Search Results (5)

This bibliometric study investigates Open Access (OA) publication and citation trends in Austria, Israel, and Mexico from 2010 to 2020—three countries with comparable research output but differing OA infrastructures. (1) Background: The study examines how national OA policies, funding mechanisms, and transformative agreements (TAs) shape publication and citation patterns across disciplines. (2) Methods: Using Scopus data, the analysis focuses on four broad subject areas (health, physical, life, and social sciences), applying both three-way ANOVA and a Weighted OA Citation Impact index that adjusts citation shares based on the proportional representation of each subject area in national research output. An OA Engagement Score was also developed to assess each country’s policy and infrastructure support. (3) Results: OA publications consistently receive more citations than closed-access ones, confirming a robust OA citation advantage. Austria leads in both OA publication volume and weighted impact, reflecting its strong policy frameworks and TA coverage. Israel, while publishing fewer OA articles, achieves high citation visibility in specific disciplines. Mexico demonstrates strengths in repositories and Diamond OA journals but lags in transformative agreements. (4) Conclusions: National differences in OA policy maturity, infrastructure, and publishing models shape both visibility and citation impact. Structural limitations and indexing disparities may further affect how research from different regions and disciplines is represented globally, emphasizing the need for inclusive and context-sensitive frameworks for evaluating OA engagement. Full article

Building extraction refers to the automatic identification and separation of buildings from the background in remote sensing images. It plays a significant role in urban planning, land management, and disaster monitoring. Deep-learning methods have shown advantages in building extraction, but they still face challenges such as variations in building types, object occlusions, and complex backgrounds. To address these issues, SDSNet, a deep convolutional network that incorporates global multi-scale feature extraction and cross-level feature fusion, is proposed. SDSNet consists of three modules: semantic information extraction (SIE), multi-level merge (MLM), and semantic information fusion (SIF). The SIE module extracts contextual information and improves recognition of multi-scale buildings. The MLM module filters irrelevant details guided by high-level semantic information, aiding in the restoration of edge details for buildings. The SIF module combines filtered detail information with extracted semantic information for refined building extraction. A series of experiments conducted on two distinct public datasets for building extraction consistently demonstrate that SDSNet outperforms the state-of-the-art deep-learning models for building extraction tasks. On the WHU building dataset, the overall accuracy (OA) and intersection over union (IoU) achieved impressive scores of 98.86% and 90.17%, respectively. Meanwhile, on the Massachusetts dataset, SDSNet achieved OA and IoU scores of 94.05% and 71.6%, respectively. SDSNet exhibits a unique advantage in recovering fine details along building edges, enabling automated and intelligent building extraction. This capability effectively supports urban planning, resource management, and disaster monitoring. Full article

Open access (OA) publishing is beneficial for researchers to improve recognition, representation, and visibility in academia. However, few studies have been conducted for studying the association between gender and OA publishing likelihood. Therefore, the current study explores the impacts of gender-based authorship structures on OA publishing in Vietnamese social sciences and humanities. Bayesian analysis was performed on a dataset of 3122 publications in social sciences and humanities. We found that publications with mixed-gender authorship were most likely to be published under Gold Access terms (26.31–31.65%). In contrast, the likelihood of publications with the solely male or female author(s) was lower. It is also notable that if female researcher(s) held the first-author position in an article of mixed-gender authorship, the publication would be less likely to be published under Gold Access terms (26.31% compared to 31.65% of male-first-author structure). In addition, publications written by a solo female author (14.19%) or a group of female authors (10.72%) had lower OA publishing probabilities than those written by a solely male author(s) (17.14%). These findings hint at the possible advantage of gender diversity and the disadvantage of gender homophily (especially female-only authorship) on OA publishing likelihood. Moreover, they show there might be some negative impacts of gender inequality on OA publishing. As a result, the notion of gender diversity, financial and policy supports are recommended to promote the open science movement. Full article

This paper proposes a Nested Sliding Window (NSW) method based on the correlation between pixel vectors, which can extract spatial information from the hyperspectral image (HSI) and reconstruct the original data. In the NSW method, the neighbourhood window constructed with the target pixel as the centre contains relevant pixels that are spatially adjacent to the target pixel. In the neighbourhood window, a nested sliding sub-window contains the target pixel and a part of the relevant pixels. The optimal sub-window position is determined according to the average value of the Pearson correlation coefficients of the target pixel and the relevant pixels, and the target pixel can be reconstructed by using the pixels and the corresponding correlation coefficients in the optimal sub-window. By combining NSW with Principal Component Analysis (PCA) and Support Vector Machine (SVM), a classification model, namely NSW-PCA-SVM, is obtained. This paper conducts experiments on three public datasets, and verifies the effectiveness of the proposed model by comparing with two basic models, i.e., SVM and PCA-SVM, and six state-of-the-art models, i.e., CDCT-WF-SVM, CDCT-2DCT-SVM, SDWT-2DWT-SVM, SDWT-WF-SVM, SDWT-2DCT-SVM and Two-Stage. The proposed approach has the following advantages in overall accuracy (OA)—take the experimental results on the Indian Pines dataset as an example: (1) Compared with SVM (OA = 53.29%) and PCA-SVM (OA = 58.44%), NSW-PCA-SVM (OA = 91.40%) effectively utilizes the spatial information of HSI and improves the classification accuracy. (2) The performance of the proposed model is mainly determined by two parameters, i.e., the window size in NSW and the number of principal components in PCA. The two parameters can be adjusted independently, making parameter adjustment more convenient. (3) When the sample size of the training set is small (20 samples per class), the proposed NSW-PCA-SVM approach achieves 2.38–18.40% advantages in OA over the six state-of-the-art models. Full article

The convolutional neural network (CNN) has been gradually applied to the hyperspectral images (HSIs) classification, but the lack of training samples caused by the difficulty of HSIs sample marking and ignoring of correlation between spatial and spectral information seriously restrict the HSIs classification accuracy. In an attempt to solve these problems, this paper proposes a dual-branch extraction and classification method under limited samples of hyperspectral images based on deep learning (DBECM). At first, a sample augmentation method based on local and global constraints in this model is designed to augment the limited training samples and balance the number of different class samples. Then spatial-spectral features are simultaneously extracted by the dual-branch spatial-spectral feature extraction method, which improves the utilization of HSIs data information. Finally, the extracted spatial-spectral feature fusion and classification are integrated into a unified network. The experimental results of two typical datasets show that the DBECM proposed in this paper has certain competitive advantages in classification accuracy compared with other public HSIs classification methods, especially in the Indian pines dataset. The parameters of the overall accuracy (OA), average accuracy (AA), and Kappa of the method proposed in this paper are at least 4.7%, 5.7%, and 5% higher than the existing methods. Full article