1,720,968 research outputs found
Automated detection of archaeology in the New Forest using deep learning with remote sensor data
No full textAs a result of the New Forest Knowledge project, many new sites were discovered. This was partly due to the undertaken LiDAR survey which was followed by an intensive manual process to interpret the results. The research presented in this paper looks at methods to automate this process especially for round barrow detection using deep learning.Traditionally, automated methods require manual feature engineering to extract the visual appearance of a site on remote sensing data. Whereas this approach is difficult, expensive and bound to detect a single type of site, recent developments have moved towards automated feature learning of which deep learning is the most notable. In our approach, we use known site locations together with LiDAR data and aerial images to train Convolutional Neural Networks (CNNs). This network is typically constructed of many layers with each representing a different filter (e.g. to detect lines or edges). When this network is trained, each new site location that is fed to the network will update the weights of features to better represent the appearance of sites in the remote sensing data. For this learning process, an accurate dataset is required with a lot of examples and therefore the New Forest is a very suitable case study, especially thanks to the extensive research of the New Forest Knowledge project.In this paper, our latest results will be presented together with a future perspective on how we can scale our approach to a country wide detection method when computing power becomes even more efficient
The Building Blocks of User-Focused 3D City Models
No full textAt Ordnance Survey, GB, we have taken an incremental approach to creating our 3D geospatial database. Research at Ordnance Survey has focused not only on methods for deriving 3D data, but also on the needs of the user in terms of the actual tasks they perform. This provides insights into the type and quality of the data required and how its quality is conveyed. In 2007, using task analysis and user-centred design, we derived a set of geometric characteristics of building exteriors that are relevant to one or more use contexts. This work has been valuable for guiding which building data to collect and how to augment our products. In 2014, we began to supply building height attributes as an alpha-release enhancement to our 2D topography data, OS MasterMap® Topography Layer. This is the first in a series of enhancements of our 2D data that forms part of a road map that will ultimately lead to a full range of 3D products. This paper outlines our research journey from the understanding of the key 3D building characteristics to the development of geo-spatial products and the specification of research. There remains a rich seam of research into methods for capturing user-focused, geo-spatial data to enable visualisation and analysis in three dimensions. Because the process of informing and designing a product is necessarily focused on the practicalities of production, storage and distribution, this paper is presented as a case report, as we believe our journey will be of interest to others involved in the capture of 3D buildings at a national level
Classification images for aerial images capture visual expertise for binocular disparity and a prior for lighting from above
Full text linkUsing a novel approach to classification images (CIs), we investigated the visual expertise of surveyors for luminance and binocular disparity cues simultaneously after screening for stereoacuity. Stereoscopic aerial images of hedges and ditches were classified in 10,000 trials by six trained remote sensing surveyors and six novices. Images were heavily masked with luminance and disparity noise simultaneously. Hedge and ditch images had reversed disparity on around half the trials meaning hedges became ditch-like and vice versa. The hedge and ditch images were also flipped vertically on around half the trials, changing the direction of the light source and completing a 2 × 2 × 2 stimulus design. CIs were generated by accumulating the noise textures associated with "hedge" and "ditch" classifications, respectively, and subtracting one from the other. Typical CIs had a central peak with one or two negative side-lobes. We found clear differences in the amplitudes and shapes of perceptual templates across groups and noise-type, with experts prioritizing binocular disparity and using this more effectively. Contrariwise, novices used luminance cues more than experts meaning that task motivation alone could not explain group differences. Asymmetries in the luminance CIs revealed individual differences for lighting interpretation, with experts less prone to assume lighting from above, consistent with their training on aerial images of UK scenes lit by a southerly sun. Our results show that (i) dual noise in images can be used to produce simultaneous CI pairs, (ii) expertise for disparity cues does not depend on stereoacuity, (iii) CIs reveal the visual strategies developed by experts, (iv) top-down perceptual biases can be overcome with long-term learning effects, and (v) CIs have practical potential for directing visual training
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Opportunities for machine learning and artificial intelligence in a national mapping agency: a perspective on enhancing ordnance survey workflow
No full textNational Mapping agencies (NMA) are tasked with providing highly accurate geospatial data for a range of customers. This challenge has traditionally been met by combining remote sensing data gathering, field work and manual interpretation and processing of the data. This is a significant logistical undertaking which requires novel approaches to improve potential feature extraction from the available data. Using research undertaken at Great Britain’sNMA, Ordnance Survey (OS)as an example, this paper provides an overview of recent advances in the use of artificial intelligence (AI)to assist in improving feature classification from remotely sensed aerial imagery, describing research using high level neural network architecture to image classification that utilisesconvolutional neural network learning
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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