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/1
负责人：
Anahid Basiri
金额：
$ 123.39万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FS01795X%2F1
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.

城市的 3 维 (3D) 模型对于许多应用都是有益的甚至是必不可少的，包括城市规划/开发、能源需求/消耗建模、紧急疏散和响应、照明模拟、地籍和土地利用建模、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设施的残疾人用户）以及建筑信息模型 (BIM) 尽管 3D 模型很重要，但对于许多人来说它们并不可用或经常更新。这可能是由于生成和更新过程（通过激光雷达（光探测和测距）等当前技术）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频繁更新。该奖学金将提出并实施一种基于众包的方法，利用全球导航卫星系统 (GNSS) 的免费使用和全球可用的数据创建准确的 3D 模型，研究城市特征（例如建筑物和树木）对 GNSS 信号的影响。 ,通过应用统计、机器学习 (ML) 和人工智能 (AI) 技术，通过使用可免费获取的原始 GNSS 技术，信号阻塞和阻碍以及衰减将有助于识别城市特征的形状、大小和材质。可以在任何当前 Android 设备上访问的数据将能够以免费或低成本生产最新的 3D 模型，这对于目前无法使用 GNSS 定位技术的发展中地区具有特别价值。因为然而，GNSS 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。这种情况在城市峡谷和室内很常见，导致定位不可靠、不准确或不可能，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，如果信号被阻挡或无法定位。衰减，然后可以了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，以及每个信号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良好分布的数据。更高的质量（更多空间然而，由于数据的复杂性，接收移动设备和广播卫星都不是固定的，一些基于现有统计、机器学习和人工智能技术的新颖的数据挖掘技术，他们将在本次研究金期间开发出高容量、高变化速度和高准确性的时空 GNSS 原始数据。时空模式将用于创建和更新。高细节水平 (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）。3D 模型将输入到 3D 地图辅助 GNSS 定位中。并与其他信号（例如 WiFi）集成，最终可以在城市峡谷和室内提供更加连续和准确的 GNSS 定位。数据的缺乏和退化作为“指示性”数据源，可以被其他奖学金学科重新应用，这一点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。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Inclusivity and diversity of navigation services

导航服务的包容性和多样性

DOI：
10.1017/s0373463321000072
发表时间：
2021
期刊：
Journal of Navigation
影响因子：
2.4
作者：
Basiri A
通讯作者：
Basiri A

Simulating and modeling the signal attenuation of wireless local area network for indoor positioning

DOI：
10.1145/3356470.3365527
发表时间：
2019-11
期刊：
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation
影响因子：
0
作者：
Terence Lines;Anahid Bassiri
通讯作者：
Terence Lines;Anahid Bassiri

Crowdsourced geospatial data quality: challenges and future directions