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

Lead Research Organisation: University of Glasgow
Department Name: College of Science and Engineering

Abstract

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.

Planned Impact

The success of this project will enable several disciplines and sectors to create, update, and (re-)use 3D models of the cities only requiring GNSS raw data, which is globally available and free-to-use. Such disciplines and sectors may include:

Energy: Currently there are several projects which run a 3D building stock model to estimate and predict energy consumption. Such 3D models are mostly based on expensive LiDAR and Ordnance Survey Mastermap. This project will provide up-to-date 3D models that are free-to-create and easy to update.
Positioning and Navigation: The primary application of GNSS is for positioning and navigation. While GNSS is the most-widely used a positioning technology with an estimated market of at £2.3B in the UK, its functionality is mainly limited to outdoors. While 3D-mapping aided GNSS positioning provides very impressive results, its accuracy and usability depend on the availability and the quality of the 3D models. This project will develop a recursive platform where GNSS RSS patterns will feed into 3D modelling and 3D models will feed into "shadow-matching" positioning services. It is also envisaged that positioning service can be integrated with 3D modelling and provides with a Simultaneous Localisation and Mapping (SLAM) service, based on GNSS raw data.
The indoor positioning and navigation applications currently are based on other positioning technologies such as Wireless Local Area Network (WLAN), Radio Frequency IDentification (RFID), Cameras, Bluetooth Low Energy (BLE), etc., none can yet provide a globally available, privacy preserving service at minimum cost to users associated with infrastructure development/installation and maintenance, or in another word a "GNSS-like" service. The success of the project will provide a seamless (indoor/outdoor) positioning and navigation service, resulting in UK industry being at the forefront of the indoor positioning market.
The seamless positioning service the project can provide, will be beneficial not only for navigation services, i.e. the biggest revenue generator of Location Based Services (LBS) industry, but also for many other services including healthcare services worldwide will benefit from a continuous, accurate positioning service improving the ability to respond quickly to emergencies, saving or improving the quality of many lives. The US statistics state 10,000 lives per year could be saved if accurate (50m horizontally) indoor location was attached to 67% of emergency calls.
Policy makers: The outcomes of this research can be utilised by policy makers (via UCL Public Policy team, Uber, OS), such as urban planners and disaster managers. E.g. with a facility to rapidly and freely generate/update the 3D model of the cities they can have a better understanding of the current situation (e.g. disaster) to manage it better. This also promotes UK involvement with European GNSS, Galileo, or mandating revisions to positional requirements for emergency calls, security and tracking related applications, in order to improve services (e.g. E112).
Public: The crowdsourcing is at the heart of the project (promoted by public engagement events and school visits). The public will contribute data and will see the 3D model of their surrounding. individuals will benefit from accessing more accurate positioning/LBS services, e.g. navigation, inside buildings.
Research: The step-changing view of this project, i.e. considering lack of data (e.g. blockage of signals) as indicative data itself (e.g. size and shape of the blocking building) can be applied by many other disciplines, such as statistics and data science. This is particularly important in the era of big data, where data might not be captured for the specific use of the application and so the level of availability and uncertainty could vary. Also, the disciplines including data science, positioning and navigation, transportation, energy, and citizen science can extend their research.

Publications

10 25 50

publication icon
Basiri A (2020) Navigating Through Pandemic: The Use of Positioning Technologies in Journal of Navigation

publication icon
Salhab M (2020) SPATIAL DATA QUALITY EVALUATION FOR LAND COVER CLASSIFICATION APPROACHES in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

publication icon
Yan J (2020) 3-D Passive-Vision-Aided Pedestrian Dead Reckoning for Indoor Positioning in IEEE Transactions on Instrumentation and Measurement

 
Description This project proposed and implemented a novel tool to create 3D maps of cities from freely available GPS and similar Global Navigation Satellite Systems (GNSS) signals. GNSS is globally available for free and so this project can provide a global 3D map anywhere, 3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This project could generate maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by
obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. The project applied algorithms to
GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed
technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings,
can be estimated with below 5 metre accuracy of national mapping agencies which is the benchmark recommended by the CityGML standard.
Exploitation Route Applications in urban planning, energy consumption modeling, green space planning, drone navigation, emergency services are yet to be developed upon these freely available 3D maps.

Other disciplines can develop the mindset of missing data as a useful dataset to learn about the underlying reasons causing missingness for other applications such as social sciences, sensor network, medical science etc.
Sectors Digital/Communication/Information Technologies (including Software)

Energy

Environment

Security and Diplomacy

Transport

 
Description The team and fellow work very closely with Ordnance Survey to feed the 3D mapping software into 3D maps OS collected and surveyed. This is extremely important for variety of OS service including positioning and navigation. https://www.ordnancesurvey.co.uk/newsroom/blog/indicative-data-science?utm_source=twitter&utm_medium=social&utm_term=&utm_content=&utm_campaign=e34094ce-9906-428c-8e69-8cbaa6302f41
First Year Of Impact 2021
Sector Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Education,Transport
Impact Types Societal

 
Description Co-Chair of ScotStat Board
Geographic Reach National 
Policy Influence Type Implementation circular/rapid advice/letter to e.g. Ministry of Health
Impact Our goal, as statisticians, is to improve data driven decision making in the public sector. To do this we need to be at the top of our game on communicating our statistics. Our published statistics must be relevant to inform public debate. And we have the power, through our data and analysis, to shine a light on environmental and societal issues that would otherwise remain hidden. Statisticians working on individual topic areas regularly engage with their users to guide their work. However, it is also important that we reflect on the statistical system as a whole. And so, to support the country analytical colleagues across the Public Sector, the Govt established a new and refreshed "ScotStat Board for Official Statistics".
URL https://blogs.gov.scot/statistics/2024/02/02/join-the-new-scotstat-board/
 
Description Office for National Statistics' Methodological Assurance Review panel
Geographic Reach National 
Policy Influence Type Contribution to a national consultation/review
Impact We provided independent assurance and guidance on the statistical methodology underpinning 2021 census estimates and those based on administrative sources, Identify significant gaps and risks in methods and make suggestions for mitigation, and reviewed admin data methods and contribute to their continuous improvement. This has resulted in a public consultation to change the future of statistics and population estimation in the country to use "new forms of data".
URL https://uksa.statisticsauthority.gov.uk/the-authority-board/committees/national-statisticians-adviso...
 
Description ATI-DSO partnership - Multi-Lingual and Multi-Modal Location Information Extraction (Multi-LM)
Amount £3,000,000 (GBP)
Organisation Alan Turing Institute 
Sector Academic/University
Country United Kingdom
Start 03/2022 
End 08/2024
 
Description ATI-DSO partnership Lead
Amount £3,000,000 (GBP)
Organisation Alan Turing Institute 
Sector Academic/University
Country United Kingdom
Start 07/2022 
End 08/2024
 
Description Delivering a Climate Resilient City through City-University Partnership: Glasgow as a Living Lab Accelerating Novel Transformation (GALLANT)
Amount £10,370,235 (GBP)
Funding ID NE/W005042/1 
Organisation Natural Environment Research Council 
Sector Public
Country United Kingdom
Start 01/2022 
End 01/2027
 
Description Developing a coherent Bayesian modelling and imputation framework that accounts for, and utilises, Structured Missingness
Amount £259,126 (GBP)
Funding ID SMSATSP1\100078 
Organisation Alan Turing Institute 
Sector Academic/University
Country United Kingdom
Start 05/2022 
End 12/2023
 
Description Scottish Graduate School of Social Science Doctoral Training Partnership
Amount £17,392,901 (GBP)
Funding ID ES/P000681/1 
Organisation Economic and Social Research Council 
Sector Public
Country United Kingdom
Start 08/2017 
End 09/2027
 
Description Turing Network Development Award
Amount £25,000 (GBP)
Funding ID TNDC2\100014 
Organisation Alan Turing Institute 
Sector Academic/University
Country United Kingdom
Start 02/2022 
End 09/2022
 
Description Turing Network Fund
Amount £10,000 (GBP)
Organisation Alan Turing Institute 
Sector Academic/University
Country United Kingdom
Start 03/2023 
End 09/2023
 
Description Urban Big Data Centre
Amount £1,786,234 (GBP)
Funding ID ES/S007105/1 
Organisation Economic and Social Research Council 
Sector Public
Country United Kingdom
Start 01/2019 
End 01/2025
 
Title GnssMapper (3D Mapping using GNSS Signals) 
Description GnssMapper provides tools for generating 3D maps by using Global Navigation Satellite System (GNSS) data. It is the results of UKRI Future Leaders Fellowship research project at the University of Glasgow, which investigates methods for using crowdsourced GNSS data for mapping. This provides a novel approach to generate 3D maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available. The Results of the proposed technique implemented by GnssMapper demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5m accuracy, which is comparable to the accuracy of the national mapping agency of Great Britain, Ordnance Survey GB. 3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This software implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. It applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard. GnssMapper is written in Python and built upon GeoPandas objects. It provides the following capabilities: -read 'raw' GNSS data from Google's gnsslogger app, available for Android phones -process data into a set of observations -estimate building heights based on the observations -simulate observations for algorithm testing -It does not include any functionality for processing GNSS data in order to estimate position, and assumes position data is available from the log file, or calculated elsewhere. GnssMapper depends on the GeoPandas package and its underlying dependencies, including PyGeos. You may view the source code at https://github.com/Indicative-Data-Science/gnssmapper You may read the details of the research on 3D mapping using GNSS here https://www.sciencedirect.com/science/article/pii/S0198971521000788 GnssMapper has been used by three MSc dissertations in Imperial College London, University of Glasgow, University College London, and currently is used with several PhD students in different countries including China, UK, US and South Africa. 
Type Of Material Improvements to research infrastructure 
Year Produced 2021 
Provided To Others? Yes  
Impact GnssMapper provides tools for generating 3D maps by using Global Navigation Satellite System (GNSS) data. It is the results of UKRI Future Leaders Fellowship research project at the University of Glasgow, which investigates methods for using crowdsourced GNSS data for mapping. This provides a novel approach to generate 3D maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available. The Results of the proposed technique implemented by GnssMapper demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5m accuracy, which is comparable to the accuracy of the national mapping agency of Great Britain, Ordnance Survey GB. 3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This software implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. It applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard. GnssMapper is written in Python and built upon GeoPandas objects. It provides the following capabilities: -read 'raw' GNSS data from Google's gnsslogger app, available for Android phones -process data into a set of observations -estimate building heights based on the observations -simulate observations for algorithm testing -It does not include any functionality for processing GNSS data in order to estimate position, and assumes position data is available from the log file, or calculated elsewhere. GnssMapper depends on the GeoPandas package and its underlying dependencies, including PyGeos. You may view the source code at https://github.com/Indicative-Data-Science/gnssmapper You may read the details of the research on 3D mapping using GNSS here https://www.sciencedirect.com/science/article/pii/S0198971521000788 GnssMapper has been used by three MSc dissertations in Imperial College London, University of Glasgow, University College London, and currently is used with several PhD students in different countries including China, UK, US and South Africa. 
URL https://github.com/Indicative-Data-Science/gnssmapper
 
Title Missing/Biased Data Science 
Description Unrepresentative data leads to flawed conclusions; a problem when decisions in industry and government are increasingly data-driven. "Big" data are likely unrepresentative but new methods are needed to understand them. Understanding (a) what is the equivalent sample size of self-reporting/crowdsourced data (such as social media account) which might be biased in a randomised survey? (b) how spatial microsimulation may be affected by survey representativeness? and (c) if anonymous behaviour data be tested for representativeness using "mass imputation", through combine datasets when items can't be directly linked. 
Type Of Material Improvements to research infrastructure 
Year Produced 2020 
Provided To Others? No  
Impact New forms of data, in the era of big data, are increasingly available while their representativeness and completeness of data are under question. This will help to address the big data paradox in that sense (quality vs. qantity) in many areas of research. 
 
Title Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM) 
Description This provides an agent-based model, entitled TRAPSim, to examine the exposure to non-exhaust emissions (NEEs) and the consequent health effects of driver and pedestrians groups in Seoul. To make the model reproducible and replicable, TRAPSim uses the ODD protocol to demonstrate the details of the agents and parameters, as well as provide the codes alongside the descriptions to avoid possible ambiguity. The model's main parameters are thoroughly tested through sensitivity experiments and are calibrated with the city's air pollution monitoring networks. This paper also provides the instructions to the model, possible artefacts, and the configurations to submit the model on the HPC cluster. • An ODD protocol is used to document the agent-based model TRAPSim. • Sensitivity experiments and calibration are explained. • The step-by-step codes and annotations are attached in the protocol and HPC sections. The data and the codes are stored in the Harvard Dataverse(https://doi.org/10.7910/DVN/C93XLZ)The data, codes, bug reports, and wiki pages are written on https://github.com/dataandcrowd/SeoultrafficABM/wikiNetLogo 6.0.4 was used for the model (https://ccl.northwestern.edu/netlogo/download.shtml)R 3.6.1 was used for the HPC works and post-processing analysis (https://cran.r-project.org/)Java 8 was used to run NetLogo on a headless mode (https://www.oracle.com/uk/java/technologies/javase/javase8-archive-downloads.html) 
Type Of Material Technology assay or reagent 
Year Produced 2022 
Provided To Others? Yes  
Impact Not yet 
URL http://creativecommons.org/licenses/by/4.0/
 
Title socio-demographic characteristics and OpenStreetMap contributor behaviours 
Description Resources to support 'Assessing the relationship between socio-demographic characteristics and OpenStreetMap contributor behaviours' Sutton, D., Solomon, G. , Yuan, X., Polat Kayali, M. , Gardner, Z. and Basiri, A. (2023) Assessing the Relationship Between Socio-Demographic Characteristics and OpenStreetMap Contributor Behaviours. In: 1st ACM SIGSPATIAL International Workshop on Geocomputational Analysis of Socio-Economic Data (GeoSocial 2023), Hamburg, Germany, 13-16 Nov 2023, pp. 5-11. Data protection prevents full sharing of the dataset and code used for this paper. Identifying information has been removed in the files provided. 
Type Of Material Technology assay or reagent 
Year Produced 2023 
Provided To Others? Yes  
Impact The work understands the relationship between Socio-Demographic Characteristics and OpenStreetMap Contributor Behaviours.This will help the developer to develop more inclusive technology platforms. 10.5281/zenodo.10810296 
URL https://zenodo.org/records/10810296
 
Title 3D mapping using Computer vision 
Description The dataset and code include a novel technique that estimates the height of the buildings using edge detection techniques adapted from computer vision for directly classifying GNSS signals into line-of-sight and non-line-of-sight classes. 
Type Of Material Data analysis technique 
Year Produced 2023 
Provided To Others? Yes  
Impact 3D maps are used extensively in a variety of applications, from air and noise pollution modelling to location-based services such as 3D mapping-aided Global Navigation Satellite Systems (3DMA GNSS), and positioning and navigation for emergency service personnel, unmanned aerial vehicles (UAVs) and autonomous vehicles. However, the financial cost associated with creating and updating 3D maps using the current state-of-the-art methods such as laser scanning and aerial photogrammetry are prohibitively expensive. To overcome this, researchers have proposed using GNSS signals to create 3D maps. This new method and the test dataset are to advance that family of methods by proposing and implementing a novel technique that avoids the difficult step of directly classifying GNSS signals into line-of-sight and non-line-of-sight classes by utilising edge detection techniques adapted from computer vision. This prevents classification biases and increases the range of environments in which GNSS-based 3D mapping methods can be accurately deployed. Being based on the patterns of blockage and attenuation of GNSS signals that are freely and globally available to receive by many mobile phones, makes the proposed technique a free, scalable, and accessible solution. This paper also identifies some key indicators affecting data collection scalability and efficiency of the 3D mapping solution. 
URL https://drive.google.com/drive/folders/1-FmJjHRsa4_J2UVfU2ykQODmRlEz1v6U
 
Title Assessing the Relationship Between Socio-Demographic Characteristics and OpenStreetMap Contributor Behaviours 
Description Resources to support: Sutton, D., Solomon, G. , Yuan, X., Polat Kayali, M. , Gardner, Z. and Basiri, A. (2023) Assessing the Relationship Between Socio-Demographic Characteristics and OpenStreetMap Contributor Behaviours. In: 1st ACM SIGSPATIAL International Workshop on Geocomputational Analysis of Socio-Economic Data (GeoSocial 2023), Hamburg, Germany, 13-16 Nov 2023, pp. 5-11. Data protection prevents full sharing of the dataset and code used for this paper. Identifying information has been removed in the files provided. 
Type Of Material Data analysis technique 
Year Produced 2023 
Provided To Others? Yes  
Impact Assessing the Relationship Between Socio-Demographic Characteristics and OpenStreetMap Contributor Behaviours result in a better understanding of obstacles against inclusivity of "crowdsourcing" platforms 
URL https://doi.org/10.5281/zenodo.10810296
 
Title Building height algorithm 
Description 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 algorithm estimates the height of a building given a 2D map of the building outline and a a dataset of GNSS readings, by applying statistical, machine learning (ML) and artificial intelligence (AI) techniques to GNSS signal characteristics such as time of flight and signal strength to identify signal blockage and obstruction, and attenuation. The algorithm is currently under development, to be published later in 2020. 
Type Of Material Computer model/algorithm 
Year Produced 2020 
Provided To Others? No  
Impact Used by our research group to show the potential use of GNSS signals to create 3D models of cities. 
 
Title Data to support the sample size optimisation part of 'Scalable 3D mapping of cities using computer vision and signals of opportunity' 
Description Data to support the sample size optimisation part of: Anahid Basiri, Terence Lines & Miguel Fidel Pereira (2023) Scalable 3D mapping of cities using computer vision and signals of opportunity, International Journal of Geographical Information Science, 37:7, 1470-1495, DOI: 10.1080/13658816.2023.2191674   The authors received the support from the UK Research and Innovation Future Leaders Fellowship "Indicative Data: Extracting 3D Models of Cities from Unavailability and Degradation of Global Navigation Satellite Systems (GNSS)", grant number MR/S01795X/2, the Royal Society Research Grant, grant reference RSG/R1/180396. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
Impact Work to investigate the effect of sample size on efficiency of GNSS mapping algorithm. Requires gnssmapper package Sourcecode comprises 3 scripts: metrics.py - sample size metrics and relationship with algorithm accuracy freespace.py - Efficiency of data collection across time and space for the scenario of an idealised standalone simple building. scenarios.py - extends efficiency studies to real physical scenarios. 
URL https://zenodo.org/doi/10.5281/zenodo.10793028
 
Title Database of GNSS observations 
Description Raw GNSS data, which can be accessed on any current Android device, is a novel form of geographic data. It has high potential for large scale use in human geographic study as it is widely accessible and based on an underlying technology which is free-to-use, privacy-preserving, and globally available. The signal characteristics and accuracy of the automatically estimated location vary due to the surrounding environment which may cause blockage, attenuation and reflection of GNSS signals. Therefore study of such datasets is complicated due to the need to infer jointly on position and on the signal characteristics of interest. A database of raw GNSS readings has been collected by the research team within a small area (0.25 sq km) manually labelled with geolocations to a high degree of accuracy. The database is currently partially collected, with the intent to make the database accessible to other researchers once completed. 
Type Of Material Database/Collection of data 
Year Produced 2021 
Provided To Others? Yes  
Impact Used by the research team to evaluate the feasibility of inferring the surrounding environment from signal characteristics, in work to be published later in 2020 and 2021. 
URL https://github.com/Indicative-Data-Science/gnssmapper/tree/master/examplefiles
 
Title GNSS post processing in R 
Description Raw GNSS data, which can be accessed on any current Android device, is a novel form of geographic data. Tools exist for processing GNSS data to estimate the receiver position but the majority are incompatible with the data format used by Android. Furthermore new applications for the data require the ability to access processed individual signals as opposed to the overall receiver position estimate. A set of tools have been written in R which perform standard GNSS post-processing techniques to the Android produced data. This replicates the functionality of similar tools written in other programming languages, but is compatible with the Android format, and allows the interim output to be available for further data analysis in R. 
Type Of Material Data analysis technique 
Year Produced 2020 
Provided To Others? No  
Impact Used by our research group to evaluate the feasibility of inferring about the surrounding environment from GNSS signal characteristics. 
 
Title Indicative-Data-Science: datasets 
Description This comprises GNSS data collected for our research. Freely available for others to use. Comprises raw GNSS data recorded from GNSSlogger, and associated "ground truth" locations list. the same datasets processed using gnssmapper (and non-open source Ordnance Survey 2D maps) into dataframes of intersection heights and signal strengths. Ready for use by GNSS mapping algorithms 
Type Of Material Database/Collection of data 
Year Produced 2021 
Provided To Others? Yes  
Impact Used by 15 other researchers and 2 companies 
URL https://zenodo.org/doi/10.5281/zenodo.10793562
 
Title sample size efficiency assurance of 3D mapping algorithm 
Description This is to investigate the effect of sample size on the efficiency of GNSS mapping algorithm. Requires gnssmapper package Sourcecode comprises 3 scripts: metrics.py - sample size metrics and relationship with algorithm accuracy freespace.py - Efficiency of data collection across time and space for the scenario of an idealised standalone simple building. scenarios.py - extends efficiency studies to real physical scenarios. 
Type Of Material Data handling & control 
Year Produced 2021 
Provided To Others? Yes  
Impact 3D maps are a key requirement for better location-based services, in particular navigation and positioning. However, 3D maps are expensive to produce and as a consequence they are often unavailable or costly to access. Patterns of blockage and attenuation extracted from Global Navigation Satellite Systems (GNSS) signals can be used to generate low-cost 3D maps. GNSS is globally available and free to receive by GNSS-receiver enabled devices, e.g. smartphones, which makes 3D mapping using GNSS data cheaper and more accessible. To date, pilot studies of GNSS-based 3D mapping techniques have not considered aspects of the data collection necessary to create such maps. Understanding this is important to designing a crowdsourced project. This paper identifies some key indicators affecting data collection efficiency 
URL https://github.com/Indicative-Data-Science/samplesize
 
Description Alan Turing Institute 
Organisation Alan Turing Institute
Country United Kingdom 
Sector Academic/University 
PI Contribution Prof Ana Basiri serves the Programmes at the Alan Turing Institute in several ways including leadership (TPS Program), joined projects and studentships (with Health, DnS, ASG), presenting ATI in different event and network, and facilitating collaboration between the Turing and Glasgow University as the Turing Network Development Awardee.
Collaborator Contribution The Alan Turing Institute supports Professor Ana Basiri's fellowship project and closer collaboration with the University of Glasgow which closely aligns with our Data Centric Engineering (DCE) programme, and Defence and Security Programme and will complement projects within the ATI's portfolio aligned to our strategic challenge to deliver resilient and robust infrastructure. ATI commit the following resources towards this project (estimated £20k, contribution in kind): • Senior management input to steer the research (c. 5 days / year, subject to availability) • Hosting project meetings and workshops at the Alan Turing Institute (subject to availability) • Access to the DCE community of experts to look for collaborative opportunities to advance the research
Impact Networking, Output (papers, data, software) meetings and presentations software packages
Start Year 2019
 
Description BIM Academy 
Organisation BIM Academy
Country United Kingdom 
Sector Private 
PI Contribution 3D map creation software Case studies that serve BIM Academy's priorities
Collaborator Contribution Steering committee participation Advising on case studies
Impact Software, Data, Algorithm
Start Year 2019
 
Description Leibniz Institute of Ecological Urban and Regional Development 
Organisation Leibniz Institute of Ecological Urban and Regional Development
Country Germany 
Sector Private 
PI Contribution Using the 3D mapping service we are testing the automatic building type recognition based on the 3D model, testing the automatic detection of changes in the building stock over time (e.g. demolition, front/back extensions, additional storeys, balcony extensions etc.) based on multi-temporal 3D models.
Collaborator Contribution IOER contributes to this project through (a) advisory activities and participation in workshops related to WP1 to WP4 to define test scenarios for the implementation of the proposed techniques, evaluation of the results and commenting on the methodology. They give advice from the perspective of urban/regional planning and sustainable development (applications, use case scenarios, etc.) but also from the methodological point of view (design of the crowdsourcing app, data quality measures). (b) they support the project with the provision of data from the IOER monitor (http://www.ioer-monitor.de/en/) and advise you in acquiring other reference data on buildings that is free of charge (Open data). We have altready (c) initiated collaborative research activities, e.g. 1-2 co-supervised master / doctoral thesis, which might lead to further collaborative research proposals. The IOER is also willing to provide research infrastructure for 1-2 CASA students / doctoral candidates visiting the IOER with access to all resources (especially data for validation) and a supervision in an interdisciplinary environment. Also, (d) testing and evaluating the quality, and the fitness-for purpose of the 3D model for different scenarios in Germany. This may include assisting the engagement of volunteers, reference data collection, and data quality analysis by comparing with official data sets/internal reference data, etc. (e) Testing our approaches on automatic building type recognition based on the 3D model and GNSS RSS degradation data and (f) Testing approaches for the automatic detection of changes in the building stock over time (e.g. demolition, front/back extensions, additional storeys, balcony extensions etc.) based on multi-temporal 3D models.
Impact This is a very multidisciplinary collaboration on several areas: IOER contributes to this project through (a) advisory activities and participation in workshops related to WP1 to WP4 to define test scenarios for the implementation of the proposed techniques, evaluation of the results and commenting on the methodology. They give advice from the perspective of urban/regional planning and sustainable development (applications, use case scenarios, etc.) but also from the methodological point of view (design of the crowdsourcing app, data quality measures). (b) they support the project with the provision of data from the IOER monitor (http://www.ioer-monitor.de/en/) and advise you in acquiring other reference data on buildings that is free of charge (Open data). We have altready (c) initiated collaborative research activities, e.g. 1-2 co-supervised master / doctoral thesis, which might lead to further collaborative research proposals. The IOER is also willing to provide research infrastructure for 1-2 CASA students / doctoral candidates visiting the IOER with access to all resources (especially data for validation) and a supervision in an interdisciplinary environment. Also, (d) testing and evaluating the quality, and the fitness-for purpose of the 3D model for different scenarios in Germany. This may include assisting the engagement of volunteers, reference data collection, and data quality analysis by comparing with official data sets/internal reference data, etc. (e) Testing our approaches on automatic building type recognition based on the 3D model and GNSS RSS degradation data and (f) Testing approaches for the automatic detection of changes in the building stock over time (e.g. demolition, front/back extensions, additional storeys, balcony extensions etc.) based on multi-temporal 3D models.
Start Year 2019
 
Description Maynooth University 
Organisation Maynooth University
Country Ireland 
Sector Academic/University 
PI Contribution Joint studentships with NCG, organising events, exchange of staff from the team, and join courses.
Collaborator Contribution the National Centre for Geocopmutation supports Prof Ana Basiri's research fellowship. The National Centre for Geocomputation (NCG) at Maynooth University en- joys a worldwide reputation in research in spatial data analytics, geographical data analysis and management, and the use of airborne sensor technology, applied in a number of areas including health, climate change, demographics and agricultural applications. The NCG offers support in a number of ways; Firstly this can involve members of the NCG attending meetings, advisory contribution to research projects, input to the supervision of PhD students and post-doctoral researchers, hosting and exchanging staff between the NCG and the School of Geographical & Earth Sciences at the University of Glasgow, collaboration on publications, and the possible hosting of joint research themed workshops or similar events. The in-kind resource value quoted below is indicative and our offer covers whatever resources are eventually required in the research, for the duration of the fellowship. We have estimated in-kind costs out to 5 years as our offering is very likely to have changed within the possible 7-year span of this fellowship.
Impact Joint studentship application to Scottish Graduate School of Social Science (SGSSS) funded by ESRC on good data-big-data.
Start Year 2020
 
Description Ordnance Survey 
Organisation Ordnance Survey
Country United Kingdom 
Sector Public 
PI Contribution The project released the 3D mapping software that uses missing GPS signals which can be used for 3D maps quality assurance or update and surveillance for the mismatched buildings. This provides much cheaper platforms (free 3D mapping platform can inform OS data hub)
Collaborator Contribution Ordnance Survey makes significant in-kind contributions to the Prof Basiri's research fellowship through staff time and the supply of data, both OS products and in-house 'research data'. The in-kind data value quoted below is indicative and our offer covers whatever geographic extent of data is eventually required in the research, for the duration of the fellowship. We have estimate in-kind product costs out to 5 years as our product offering is very likely to have changed within the possible 7-year span of this fellowship. -Senior management input to steer the research (c. 3 days / year) -Technical data support -OS R&D team support (c. 6 days / year) - OS MasterMap Topographic Layer - OS Terrain 5 height data - OS Terrain 2 height data - AddressBase Premium
Impact The project released the 3D mapping software that uses missing GPS signals which can be used for 3D maps quality assurance or update and surveillance for the mismatched buildings. This provides much cheaper platforms (free 3D mapping platform can inform OS data hub)
Start Year 2019
 
Description Ordnance Survey 
Organisation Ordnance Survey
Country United Kingdom 
Sector Public 
PI Contribution The output of the project, free 3D mapping service, will improve the quality of OS maps (and staffing for sending surveyers to update maps, only when the crowdsourcing app finds changes).
Collaborator Contribution Ordnance Survey has made significant in-kind contributions to the fellowship project through staff time and the supply of data, both OS products and in-house 'research data'. The in-kind data value quoted below is indicative and our offer covers whatever geographic extent of data is eventually required in the research.
Impact Future plans for partially funding Ph.D. studentships, joined seminars and workshop hosted/endorsed by OS.
Start Year 2019
 
Title GNSSMapper 
Description GnssMapper provides tools for generating 3D maps by using Global Navigation Satellite System (GNSS) data. 
Type Of Technology Software 
Year Produced 2021 
Open Source License? Yes  
Impact GnssMapper provides tools for generating 3D maps by using Global Navigation Satellite System (GNSS) data. It is connected to a research project at the University of Glasgow, which investigates methods for using crowdsourced GNSS data for mapping. It is written in Python and built upon GeoPandas objects. It provides the following capabilities: read 'raw' GNSS data from Google's gnsslogger app, available for Android phones process data into a set of observations estimate building heights based on the observations simulate observations for algorithm testing It does not include any functionality for processing GNSS data in order to estimate position, and assumes position data is available from the log file, or calculated elsewhere. 
URL https://github.com/Indicative-Data-Science/gnssmapper
 
Title Indicative-Data-Science: samplesize 
Description Work to investigate the effect of sample size on efficiency of GNSS mapping algorithm. Requires gnssmapper package Sourcecode comprises 3 scripts: metrics.py - sample size metrics and relationship with algorithm accuracy freespace.py - Efficiency of data collection across time and space for the scenario of an idealised standalone simple building. scenarios.py - extends efficiency studies to real physical scenarios. 
Type Of Technology Software 
Year Produced 2021 
Open Source License? Yes  
Impact Used by 15 research units and 2 companies 
URL https://zenodo.org/doi/10.5281/zenodo.10793586
 
Description Active Travel talk at Royal Institute of Nativation 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Undergraduate students
Results and Impact This talk looked at the challenges of using navigation services, that have been initially designed for in-car use, for pedestrians and wheelchair users. Active travel and non-drivers navigation services still need some customisations to accommodate the needs of other user groups, including pedestrians or wheelchair users. Non-drivers' movements are not limited by the directed networks of streets and roads, can continue inside the buildings, while limited to a much slower speed, and more vulnerable to the bad weather or pavement surface conditions. These differences are not completely addressed in current routing and navigation services and as a result walking, cycling, or wheelchair pathfinding and routing still need further adjustments. This talk reviewed the challenges, provide some examples and solutions to address the challenges, and showcase some of the implementations of the proposed solutions.
Year(s) Of Engagement Activity 2022
URL https://www.youtube.com/watch?v=tbhG5B23Vvo
 
Description Glasgow Science Fest 
Form Of Engagement Activity A broadcast e.g. TV/radio/film/podcast (other than news/press)
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact As a part of Science on the Sofa's digital programme Talking Science, Prof Ana Basiri gave a talk on "Time To Go On A Digital Diet: How Clean is Our Digital Lifestyle?"
Year(s) Of Engagement Activity 2021,2022
URL https://www.youtube.com/watch?v=u_nL7C1kKeQ
 
Description Interview with the Sun 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Media (as a channel to the public)
Results and Impact I was interviewed by the Sun on the LLM and generative AI adverts and their greater risk of consumers being duped and left out of pocket.
Year(s) Of Engagement Activity 2024
URL https://www.thescottishsun.co.uk/news/12132286/artificial-intelligence-marketing-glasgow-wonka-event...
 
Description Interviews with newspaper 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact My interview with Herald on societal and policy readiness for advances in data science and AI, i.e. digital data sustainability and digital inheritence
Year(s) Of Engagement Activity 2022,2023,2024
URL https://www.heraldscotland.com/business_hq/23780839.agenda-tackling-problem-digital-immortality/
 
Description Keynote in an International Conference 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Industry/Business
Results and Impact More than 600 participants watched the events (hybrid) and several new collaborations, consultancies, advisory roles etc started to emerge. DGI is a major event in Defence and Geospatial community and many follow up emails from policy makers, business are still coming, after my talk on missing geospatial data.
Year(s) Of Engagement Activity 2022
URL https://dgi.wbresearch.com/speakers/ana-basiri
 
Description Stand up commedy on stubborn minorities 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact THE CABARET OF DANGEROUS IDEAS is a platform for top academics to discuss some dangerous ideas, guided by a comedian and it's the force of nature that is the Cabaret of Dangerous Ideas (CODI). Hosted by comedian Susan Morrison, and now in its ninth year, CODI is sixty minutes of rapid-fire research from some of the finest minds in the country.

The talk given by Prof Ana Basiri was on "Size doesn't matter (and I mean it this time!)"
The data says we need more data. A middle-aged white British man will also suffer if AI ignores data from women and ethnic minority groups! Professor Ana Basiri (University of Glasgow) argues for the importance of democratising and representativeness of data.
Year(s) Of Engagement Activity 2021
URL https://www.youtube.com/watch?v=reOMyLafUVY
 
Description Working with Govrt: Appointed as the Chair of ScotStat Boar 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Policymakers/politicians
Results and Impact I co-chair a Govt Board that works on statistical system in Scotland.

https://blogs.gov.scot/statistics/2024/02/02/join-the-new-scotstat-board/
Year(s) Of Engagement Activity 2023,2024
URL https://blogs.gov.scot/statistics/2024/02/02/join-the-new-scotstat-board/
 
Description the Royal Academy of Engineering's 'Critical Conversations' with the RAEng CEO Dr Hayaatun Sillem and Dr Natasha McCarthy, Associate Director, National Engineering Policy Centre at the RAEng. 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact In this online event which was as part of the Critical conversations series, Academy CEO, Dr Hayaatun Sillem CBE, and Prof Ana Basiri discussed the legal, ethical, and safety challenges created by the immortality of digital data and how the engineering community can address them. The conversation looked at whether 'digital is always greener' when data storage and maintenance is energy intensive and has such an environmental impact.

Unlike most engineered products, when digital data reaches the end of its useful life it can be stored indefinitely. In this conversation, we will explore the legal, ethical, and safety challenges created by the immortality of digital data and how the engineering community can address them. The conversation will also look whether 'digital is always greener' when data storage and maintenance is energy intensive and has such an environmental impact.
Year(s) Of Engagement Activity 2022
URL https://www.youtube.com/watch?v=K14YFu1K_YU