The main goal of this study is to evaluate different models for further improvement of the accuracy of land use and land cover (LULC) classification on Google Earth Engine using random forest (RF) and support vector machine (SVM) learning algorithms. Ten indices, namely normalized difference vegetation index, normalized difference soil index, index-based built-up index, biophysical composition index, built-up area extraction index (BAEI), urban index, new built-up index, band ratio for built-up area, bare soil index, and normalized built up area index, were used as input parameters for the machine learning algorithms to improve classification accuracy. The combinatorial analysis of the Sentinel-2 bands and the aforementioned indices allowed us to create four combinations based on surface reflectance characteristics. The study includes data from April 2020 to September 2021 and April 2022 to June 2022. The multitemporal Sentinel-2 data with spatial resolutions of 10 m were used to determine the LULC classification. The major land use classes such as water, forest, grassland, urban areas, and other lands were obtained. Generally, the RF algorithm showed higher accuracy than the SVM. The overall accuracy for RF and SVM was 86.56% and 84.48%, respectively, and the mean Kappa was 0.82 and 0.79, respectively. Using the combination 2 with the RF algorithm and combination 4 with the SVM algorithm for LULC classification was more accurate. The additional use of vegetation indices allowed to increase in the accuracy of LULC classification and separate classes with similar reflection spectra.
Aim of the study: The main purpose of the study is the analysis and assessment of anthropogenically transformed landscapes of Bila Tserkva (Ukraine) based on a combination of remote sensing methods and GIS mapping Material and methods: Usage of geoinformatics methods for mapping anthropogenically transformed landscapes of Bila Tserkva is studied. The data was downloaded and processed using the Semi-Automatic Classification Plugin QGIS for the supervised classification of remote sensing data. Satellite images were radiometrically calibrated and atmospherically corrected, followed by a controlled classification with signature creation, visualization of spectral profiles, quality assessment and post-processing Results and conclusions: The main methods of landscape research are analyzed. The conclusion is made about the expediency of using spectrophotometry of satellite images in order to identify different types of landscapes based on satellite data. An supervised classification of satellite images different-time images was performed, as a result of which the main Bila Tserkva landscape types were identified. Those identified types are: water bodies, vegetation (grass, forest, parks) urban areas and bare soils. Spatio-temporal changes of landscapes are studied and these changes are described in quantitative indicators
Industrial equipment is a dynamic system that deforms during installation (assembly) and during operation. Under the influence of variable load and mixing of the center of gravity of the equipment and foundations on which it is installed, uneven horizontal and vertical displacements occur, therefore individual equipment elements are unevenly deformed, which can lead to poor performance or stoppage of this equipment. Timely measurement of the displacement of certain points of equipment (deformations) of precision equipment with the help of geodetic and other methods and their correct use for correcting the geometry of the equipment will contribute to improving the operational properties and increasing the period of uninterrupted operation of equipment’s, for example, precision conveyor lines for assembling cars.
. In the article, the authors had done a brief analysis of existing modern, traditional methods and tools that allow to determine the planned coordinates of geodetic signs, located on the last tier of super-high engineering structures, paid special attention to the disadvantages and concluded that it’s necessary to develop a method and device for determining the geodetic coordinates on ultra-high engineering structures with high accuracy to provide engineering and geodetic works during the construction and operation of high-rise structures. In the article, the authors propose their method and device for determining the planar coordinates of the upper geodetic sign of the line of vertical design on ultra-high engineering structures with high accuracy, which is based on the method of the straight linear resection by the light distance meter. The result of the proposed method is the enhancing of the accuracy of engineering and geodetic works during the construction and control of geometric parameters of high-rise structures. This method of distance measurements allows getting the enhancing of the accuracy of the engineering and geodetic measurements by fixing the moment of occurrence of the double frequency with root mean square error (RMSE) above 0.5 mm, thus eliminating the need to measure the phase difference between direct and reflected pulses. A particular advantage of the proposed method is that the accuracy of the measurements depends on the comparison of the radiated f and double fg frequencies, which makes the measurement precision.
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