Digital 3D models are used in almost all areas of design and engineering from drug discovery through to architectural design optimization. With the advent of new imaging technologies such as more advanced remote sensing systems and consumer 3D cameras, there is a new capability to capture models that are both deep and broad in detail. Currently there is a range of different software packages for editing 3D data but each is rather specialised to its domain. If data is to be exchanged between different users then the most usual way of doing this is to export files from one machine, move the file and import it on another machine. This can be facilitated by various file-sharing systems, but the unit of access is a file on a local file system. This presents multiple problems: as models grow in complexity managing them in files becomes problematic, collaborative editing is very hard and tracking of changes becomes challenging. In addition in the next few years, we should expect 3D model sizes to grow in size and detail at increasing rates. This will be driven both by consumer editing and scanning tools, but also increasing use of commercially scanned and produced models. One can extrapolate from the current extensive but crude representations of cities on Google Earth, or the highly detailed, but ultimately limited in scale models in modern video games, to imagine that models will reach 10^9 - 10^11+ polygons in scale within the next decade. There is a domain where collaborative access to large models has been solved: Internet storage of documents. Systems as diverse as Wikipedia and Google Docs demonstrate that by decoupling storage from viewing and editing, extremely large repositories of information can be built. In Open3D we will design the necessary algorithms and services that allow the hosting of 3D models of such scale on the Internet. Such 3D models, however, are fundamentally different from text counterparts: 3D models extend across space (e.g. 2-manifold data), lack an obvious extrinsic parameterization, and can have large variations in local details; while, text documents have a natural linear ordering making them much simpler to work with. Our observation is that while models may be big, the spatial scope of an editing or visualisation is usually limited. Thus we can imagine a network protocol that can exchange model assets based on spatial queries, rather than file access. Further, we can imagine that we can perform locking and revision control on this model to prevent inconsistent model states across multiple editors. Through Open3D's unique set of facilities we want to enable synchronous collaborative modelling of a unified model with the minimum of interference in the user experience. By doing this we hope to enable a new crowd-sourcing effort to develop large-scale models in a couple of domains. In particular, in our impact plan we target creating a virtual model of part of London, and an open access anatomical model of the human body.

An ontology is a precise specification of the meanings of a vocabulary of concepts. In computer systems, ontologies provide a tool for robust and flexible manipulation of diverse data. They can support transfer of information between systems, and also allow the presentation of information to be customised to a user's requirements.The domain of geography is one where there is a real and widely recognised need for ontology. Geographic Information Systems are of increasing importance in both commercial and governmental planning. Effective use of these systems requires high-level, flexible mechanisms for accessing the data. Moreover, in many situations one would like to combine information from several sources that may organise their data in very different ways.The geographic realm presents particular challenges to the formulation of an adequate ontology. Geographic classifications are highly affected by ambiguity, vagueness and context sensitivity; so establishing a precise relationship between geographic terms and the physical reality they describe is problematic.The proposed project seeks to develop an ontology based on rigorous principles of knowledge representation using formal logic. This will build on foundational theories of space, time, material objects and processes, which have been a focus of previous research conducted at Leeds. These theories will provide a framework within which specifically geographic concepts and relationships will be defined.The representation used to express our ontology will explicitly model of the vagueness present in the high-level vocabulary of natural language. It is proposed to employ a novel approach called Standpoint Semantics . This models the variable meaning of vague concepts in terms of threshold values for objective properties. For instance a relevant property for distinguishing lakes and rivers is rate of water flow. A given standpoint is associated with a particular choice of threshold separating bodies considered flowing from those considered still . (Prototype software has already been developed at Leeds that implements a standpoint semantics for geographic water features. It allows concept definitions to be inspected and modified, and will automatically label a map in accordance with these definitions.)Detailed ontology construction will focus on the geographic realm and in particular on concepts relating to a) hydrographic features (lakes, rivers, canals, marshes etc), b) the built environment (buildings, roads, towns etc).which have been the subject of pilot projects at Leeds.During the course of the project we shall develop a web-based resource to enable collation and maintenance of our ontology and will allow researchers around the world to access our theories.The project will involve close collaboration with the Research and Innovation group of Ordnance Survey, UK's national mapping agency, who will be providing geographic data, expertise and a significant financial contribution to the project. Ordnance Survey have recently undertaken a major upgrade of data-storage and delivery systems and are collating their map data within a feature-based object-oriented framework known as MasterMap . In order to organise and provide flexible access to this data they wish to develop an ontology that would enable this information to be interpreted at a conceptual level. This would allow integration with other data sources and customisable presentation of map information.Collaboration is also planned with the Laboratory for Applied Ontology in Trento, the Institute of Formal Ontology in Information Systems in Saarbrucken and the Institut fur Geoinformatik in Muenster.

A passenger in an aircraft requires information about a flight at a very different level of detail from the pilot. A map of an entire country on a computer screen shows far less detail than a map of single town on the same screen. For certain kinds of data, reducing the level of detail is a relatively well-understood process, but for other kinds this reduction is a challenging problem. This project is concerned with reduction in level of detail for data associated to networks in geographic information systems. Examples of such networks are roads, rivers, railways, electricity distribution networks, etc.Manipulation of level of detail, or granularity, is vitally important for any kind of system for managing processes and detecting events in geographical networks. For example: congestion and accidents on roads, floods in rivers, or terrorist attacks on railways. Such systems require some level of human intervention, and to do this effectively requires the ability to zoom in and out of the data in various ways. Changing the spatial level of detail, or 'scale' in traditional paper-based maps, is only one of the requirements -- it is also necessary to deal with classification of the things represented (ontologies), and with time at different granularities.Features in geographical information are usually classified by what kind of thing they are: here is a house, there is a school and that is a railway station, and they are all buildings . In a large scale (i.e. detailed) map we generally work with a classification that is itself detailed. Besides showing individual buildings, such maps can make fine distinctions between many different kinds of building. At smaller scales, as the separate buildings merge into undifferentiated built-up areas on the map, the classification becomes coarser too. The level of detail in classification is termed ontological granularity.If dealing with a map showing, say, traffic flow along streets in a city, we might need to see how levels of traffic vary over a single day or at a given time over a number of different days. In both of these examples, temporal granularity is involved -- grouping together and selecting periods of time.The challenge that this project addresses is the combination of these three kinds of granularity: the spatial, the ontological, and the temporal. In varying one kind of level of detail, what changes are necessarily imposed in the other kinds of level of detail? Some simple examples are easily understood: if a church and an adjacent house become represented at a smaller scale by a single entity, it might get classified simply as a building. However, general theoretical principles are lacking; the project will develop these and will evaluate them in collaboration with the Ordnance Survey. The principles will be used to specify operations for changing level of detail in network-based geographic data.The evaluation will be based on a major resource for UK network data: the Integrated Transport Network. This is a layer within Ordnance Survey'sMasterMap providing two themes: the Roads Network (containing all navigable roads in Great Britain) and Road Routing Information (containing additional information such as one-way streets and other restrictions). The project will also make essential use of the expertise of Professor Michael Worboys, Chair of the Department of Spatial Information Science and Engineering, University of Maine, who will be based in Leeds as a visiting researcher.

Hestia2 is an innovative public engagement project based on the spatial reading and visualising of texts. Involving a research team from Classics, Geography and Computing, Hestia produced two innovative outcomes: the understanding of geographic space in Herodotus's Histories in terms of connections between places and peoples rather than as points on a map; and the development and use of web technologies for visualising and thinking about a narrative. Hestia2 represents a deliberate shift from experimenting with geospatial analysis of a single text to making Hestia's outcomes available to new audiences through a variety of creative means: (i) a seminar series fostering knowledge exchange of Hestia's spatial analysis of a text between researchers and non-academic communities; (ii) an innovative online platform designed to enhance the experience for general enthusiasts, students and teachers of reading Herodotus and be extensible to other texts with spatial aspects; and (iii) a blog and free learning materials aimed at disseminating Hestia's resources and generating public interest. With the digital medium rapidly transforming the ways in which information is viewed and processed, data visualisation is one of the key challenges to academic and non-academic groups alike. Cultural heritage groups, government agencies and firms working in the digital economy, all have to deal with the problem of presenting big data in ways that make sense to their users but that do not reduce the complexity of the data or give a misleading picture. Hestia2 uses the key intellectual outcome of the original project-the analysis of spatial relations embedded in literary texts-to discuss humanistic approaches to data visualisation which, by virtue of being based on real content that is complex and messy, can help contribute to this debate. In a four-part seminar series, Hestia2 considers: (i) network analysis techniques, methods and models used for data exploration; (ii) the role of GIS in mapping texts; (iii) digital visualisations of data, especially complex literary texts; and (iv) the extent to which digital technologies help non-academics access and comprehend research. To promote wider engagement among teachers, students and general enthusiasts with the original project's re-imagining of the geography of Herodotus's Histories, Hestia2 brings together the disparate technological innovations into a cohesive, intuitive reading interface (called GapVis). This platform allows users to grasp the total distribution of place references at a glance, move through the narrative and see locations appearing and 'fading from memory', and focus on individual places including their relationships to other locations mentioned 'in the same breath'. By trialling Hestia GapVis with an experienced academic innovator, Hestia2 will ensure a robust and user-friendly reading interface fit for the enthusiast, student, teacher and researcher alike. In addition, by using the Pelagios project's index of references for places mentioned in Herodotus, users of Hestia will be able to link to and bring together different kinds of online data associated with those places, from other texts that mention them, artefacts and inscriptions found there, to digital photos of them. To enhance dissemination of Hestia's outputs, all activity will be documented on the blog and disseminated via social media, while free learning materials will be produced to enrich the resource. The blog will promote continuous dissemination and discussion of the findings from the seminars as well as feedback about the GapVis reading interface. By documenting the work of adapting the Hestia dataset to GapVis, Hestia2 also sets down guidelines for other users, who may wish to repurpose GapVis for displaying a text of their own choice that has geospatial elements. Lastly, Hestia2 will work with the OU's OpenLearn unit to build free learning materials that provide context for reading Herodotus in GapVis.

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.