With increasing access to open spatial data, it is possible to improve the quality of analyses carried out in the preliminary stages of the investment process. The extraction of buildings from raster data is an important process, especially for urban, planning and environmental studies. It allows, after processing, to represent buildings registered on a given image, e.g., in a vector format. With an actual image it is possible to obtain current information on the location of buildings in a defined area. At the same time, in recent years, there has been huge progress in the use of machine learning algorithms for object identification purposes. In particular, the semantic segmentation algorithms of deep convolutional neural networks which are based on the extraction of features from an image by means of masking have proven themselves here. The main problem with the application of semantic segmentation is the limited availability of masks, i.e., labelled data for training the network. Creating datasets based on manual labelling of data is a tedious, time consuming and capital-intensive process. Furthermore, any errors may be reflected in later analysis results. Therefore, this paper aims to show how to automate the process of data labelling of cadastral data from open spatial databases using convolutional neural networks, and to identify and extract buildings from high resolution orthophotomaps based on this data. The conducted research has shown that automatic feature extraction using semantic ML segmentation on the basis of data from open spatial databases is possible and can provide adequate quality of results.
Introduction. So far there have been few studies on the effect of interval training with active recovery aimed at increasing aerobic power on the physical capacity of long-distance runners. Unlike standard interval training, this particular type of interval training does not include passive rest periods but combines high-intensity training with low-intensity recovery periods. The aims of the study were to determine the effect of aerobic power training implemented in the form of interval training with active recovery on the physical capacity of amateur long-distance runners as well as to compare their results against those of a group of runners who trained in a traditional manner and only performed continuous training. Material and methods. The study involved 12 recreational male long-distance runners, who were randomly divided into two groups, consisting of 6 persons each. Control group C performed continuous training 3 times a week (for 90 minutes, with approximately 65-85% VO2max). Experimental group E participated in one training session similar to the one implemented in group C and additionally performed interval training with active recovery twice a week. The interval training included a 20-minute warm-up and repeated running sprints of maximum intensity lasting 3 minutes (800-1,000 m). Between sprints, there was a 12-minute bout of running with an intensity of approximately 60-70% VO2max. The time of each repetition was measured, and the first one was treated as a benchmark in a given training unit. If the duration of a subsequent repetition was 5% shorter than that of the initial repetition, the subjects underwent a 15-minute cool-down period. A progressive treadmill test was carried out before and after the 7-week training period. The results were analysed using non-parametric statistical tests. Results. VO2max increased significantly both in group E (p < 0.05; d = 0.86) and C (p < 0.05; d = 0.71), and there was an improvement in effort economy at submaximal intensity. Although the differences were not significant, a much greater change in the post-exercise concentrations of lactate and H+ ions was found in group E. Conclusions. The study showed that interval training with active recovery increased VO2max in amateur runners with higher initial physical capacity and stimulated adaptation to metabolic acidosis more than continuous training.
The integration of BIM (Building Information Modeling) and GIS (Geographic Information System) technologies allows for added value in many fields; starting from the construction industry to administrative operations. However, the issue of integration is currently quite challenging. This is due to the lack of consistency (inter alia, a lack of standards) in the integration of both technologies. It is the result of the different primary use of BIM and GIS. The use of BIM and GIS integration has great potential, especially in the construction industry. Therefore, it was decided to analyze the strengths and weaknesses of integration as well as the opportunities and threats in the future by performing a SWOT analysis. The analysis was performed cross-sectionally based mainly on the existing literature. Finally, six strengths, five weaknesses, five opportunities, and four threats were identified and described.
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