example, if the signals are blocked or attenuated, then the size and shape of the obstacles or the type of media/material the signals have gone through or been reflected by can be understood. This needs the precise locations of satellites, and the receiver, and also predicted signal strength level at each location and time. The crowdsource-based framework, i.e. a mobile app for data capture and a web mapping application for upload of GNSS raw data, will allow the project to have well-distributed data both in space and time. This will ultimately lead to higher quality (more spatially and temporally accurate, complete, precise) 3D models. However due to the complexity of data, as neither the receiving mobile devices nor the broadcasting satellites are fixed, some novel data mining techniques, based on already existing statistical, ML, and AI techniques, need to be developed during this fellowship. They will handle the high volume, the velocity of change, and the complexity of the spatio-temporal GNSS raw data with high levels of veracity. The spatio-temporal patterns will be used for creating and updating the 3D models of cities at a high level of detail (LoDs), i.e. approximating the façade and the building materials, e.g. windows, from which the signals are reflected or have gone through. The 3D models will feed into 3D-mapping aided GNSS positioning (and integrated with other signals e.g. WiFi) which can ultimately provide more continuous and accurate GNSS positioning in urban canyons and indoors. This fellowship will provide a novel perspective which perceives lack and degradation of data as an "indicative" source of data, which can be re-applied by other disciplines. The success of this fellowship will help me to establish myself as an internationally recognised leader in the area of spatial data science.