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

城市的三维（3D）模型对许多应用都是有益的，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人用户），以及建筑信息模型（BIM）。尽管3D模型很重要，但在许多地区/城市，它们是不可用的或经常更新的。这可能是由于生成和更新的过程（通过当前的技术，如激光雷达（光探测和测距））在计算和经济上昂贵，耗时，并且由于城市的动态性，需要频繁更新。该奖学金将提出并实施一种基于众包的方法，从全球导航卫星系统（GNSS）的免费使用和全球可用数据中创建精确的3D模型。通过应用统计、机器学习（ML）和人工智能（AI）技术，城市特征（如建筑物和树木）对GNSS信号的影响（即信号阻塞、阻碍和衰减）将有助于识别城市特征的形状、大小和材料。使用免费获取的原始GNSS数据，可以在任何当前的Android设备上访问，将使最新的3D模型的生产无成本或低成本，在目前无法获得这些模型的发展中地区具有特别的价值。GNSS是使用最广泛的定位技术，因为它可以免费使用，保护隐私，并且信号全球可用。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。在城市峡谷和室内，GNSS信号普遍存在阻塞、衰减和反射等现象，导致定位不可靠、不准确或无法定位，但受影响的接收信号可以作为周围环境结构的指标。这意味着，例如，如果信号被阻挡或衰减，那么障碍物的大小和形状或信号经过或被反射的介质/材料的类型就可以被理解。这需要卫星和接收机的精确位置，还需要预测每个位置和时间的信号强度水平。基于众包的框架，即一个用于数据捕获的移动应用程序和一个用于上传GNSS原始数据的网络地图应用程序，将使项目在空间和时间上都拥有良好分布的数据。这将最终导致更高质量的3D模型（在空间和时间上更准确、完整、精确）。然而，由于数据的复杂性，无论是接收移动设备还是广播卫星都不是固定的，因此需要在现有的统计、ML和AI技术的基础上开发一些新的数据挖掘技术。他们将以高水平的准确性处理高容量，变化速度和时空GNSS原始数据的复杂性。这些时空模式将用于创建和更新城市的三维模型，在高水平的细节（LoDs），即近似的外墙和建筑材料，如窗户，从中反射或穿过信号。3D模型将输入3D地图辅助GNSS定位（并与其他信号（如WiFi）集成），最终可以在城市峡谷和室内提供更连续和准确的GNSS定位。该研究金将提供一种新的观点，将数据的缺乏和退化视为数据的“指示性”来源，可以由其他学科重新应用。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.