1,720,974 research outputs found
Classification of land cover forms using convolutional neural networks and high resolution orthophotomap
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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
Application of hyperspectral data and artificial neural networks for tree species classification of Karkonoski National Park
No full textZnajomość składu gatunkowego lasu jest ważnym zagadnieniem w zarządzaniu zasobamiśrodowiska leśnego. Główny nacisk powinien być położony na monitoring składu gatunkowegoposzczególnych zbiorowisk i ich rozmieszczenia przestrzennego. Praca skupiła się na opracowaniumetod identyfikacji gatunków drzew wykorzystując lotnicze dane hiperspektralne.Wysokorozdzielczy skaner hiperspektralny APEX (288 kanałów spektralnych w zakresie 413-2440nm o wielkości piksela 3,35 m) został użyty jako źródło danych do opracowania maprozmieszczenia wybranych gatunków drzew na obszarze Karkonoskiego Parku Narodowego. Wbadaniach wykonano mapę lokalizacji przestrzennej następujących gatunków: buk (Fagus sylvaticaL.), brzoza (Betula pendula Roth), olcha (Alnus Mill.), modrzew (Larix decidua Mill), sosna (Pinussylvestris L.) i świerk (Picea abies L. Karst). W celu zredukowania czasu przetwarzania danych,przeprowadzono procedurę wyboru najlepszych kanałów spektralnych. Zaszumione kanałyzobrazowania oraz te o niskiej jakości zostały usunięte (66 kanałów) przed analizą składowychgłównych (Principal Component Analysis – PCA). Po transformacji, zawartość informacji wkażdym kanale została obliczona wykorzystując współczynnik użyteczności kanału (band loading).Analiza PCA pozwoliła wybrać 40 kanałów spektralnych o największej zawartości informacji, którezostały użyte do klasyfikacji drzewostanów. Jako klasyfikator wykorzystano perceptronwielowarstwowy z jedną warstwą ukrytą. Symulowanie działania sztucznej sieci neuronowejprzeprowadzono przy użyciu programu R oraz paczki nnet. Przeprowadzono proceduręoptymalizacji parametrów uczenia oraz struktury (liczba neuronów w warstwie ukrytej) w celuotrzymania jak najlepszych wyników. Uzyskane wyniki zostały zweryfikowane na podstawiemarszruty terenowej. Rezultatem badań jest mapa rozmieszczenia gatunków drzewiastych.Uzyskane dane statystyczne (mediana dokładności całkowitej wyniosła 87% oraz współczynnikkappa 0,81) potwierdziły przydatność opracowanej metody oraz obrazów hiperspektralnych APEX,gdyż wszystkie sklasyfikowane gatunki uzyskały medianę dokładności producenta wyższą niż 68%.Najlepiej sklasyfikowały się świerki, buki i brzozy (mediana dokładności producenta wyniosłaodpowiednio 93, 88 i 83%. Sosna sklasyfikowała się uzyskując medianę dokładności producenta napoziomie 68% oraz mediana dokładności użytkownika 75%. Opracowana metoda potwierdziłapotencjał teledetekcji hiperspektralnej oraz sztucznych sieci neuronowych jako narzędzi dokartowania gatunków drzew.Knowledge of tree species composition in forest is an important topic in forest management.Accurate tree species maps allow acquiring more details of forest biophysical variables. Thisresearch focused on developing methods of tree species identification using aerial hyperspectraldata. Research area was the Karkonoski National Park located in south-western Poland. Highresolution (3,35m) APEX hyperspectral data (288 spectral bands in range from 413 to 2440 nm)were used as a basis for tree species classification. Beech (Fagus sylvatica L.), birch (Betulapendula Roth), alder (Alnus Mill.), larch (Larix decidua Mill), pine (Pinus sylvestris L.) and spruce(Picea abies L. Karst) were classified. Noisy bands (including water vapor absorption range) weretaken out of whole dataset before band selection procedure. Remaining bands went thought PCA(Principal Component Analysis) analysis to find out bands with highest information load. Eachband had its information load assessed and was ranked based on amount of information it held.Finally 40 most informative bands were selected for final classifications. Feed forward multilayered-perceptron with single hidden layer was applied. To simulate such network we used Rstatistical program and package nnet. Methods of the best artificial neural network architecturedetermination (number of neurons in hidden layer) and network training parameters were used. Theoutput maps were verified using field collected data. Final tree species maps cover whole area ofKPN; achieved median overall accuracy of 87%, with median producer accuracies for all classesexceeding 68%. Best classified classes were spruce, beech and birch with median produceraccuracies of 93%, 88% and 83% respectively. Class pine achieved lowest median producer anduser accuracies of 68% and 75%. Results show great potential in hyperspectral data as tool foridentifying tree species location in diverse mountainous forest
Application of hyperspectral data and artificial neural networks for tree species classification of Karkonoski National Park
Full text linkLink archiwalny https://depotuw.ceon.pl/handle/item/215217236
The influence of topographic factors on the state of vegetation in the Karkonosze National Park
No full textDispelling 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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