1,720,959 research outputs found
A Customized JAVA OpenStreetMap Preset to Extract Solar Panel Installations for Humanitarian Purposes
No full textThe use of clean and renewable energies, such as solar power, is essential for improving local economies, reducing reliance on scarce fossil fuels, and mitigating climate change. However, although solar power harvested using PhotoVoltaic (PV) cells has grown significantly in recent years, the actual amount of energy produced is unknown and challenging to define because of the lack of geographic data on the number of PV panels installed on rooftops. Due to the low spatial resolution of open-source satellite images, free surveying PV small-scale installations is currently not feasible. YouthMappers, an academic network dedicated to the creation and use of open mapping for development and humanitarian purposes, offers a possible solution. Indeed, it is an effective method to gather free detailed information on a large scale thanks to the support of high-resolution satellite images such as MapBox, Bing, or DigitalBox in an open-source environment, like Java OpenStreetMap (JOSM). As a result, in this study, an ad hoc tool written in JOSM was created to map PV panels on rooftops manually. This preset collects all of the information needed to describe PV panel features, such as type, size, and orientation, and calculate the amount of energy produced. Furthermore, its interface is simple and easy to use for both Information Technology (IT) and non-IT users. All data collected is stored in a geodatabase accessible to local governments, communities, industries, and scientists, allowing for a global overview of installed PV panel systems, the potential amount of energy produced, and the tracking of their evolution over time
Evaluation of eCognition Developer and Orfeo ToolBox Performances for Segmenting Agrophotovoltaic Systems from Sentinel-2 Images
No full textA deep energy transformation will be tried out across the globe in the next decades in order to detect potential green and renewable sources able to replace fossil fuels. Among the various alternatives, photovoltaic technology, recognized as sustainable, clean, and environmentally friendly essence, is considered one of the most relevant solutions. To date, the Remotely Piloted Aircraft System has been largely used to inspect solar parks albeit the treatment of very high-resolution satellite images through object-based models may be a valid option. In this work, the potentialities of two segmentation approaches (multi-resolution and mean-shift algorithms, implemented in eCognition Developer and Orfeo Toolbox software, respectively) in extracting photovoltaic panels from Sentinel-2 time series were explored and compared. Such techniques were tested in Montalto di Castro in Viterbo (Italy). Multi-resolution algorithm was applied by varying scale and shape parameters between 20 and 100 and 0.1 and 0.5, respectively. Conversely, the mean-shift approach was used by considering the default values of spatial radius and range radius. Their segmentation outcomes were compared on the base of i) minimum Euclidean Distance 2 (ED2), calculated in AssesSeg environment, ii) segmentation polygons statistics and areas value, and, lastly, iii) their performance in terms of processing time, versatility, ability of handling heavy data, and cost. ECognition Developer demonstrated a better performance in segmenting Sentinel 2-images for extracting PV systems in terms of segmentation parameters management and outcomes interpretation ability
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
Combining OBIA approach and Machine Learning algorithm to extract photovoltaic panels from Sentinel-2 images automatically
No full textThe Energy Union Framework Strategy is pushing the entire world to move from fossil fuels to renewable energy to tackle climate changes and mitigate their effects. Among the clean energy alternatives, the sun is recognized as the most abundant and inexhaustible source and the energy production can be carried out through photovoltaic panels. Nevertheless, such a solar park requires the use of large land areas, stolen, in such a way, from food production, which demand has strongly increased in the last few years due to the growing world population. Thus, agrophotovoltaic systems, also known as agrivoltaic structures, are under way to meet the above-mentioned needs synergistically. This has led to the necessity of monitoring solar panels amount and allocation. Their detection is challenging since, albeit their spectral signature is totally different from that one emitted from other land covers, their occurrence received little attention in the field of remote sensing. Thus, in this study, a proper rule-based model for distinguishing photovoltaic panels developed on eCognition environment was proposed. Such a model is based on the combination of Object-Based Image Analysis and machine learning algorithm. Indeed, after optimizing segmentation parameters and analyzing morphological features of the panels, the Random Forest classification algorithm was implemented. Lastly, classification accuracy was evaluated. The experimentation was conducted on the study area of Viterbo (Lazio Region, Italy) by adopting open medium-resolution satellite data (Sentinel 2). This research showed promising results in classifying targets for almost all months of the time series, except for the months of October and November where there is a lowering of the accuracy value due to the variability of spectral signatures
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
Optimizing Feature Selection for Solar Park Classification: Approaches with OBIA and Machine Learning
No full textIn the context of climate change mitigation, the crucial role of solar energy as an inexhaustible and renewable source is underlined by the widespread integration of photovoltaic (PV) panels. To keep track of the rapid expansion of extensive PV systems (such as solar parks), it is important to keep monitoring their quantity, distribution, and impact. Despite the many complications involved in mapping PV systems, stemming from the diversity of materials and field layouts, many researchers have focused on ways to improve this process through Remote Sensing techniques aided by Machine Learning and Deep Learning. In this study, multiple object-based image (OBIA) classification models were created for mapping large solar parks, using open-source data (Sentinel-2) and computational tools such as Google Earth Engine (GEE) and RStudio. Thecase study was conducted in Pavagada Solar Park, India, which is the third largest solar park in the world spanning over 153 km2. For training, validation, and testing purposes two zones within the park were identif ied. Data were collected for four seasonal periods; a classification model was developed for each season while validating them. The most accurate model was then implemented in the test area. The selection of features for the Random Forest (RF) classifier was done using the Recursive Feature Elimination (RFE) method, with the main objective of eliminating nonrelevant features while preserving only the significant ones. This analysis showed a substantial improvement in accuracy, thereby validating the effectiveness of the RFE algorithm in feature selection. This is particularly true in the autumn when a maximum Overall Accuracy (OA) of 88.72% was achieved
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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