The article describes how the Structure-from-Motion (SfM) method can be used to calculate the volume of anthropogenic microtopography. In the proposed workflow, data is obtained using mass-market devices such as a compact camera (Canon G9) and a smartphone (iPhone5). The volume is computed using free open source software (VisualSFMv0.5.23, CMPMVSv0.6.0., MeshLab) on a PCclass computer. The input data is acquired from video frames. To verify the method laboratory tests on the embankment of a known volume has been carried out. Models of the test embankment were built using two independent measurements made with those two devices. No significant differences were found between the models in a comparative analysis. The volumes of the models differed from the actual volume just by 0.7‰ and 2‰. After a successful laboratory verification, field measurements were carried out in the same way. While building the model from the data acquired with a smartphone, it was observed that a series of frames, approximately 14% of all the frames, was rejected. The missing frames caused the point cloud to be less dense in the place where they had been rejected. This affected the model’s volume differed from the volume acquired with a camera by 7%. In order to improve the homogeneity, the frame extraction frequency was increased in the place where frames have been previously missing. A uniform model was thereby obtained with point cloud density evenly distributed. There was a 1.5% difference between the embankment’s volume and the volume calculated from the camera-recorded video. The presented method permits the number of input frames to be increased and the model’s accuracy to be enhanced without making an additional measurement, which may not be possible in the case of temporary features.
The paper is dedicated to factors influencing users’ adoption of sustainable cloud computing solutions. The article covers the important characteristics related to cloud computing. It also discusses how sustainable cloud computing is important for sustainability. The current state of their security and potential threats waiting for users is reviewed. The purpose of this study is to investigate the influence of perceived usefulness, security, availability, and satisfaction on users’ adoption of sustainable cloud computing solutions. The study tested and used the adapted Technology Acceptance Model (TAM) model in the context of cloud computing solutions. The partial least square method of structural equation modeling is employed to test the proposed research model. The study utilizes an online survey to obtain data from 252 cloud computing solutions users. The data set was analyzed using SmartPLS 3 software. Results showed that the best predictor of users’ perceived usefulness and system & service quality is perceived availability, followed by perceived security. Both perceived usefulness and system & service quality predict users’ attitude and intention to use of cloud computing solutions. The findings improve understanding regarding the adoption of cloud computing solutions, and this work is, therefore, of particular interest to the IT departments and cloud computing vendors.
The study presents a new method for quantitative landscape assessment. The method uses LiDAR data and combines the potential of GIS (ArcGIS) and 3D graphics software (Blender). The developed method allows one to create Classified Digital Surface Models (CDSM), which are then used to create 360° panoramic images from the point of view of the observer. In order to quantify the landscape, 360° panoramic images were transformed to the Interrupted Sinusoidal Projection using G.Projector software. A quantitative landscape assessment is carried out automatically with the following landscape classes: ground, low, medium, and high vegetation, buildings, water, and sky according to the LiDAR 1.2 standard. The results of the analysis are presented quantitatively—the percentage distribution of landscape classes in the 360° field of view. In order to fully describe the landscape around the observer, graphs of little planets have been proposed to interpret the obtained results. The usefulness of the developed methodology, together with examples of its application and the way of presenting the results, is described. The proposed Quantitative Landscape Assessment method (QLA360) allows quantitative landscape assessment to be performed in the 360° field of view without the need to carry out field surveys. The QLA360 uses LiDAR American Society of Photogrammetry and Remote Sensing (ASPRS) classification standards, which allows one to avoid differences resulting from the use of different algorithms for classifying images in semantic segmentation. The most important advantages of the method are as follows: observer-independent, 360° field of view which simulates human perspective, automatic operation, scalability, and easy presentation and interpretation of results.
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