1,720,957 research outputs found
A virtual reality application for augmented panoramic mountain images
No full textVirtual reality is a powerful interaction mechanism that holds the promise of engaging users, not only for entertainment, but also for social and environmental purposes. In this paper we present PeakLensVR, a virtual reality mobile application that enables users to capture panoramic mountain images with their mobile devices and later visualize such images, enriched with metadata about the peaks visible from the capture point, with a low-end VR device. The application exploits a multi-stage data processing pipeline, which comprises the following steps: (1) the acquisition of a sequence of frames with the mobile phone camera and their annotation with sensor readings captured during the shooting session; (2) the creation of a panoramic image from the acquired frames, with state-of-the art stitching algorithms; (3) the registration of the panoramic image to the mountain skyline in view, by comparing the image skyline with a virtual profile extracted from the NASA SRTM Digital Elevation Model of the Earth; (4) the enrichment of the registered panoramic image with markers and metadata (name, altitude, etc.) of the peaks in view, by querying the OpenStreetMap GIS
Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods
No full textLandform detection and analysis from Digital Elevation Models (DEM) of the Earth has been boosted by the availability of high-quality public data sets. Current landform identification methods apply heuristic algorithms based on predefined landform features, fine tuned with parameters that may depend on the region of interest. In this paper, we investigate the use of Deep Learning (DL) models to identify mountain summits based on features learned from data examples. We train DL models with the coordinates of known summits found in public databases and apply the trained models to DEM data obtaining as output the coordinates of candidate summits. We introduce two formulations of summit recognition (as a classification or a segmentation task), describe the respective DL models, compare them with heuristic methods quantitatively, illustrate qualitatively their performances, and discuss the challenges of training DL methods for landform recognition with highly unbalanced and noisy data sets
A Testing Framework for Multi-Sensor Mobile Applications
Full text linkOutdoor mobile applications are becoming popular in many fields, such as gaming, tourism and environment monitoring. They rely on the input of multiple, possibly noisy sensors, such as the camera, Global Positioning System (GPS), compass, accelerometer and gyroscope. Testing such applications requires the reproduction of the real conditions in which the application works, which are hard to recreate without automated support. This paper presents a capture & replay framework that automates the testing of mobile outdoor applications; the framework records in real-time data streams from multiple sensors acquired in field conditions, stores them, and let developers replay recorded test sequences in lab conditions, also computing quality metrics that help tracing soft errors
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
A Deep Learning Model for Identifying Mountain Summits in Digital Elevation Model Data
No full textAnalyzing Digital Elevation Model (DEM) data to identify and classify landforms is an important task, which can contribute to improve the availability and quality of public open source cartography and to develop novel applications for tourism and environment monitoring. In the literature, several heuristic algorithms are documented for identifying the features of mountain regions, most notably the coordinate of summits. All these algorithms depend on parameters, which are manually set. In this paper, we explore the use of Deep Learning methods to train a model capable of identifying mountain summits, which learns from a gold standard dataset containing the coordinates of peaks in a region. The model has been trained and tested with Switzerland DEM and peak data
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
Crowdsourcing landforms for open GIS enrichment
No full textOpen Source Geographical Information Systems, such as OpenStreetMap (OSM), offer a valuable alternative to proprietary solutions for the development of voluntary environment monitoring systems. However, the quantity and quality of information stored in such systems must be carefully evaluated and the contributions of volunteers must be boosted by means of effective engagement methods. This paper reports the results of the assessment of the quality and quantity of OpenStreetMap mountain information: different types of information and world regions have different gaps and improvement requirements. To address this issue, we propose a hybrid approach, in which an open Digital Elevation Model data set is processed with a heuristic algorithm to find candidate mountain information and uncertainty in the automatically extracted candidates is reduced by means of voluntary expert crowdsourcing. The improvement of landform information (not only about mountains, but also about orography and hydrography in general) can support the development of environment monitoring applications
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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