The extent to which member states transpose EU directives in a timely fashion is often argued to be strongly associated with the general effectiveness of national bureaucracies. But what kind of institutional solutions ensure better performance? This paper examines the patterns and effects of departmental oversight across 28 ministries in Estonia, Hungary, Poland and Slovenia. In mapping the strength of oversight, it relies on around 90 structured interviews regarding the rules-in-use on transposition planning, legal review and monitoring of deadlines. The analysis of the impact of departmental oversight is based on an original dataset of over 300 directives with transposition deadlines between January 2005 and December 2008.

On the european union

Landslides are considered to be among the most alarming natural hazards. Therefore, there is a growing demand for databases and inventories of these events worldwide, since they are a vital resource for landslide risk assessment applications. Given the recent advances in the field of image processing, the objective of this study is to evaluate the performance of a deep convolutional neural network architecture called U-Net for the mapping of landslide scars from satellite imagery. The question that drives the study is: can fully convolutional neural networks be successfully applied as the backbone of automatic frameworks for building landslide inventories, keeping or improving the identification accuracy and agility when compared to other methods? To seek for an answer to it, scenes from the Landsat-8 satellite of a region of Nepal were obtained and processed in order to compose a landslide image database that served as the basis for the training, validation and test of deep convolutional neural networks. The U-Net architecture was applied and the results indicate that it has the potential to identify landslide scars, improving over previously published research on the topic for the same study region. The validation process resulted in recall, precision and F1-score values of 0.74, 0.61 and 0.67, respectively, thus higher than those from previous studies using different methodologies. The results indicate the potential of the method to be applied in dynamic mapping systems for landslide scar identification, which paves the way to the composition and updating of landslide scar databases. These, in turn, can support a great deal of quantitative landslide susceptibility mapping methods that heavily rely on data to provide accurate results.

Convolutional neural networks applied to semantic segmentation of landslide scars

Deep learning and convolutional neural network technologies are increasingly used in the problems of analysis, segmentation and recognition of objects in images. In this article a convolutional neural network for automated wildfire detection on high-resolution aerial photos is presented. Two databases of satellite RGB-images with different spatial resolution containing 1457 and 393 high-resolution images, respectively, were prepared for training and testing the neural network. Various techniques of data augmentation are used to enlarge training and test sets generated by data windowing. U-Net neural network with the ResNet34 as encoder was used in research. Neural network training was learning using the NVIDIA DGX-1 supercomputer. Adaptive moment estimation algorithm was used for optimization of training process. Special metrics, such as Sorensen-Dice coefficient, precision, recall, F1-score and IoU value allows to measure the quality of developed model. The developed algorithm can be successfully applied for early wildland fires detection in practical applications.

Wildfire Segmentation on Satellite Images using Deep Learning

Amazon forest cover change mapping based on semantic segmentation by U-Nets

Automatic segmentation of TBM muck images via a deep-learning approach to estimate the size and shape of rock chips

This article presents research results of two convolutional neural networks for building detection on satellite images of Planet database. To analyze the quality of developed algorithms, there was used Sorensen-Dice coefficient of similarity which compares results of algorithms with tagged masks. The masks were generated from json files and sliced on smaller parts together with respective images before the training of algorithms. This approach allows to cope with the problem of segmentation for aerial high-resolution images efficiently and effectively. The problem of building detection on satellite images can be put into practice for urban planning, building control, etc.

Building Detection on Aerial Images Using U-NET Neural Networks

Convolutional neural networks for detection geo-objects on the satellite images from DSTL, Landsat -8 and PlanetScope databases were analyzed. Three modification of convolutional neural network architecture for implementing the recognition algorithm was used. Images obtained from the Landsat -8 and PlanetScope satellites are used for estimation of automatic object detection quality. To analyze the accuracy of the object detection algorithm, the selected regions were compared with the areas by previously marked by experts. An important result of the study was the improvement of the detector for the class “Forest”. Segmentation of satellite images has found application at urban planning, forest management, climate modelling, etc.

Comparison of Different Convolutional Neural Network Architectures for Satellite Image Segmentation

Results of training of convolutional neural network for satellite four-channel image segmentation are performed. Input images contain blue, green, red and near-infrared channels. The algorithm was trained to detect buildings and other urban areas. Modification of the U-Net neural network with two encoders was used. The values of Sorensen coefficient and Jaccard index were calculated for 16 different urban regions.

Modification of U-Net neural network in the task of multichannel satellite images segmentation

Purpose: to outline ways how to support the innovative and competencies management in modern corporations including those operating in IT and online banking sector Design/methodology/approach: the analysis of the best practices of competencies management including those which use IT-tools Findings: Analysis of advanced practices of competencies management shows thatmodern social and IT technologies can be used to solve business problems and form new unique competencies Among the most important examples of this kind of technology are electronic portals and platforms with different features, which allow attraction of a large number of internal and external experts to collaborate Modern Internet technologies can also help in collective collaboration in the innovative process itself Research implications: Though the form of IT portal for innovation process seems to prove its efficiency, the optimal functionality of a portal is still in question So the future investigation and testing is required, including the special research in the Internet banking sector, if a portal is designed for its special purposes Practical implications: The results of the analysis of the best practices in the field of competencies management can be used in the development of corporate tools aimed to form and control the unique competencies and competitiveness Social implications: Creation of modern corporate portal of this kind can help a corporation to achieve better results and to increase the competitiveness level both on domestic and foreign markets Originality/value: The analysis will help to improve the efficiency of management in companies, the growth of innovation activity of enterprises, as well as the competitiveness of products and services, which in especially actual in online finance sector

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The Modern Approach to Competencies Management Based on IT Solutions

The most important condition for increasing the competitiveness of business is the formation, retention, and development of key competences of the organization, which reflect the competitive advantage. This problem is especially urgent for high-tech industries, which are the most sensitive to all kinds of changes and innovations. The ways of applying the company's technological competences to form a business model, the proper form of competence description and analysis on the example of the company "Teplolux" are considered. The following from is recommended to use in IT solutions for competence databases.

https://iopscience.iop.org/article/10.1088/1757-899X/312/1/012003/pdf

Anna Ostrovskaya

Papers

Building Detection on Aerial Images Using U-NET Neural Networks

Comparison of Different Convolutional Neural Network Architectures for Satellite Image Segmentation

Modification of U-Net neural network in the task of multichannel satellite images segmentation

The Modern Approach to Competencies Management Based on IT Solutions

The approach to detection and application of the company’s technological competences to form a business-model