Gary Priestnall

Extracting urban features from LiDAR digital surface models

The use of airborne Light Detection And Ranging (LiDAR) technology offers rapid high resolution capture of surface elevation data suitable for a large range of applications. The representation of both the ground surface and the features on that surface necessitates the removal of these surface features if a ground surface Digital Elevation Model (DEM) product is to be produced. This paper examines methods for extracting surface features from a Digital Surface Model (DSM) produced by LiDAR. It is argued that for some applications the extracted surface feature layer can be of almost equal importance to the DEM. The example of flood inundation modelling is used to illustrate how a DEM and a surface roughness layer could be extracted from the original DSM. The potential for refining surface roughness estimates by classifying extracted surface features using both topographic and spectral characteristics is considered using an Artificial Neural Network to discriminate between buildings and trees.

Corona Remotely-Sensed Imagery in Dryland Archaeology: The Islamic City of al-Raqqa, Syria

Abstract Satellite remote sensing has seen some use in archaeological research, although its effectiveness has been restricted by the low spatial resolution and the high cost of available imagery. In 1995 the United States declassified 860,000 high-resolution Corona satellite photographs, acquired for intelligence purposes between 1960 and 1972. Corona offers global coverage with an emphasis on areas of strategic significance to the United States. The imagery has been released into the public domain at low cost of acquisition. In this paper we explore the potential of Corona imagery in archaeological investigations using as an example the topography and cultural landscape of the early Islamic city of al-Raqqa in northern Syria.

Of Catwalk Technologies and Boundary Creatures

Researchers designing and deploying technologies in the wild can find it difficult to balance pure innovation with scalable solutions. Tensions often relate to expectations around current and future roles of the technology development. We propose a catwalk technology metaphor where researchers as boundary creatures focus on innovation whilst providing links to prêt-à-porter (ready to wear) developments. Evidence from 140 participants, within three “in-the-wild” field-based learning case studies (for mobile, distributed, sensor and augmented reality systems), conceptualise the researchers’ “boundary creature” role in managing design process tensions. Stakeholders, including participants, expected the research projects to produce ready to wear (prêt-à-porter) boundary objects for current practices even when researchers sought to take catwalk approaches by innovating technologies and changing practices. The researcher design role (RDR) model articulates researchers’ narratives with the design team, stakeholders and users around what is innovated (e.g., technology, activities) and how the intervention changes or sustains current practices.

Learner immersion engagement in the 3D virtual world: principles emerging from the DELVE project

Abstract This paper investigates the issues surrounding the use of 3D virtual worlds to enhance learner immersion through improved learner engagement. It is based on findings from the JISC-funded DEsign of Learning spaces in 3D Virtual Environments (DELVE) project at the University of Nottingham and the Open University. Given continued confusion about the term immersion, what it means for a learner to be immersed, and the relationship between immersion, presence and engagement, notions of immersion and engagement in 3D virtual environments are explored in the context of previous published studies ranging from virtual reality to psychology. The resultant improved understanding of the terminology is then used as the basis for coding results from a qualitative, inductive analysis of 20 students that undertook a substantive learning task in the virtual environment Second Life. Emergent themes from the analysis identify key factors that act to both enhance and restrict learner engagement in 3D virtual worlds and a set of principles for practitioners who wish to use 3D virtual environments to enhance learner engagement is presented.

Mobile capture of remote points of interest using line of sight modelling

Recording points of interest using GPS whilst working in the field is an established technique in geographical fieldwork, where the users current position is used as the spatial reference to be captured; this is known as geo-tagging. We outline the development and evaluation of a smartphone application called Zapp that enables geo-tagging of any distant point on the visible landscape. The ability of users to log or retrieve information relating to what they can see, rather than where they are standing, allows them to record observations of points in the broader landscape scene, or to access descriptions of landscape features from any viewpoint. The application uses the compass orientation and tilt of the phone to provide data for a line of sight algorithm that intersects with a Digital Surface Model stored on the mobile device. We describe the development process and design decisions for Zapp present the results of a controlled study of the accuracy of the application, and report on the use of Zapp for a student field exercise. The studies indicate the feasibility of the approach, but also how the appropriate use of such techniques will be constrained by current levels of precision in mobile sensor technology. The broader implications for interactive query of the distant landscape and for remote data logging are discussed.

Cover: Spatial and temporal remote sensing requirements for river monitoring

Rivers represent one of many dynamic environments that are routinely modelled and monitored using remote sensing. Given that rivers vary markedly in ‘size’, ‘shape’ and ‘rate of change’, as does th...

Landscape Visualization in Fieldwork

This paper presents a methodology for the use of 3D modelling and landscape visualization in the context of real-world experience. The techniques offer a framework for a qualitative ‘ground truth’ exercise to assess the nature and quality of digital geographic information within the context of fieldwork exercises. In view of the increasing use of such compelling images in a range of public contexts, the approach aims to encourage viewers of computer-generated landscape images to question the data and processing techniques which generated the digital models. The techniques are used to complement the physical geography components of fieldwork by augmenting real scenes with hidden (geological) and past (glaciated) landscapes. Ongoing developments in the use of mobile computing devices to further support these approaches are presented.

The Processual Intertextuality of Literary Cartographies: Critical and Digital Practices

Abstract Drawing upon recent interdisciplinary research in the fields of literary geography and critical cartography, this article argues that a concept of processual intertextuality might be used to open up thinking about literary maps and mapping practices: a concept through which such maps are understood to be systems of cultural signification which are inextricably embedded within the material world and which are brought into being with each embodied reading or use. This theory is then applied to maps which are both reproduced within and generated by Arthur Ransome’s Swallows and Amazons (1930): an adventure novel for children which is predicated upon a conflation of actual and imagined geographies. The article goes on to propose that the critical understanding of the processual intertextuality of literary cartographies might be further enhanced by the use of a suite of geo-location technologies; and, ultimately, it suggests that the future of critical literary cartography might be founded, at least in part, upon in-the-field digital mapping practices.

A Framework for Automated Extraction and Classification of Linear Networks

Linear features, including roads, are important components of geographic information system (GIS) databases. This article outlines a framework for extracting networks of linear features from imagery using an object-oriented geodata model. The Automated Linear Feature Identification and Extraction (ALFIE) uses a control strategy to automate the process flow. The authors discuss the flexibility of the resulting system, which incorporates a toolkit of algorithms and imagery to extract linear features and utilizes contextual information to allow evidence of class membership to be built up from a variety of sources. The classification algorithm employs a Bayesian modeling approach that incorporates both geometric and photometric information. In this approach, five key discriminators are identified: width, width variation, sinuosity, spectral value, and spectral value variation. The authors discuss the processes undertaken by the ALFIE system and quantitative results of the final output from the system in terms of classification accuracy and network completeness. The authors conclude that the adoption of an object-oriented geospatial database has allowed complex discriminating characteristics of objects to be dynamically extracted, effectively enabling objects to classify themselves.

The Influence of Digital Surface Model Choice on Visibility-based Mobile Geospatial Applications

In this work we investigate the effectiveness of different types of visibility models for use within location-based services. This article outlines the methodology and results for our experiments, which were designed to understand the accuracy and effects of model choices for mobile visibility querying. Harnessing a novel mobile media consumption and authoring application called Zapp, the levels of accuracy of various digital surface representations used by a line of sight visibility algorithm are extensively examined by statistically assessing randomly sampled viewing sites across the 1u2009km2 study area, in relation to points of interest (POI) across the University of Nottingham campus. Testing was carried out on three different surface models derived from 0.5u2009m LiDAR data by visiting physical sites on each surface model with 14 random point of interest masks being viewed from between 10 and 16 different locations, totalling 190 data points. Each site was ground-truthed by determining whether a given POI could be seen by the user and could also be identified by the mobile device. Our experiments in a semi-urban area show that choice of surface model has important implications for mobile applications that utilize visibility in geospatial query operations.