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

城市的三维(3D)模型对许多应用是有益的，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驾驶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，但它们在许多地区/城市都不可用或经常更新。这可能是由于生成和更新(通过诸如LiDAR(光检测和测距)等当前技术)的过程在计算和经济上昂贵、耗时，并且由于城市的动态性质而需要频繁更新。该研究金将提出并实施一种基于众包的方法，根据全球导航卫星系统(GNSS)的免费使用和全球可用的数据创建准确的3D模型。建筑物和树木等城市特征对全球导航卫星系统信号的影响，即信号阻塞和阻塞，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。全球导航卫星系统的原始数据可在任何现有的安卓设备上免费获取，使用这种数据将能够免费或低成本地制作最新的三维模型，在目前没有这些模型的发展中区域具有特殊价值。GNSS是最广泛使用的定位技术，因为它具有免费使用、隐私保护和全球可用信号的特点。然而，全球导航卫星系统的信号可能会被树木、建筑物、墙壁和窗户等物体阻挡、反射和/或衰减。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，使得定位不可靠、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，或者信号已经通过或被反射的介质/材料的类型。这需要卫星和接收器的准确位置，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，即用于获取数据的移动应用程序和用于上传全球导航卫星系统原始数据的网络地图应用程序，将使该项目在空间和时间上都有分布良好的数据。这最终将导致更高质量的3D模型(在空间和时间上更加准确、完整、精确)。然而，由于数据的复杂性，由于接收移动设备和广播卫星都不是固定的，在这次研究期间，需要在现有的统计、ML和人工智能技术的基础上开发一些新的数据挖掘技术。它们将以高水平的准确性处理时空GNSS原始数据的海量、变化速度和复杂性。时空模式将用于以高细节级别(LOD)创建和更新城市的3D模型，即近似于立面和建筑材料，例如从其反射或穿过信号的窗户。3D模型将用于3D测绘辅助GNSS定位(并与其他信号集成，如WiFi)，最终可在城市峡谷和室内提供更连续和更准确的GNSS定位。这一研究金将提供一种新的视角，将数据的缺乏和退化视为数据的“指示性”来源，可供其他学科重新应用。这项奖学金的成功将帮助我在空间数据科学领域确立自己作为国际公认的领导者的地位。

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.