Up-to-date information about the Earth’s surface provided by land cover maps is essential for numerous environmental and land management applications. There is, therefore, a clear need for the continuous and reliable monitoring of land cover and land cover changes. The growing availability of high resolution, regularly collected remote sensing data can support the increasing number of applications that require high spatial resolution products that are frequently updated (e.g., annually). However, large-scale operational mapping requires a highly-automated data processing workflow, which is currently lacking. To address this issue, we developed a methodology for the automated classification of multi-temporal Sentinel-2 imagery. The method uses a random forest classifier and existing land cover/use databases as the source of training samples. In order to demonstrate its operability, the method was implemented on a large part of the European continent, with CORINE Land Cover and High-Resolution Layers as training datasets. A land cover/use map for the year 2017 was produced, composed of 13 classes. An accuracy assessment, based on nearly 52,000 samples, revealed high thematic overall accuracy (86.1%) on a continental scale, and average overall accuracy of 86.5% at country level. Only low-frequency classes obtained lower accuracies and we recommend that their mapping should be improved in the future. Additional modifications to the classification legend, notably the fusion of thematically and spectrally similar vegetation classes, increased overall accuracy to 89.0%, and resulted in ten, general classes. A crucial aspect of the presented approach is that it embraces all of the most important elements of Earth observation data processing, enabling accurate and detailed (10 m spatial resolution) mapping with no manual user involvement. The presented methodology demonstrates possibility for frequent and repetitive operational production of large-scale land cover maps.

Light alloys like aluminum and its alloys have excellent physical and mechanical properties for a number of applications. The use of aluminum alloys can significantly decrease the mass of automobiles without decreasing structural strength. Therefore, the reason of this work was to determine the optimal cooling rate values, to achieve good mechanical properties for protection of this aluminum cast alloy from losing their work stability, and to make it more resistant to action in hard working conditions. The carried out investigations have allow to found that changes in the cooling rate do not cause changes in the phase composition, revealing the Al 2 Cu and Al 5 FeSi phase especially, but only changes the morphology of a ? b eutectic as well as the particle size and secondary dendrite arm spacing. As a result, the number of fine crystals in per unit volume increases, leading to a fine grain structure, which influences the recalescence temperature. The purpose of this research work is to investigate the thermoderivative interdependencies occurred in analyzed aluminum cast alloys using Universal Metallurgical Simulator and Analyzer. For the investigation, the cast AlSi9Cu aluminum alloy was used. As a result of this research, the cooling rate influence on the structure and mechanical properties changes was investigated. The cooling rate was set in a variable range of 0.16-1.25°C s -1 , where the cooling rate of 0.16°C s -1 corresponds to freely cooling, without any forced air flow.

In this study, the change of the cooling rate in the range of about 0.1-1°C s -1 and the addition of Sr on the crystallization kinetics of the cast zinc alloys of the ZnAlCu type, as well as its relation to the microstructure were also investigated. Therefore, the aim of the rapid crystallisation is the achievement of materials with better properties, which can be obtained by refinement of the dendritic or eutectic microstructure, elimination of segregation, or creation of metastable phases and their morphology changes. In the investigated alloys, the change of cooling rate of 1°C s -1 has caused microstructure's refinement as well as increase in hardness. Increase in the cooling rate causes also morphology changes of the g ? a eutectic, and makes generally a global overcooling of the alloy as well as change in the temperatures at the beginning of crystallization T DN and of the alloy crystallization T S . The presented investigations concerning the electron microscopy methods, including transmission electron microscopy, allow revealing the crystallographic structure, based on the d-spacing changes, as well as the diffraction method used for phase determination, which is a helpful tool for the explanation of the important points in the thermo-derivative analysis curve, where the relation between the amount of phase and the occurrence of new phases can be determined.

In this work we analyse fractal and multifractal characteristics for description and extraction of information from VHR satellite images. We propose the degree of multifractality as a global descriptor of satellite image content and investigate its usefulness for classification of WorldView-2 image chips into main land cover types. The research confirmed that it is possible to use the textural features as efficient global descriptors of WorldView-2 panchromatic image content. Results show that the degree of multifractality is related to land cover type prevailing in the imaged area. It was also proved that multifractal parameters should be considered as valuable textural features in the context of land cover classification.