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

指示性数据：从全球导航卫星系统 (GNSS) 不可用和退化中提取城市 3D 模型

基本信息

批准号：
MR/S01795X/2
负责人：
Anahid Basiri
金额：
$ 96.74万
依托单位：
University of Glasgow
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FS01795X%2F2
关键词：
Indicative Data Extracting 3D Models

项目摘要

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.

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 (partly for autonomous cars in urban canyons and disabled users requiring accessibility), and Building Information Modelling (BIM).尽管3D型号非常重要，但对于许多领域/城市，它们尚未可用或经常更新。这可能是由于生成和更新的过程（通过当前技术（例如LIDAR（光检测和范围）））在计算和财务上昂贵，耗时，并且由于城市的动态性质而需要经常更新。该奖学金将提出并实施一种基于众包的方法，以创建自由使用的准确3D模型以及全球导航卫星系统（GNSS）的全球可用数据。城市特征（例如建筑物和树木）对GNSS信号的影响，即信号阻塞和反对以及衰减，将有助于通过应用统计，机器学习（ML）和人工智能（AI）技术来识别城市特征的形状，大小和材料。可以在任何当前的Android设备上访问的可自由访问的RAW GNSS数据的使用将使最新的3D模型以任何或低成本的价格生产最新的3D模型，在当前尚不可用的这些模型的开发区域中，其价值尤其为价值。 GNSS是最广泛使用的定位技术，因为免费使用，保护隐私和全球可用的信号。但是，可以通过对象阻止，反射和/或衰减GNSS信号，例如树木，建筑物，墙壁和窗户。尽管GNSS信号的阻塞，衰减和反射在城市峡谷和室内很常见，这使得定位不可靠，不准确或不可能，但受影响的接收信号可以充当周围环境结构的指标。例如，这意味着，如果信号被阻塞或衰减，则可以理解信号通过或反射的障碍物的大小和形状或媒体/材料的类型。这需要卫星和接收器的确切位置，并且还需要在每个位置和时间预测信号强度水平。基于众包的框架，即用于数据捕获的移动应用程序和用于上传GNSS原始数据的Web映射应用程序，将使项目在时空中具有良好的分布数据。这最终将导致更高的质量（在空间和暂时准确，完整，精确）3D模型中。但是，由于数据的复杂性，由于接收移动设备或广播卫星是固定的，因此在此奖学金期间需要开发一些基于现有统计数据，ML和AI技术的新型数据挖掘技术。他们将处理具有高度准确性的空间GNSS原始数据的高音量，变化的速度和复杂性。时空模式将用于创建和更新高度细节（LOD）的城市3D模型，即近似立面和建筑材料，例如窗户，从中反射或通过其通过的窗户。 3D模型将进食3D映射的辅助GNSS定位（并与其他信号集成，例如WiFi），这些信号最终可以在Urban Canyons和室内提供更连续，准确的GNSS定位。该奖学金将提供一种新颖的观点，该视角将数据缺乏和退化为“指示性”数据来源，可以由其他学科重新应用。这项奖学金的成功将帮助我确立自己是空间数据科学领域的国际认可领导者。