LiDAR as a powerful system has been known in remote sensing techniques for 3D data acquisition and modeling of the earth’s surface. 3D reconstruction of buildings, as the most important component of 3D city models, using LiDAR Point Cloud has been considered in this study and a new data-driven method is proposed for 3D buildings modeling based on City GML standards. In particular, this paper focuses on the generation of an Enhanced Level of Details 1 (E-LOD1) of buildings containing multi-level flat-roof structures. An important primary step to reconstruct the buildings is to identify and separate building Points from other Points such as ground and vegetation Points. For this, a multi-agent strategy is proposed for simultaneous extraction of buildings and segmentation of roof Points from LiDAR Point Cloud. Next, using a new method named “Grid Erosion” the edge Points of roof segments are detected. Then, a RANSAC based technique is employed for approximation of lines. Finally, by modeling of the rooves and walls, the 3D buildings model is reconstructed. The proposed method has been applied on the LiDAR data over the Vaihingen city, Germany. The results of both visual and quantitative assessments indicate that the proposed method could successfully extract the buildings from LiDAR data and generate the building models. The main advantage of this method is the capability of segmentation and reconstruction of the flat buildings containing parallel roof structures even with very small height differences (e.g. 50 cm). In model reconstruction step, the dominant errors are close to 30 cm that are calculated in horizontal distance.

Today, LiDAR systems are used as a common technology to extract three-dimensional information which provide the possibility of fast and accurate generation of surface models. In LiDAR data correction process, systematic errors must be modeled based on LiDAR system calibration. Calibration process improves systematic errors by using LiDAR equation and raw measurements of GPS, the INS and laser scanner. Considering the impact of the calibration on the results optimal flight planning can be designed and by evaluating the compatibility degree between the footprints in overlapping strips, data quality control can be estimated. This paper suggests an approach for the quality control of the LiDAR Point Cloud. To evaluate the quality control the proposed method includes three steps: Primitives Extraction and Matching, Similarity Measure, Noise Level Verification. Finally, the results of each step are evaluated with an experimental test.

Building fixtures like lighting are very important to be modelled, especially when a higher level of modelling details is required for planning indoor renovation. LIDAR is often used to capture these details due to its capability to produce dense information. However, this led to the high amount of data that needs to be processed and requires a specific method, especially to detect lighting fixtures. This work proposed a method named Size Density-Based Spatial Clustering of Applications with Noise (SDBSCAN) to detect the lighting fixtures by calculating the size of the clusters and classifying them by extracting the clusters that belong to lighting fixtures. It works based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), where geometrical features like size are incorporated to detect and classify these lighting fixtures. The final results of the detected lighting fixtures to the raw Point Cloud data are validated by using F1-score and IoU to determine the accuracy of the predicted object classification and the positions of the detected fixtures. The results show that the proposed method has successfully detected the lighting fixtures with scores of over 0.9. It is expected that the developed algorithm can be used to detect and classify fixtures from any 3D Point Cloud data representing buildings.

The main object of this paper is to evaluate and discuss the Cloud Point temperature dependence on the solution nature in the crude oil. A correlation was also developed to predict the Cloud Point temperature in paraffin precipitation. The Cloud Point temperature is an important parameter to see whether there will be paraffin precipitation or not. So, we interested on the parameters that affect the Cloud Point temperature of pure paraffinic hydrocarbons. Therefore, besides literature survey, some experiments on paraffin precipitation and deposition have been performed and evaluated. We mean heavy paraffinic hydrocarbon components by the pure paraffins. Based on our experimental observations, the most important parameters that affect the hydrocarbon Cloud Point temperature are the apparent molecular weights of solution and solute, and solute weight fraction. These are the basic parameters of our developed correlation. In other words, the correlation is based on the intensive properties of the solution components such as their molecular weights and melting Point temperatures, and the extensive properties such as weight fractions. Through the regression process different equations have been used and a complex nonlinear polynomial equation was selected to obtain a correlation which predicts the Cloud Point temperature with less than 1.8 percent error. This modeled correlation is very simple to use and less time consuming with respect to the other models.

In recent years، Light Detection and Ranging (LiDAR) systems، as one of the active remote sensing laser technology، have become one of the most promising tools for measurements of Earth surface and its modeling. With the advent of airborne and satellite LiDAR systems، it has been possible to extract information and parameters related to the vertical structure of the targets، especially trees، while earlier، this was not possible by the use of passive remote sensing data such as multispectral images. Point Cloud generated by this sensors provides precise information of the targets on the laser path and their vertical distribution. Some of the applications of these systems are forest management، measurement of forest parameters، Digital Terrain Model generation، sea depth determination، the polar ice thickness determination، 3D city modeling، bridge and power line detection، costal mapping، open cast mapping and land cover classification. Due to the fact that the output of primary LiDAR systems (discrete LiDAR systems) is merely Point Cloud and is less associated with the intensity recorded for them، there are some limitations in some of its applications such as tree species classification and single tree detection especially in densely forested areas. Since 2004، new commercial airborne laser scanners، namely full waveform LiDAR Systems، have appeared. In recent years، recording the full waveform LiDAR data by these systems has made it possible to rectify some of the weaknesses of the discrete LiDAR systems such as low density of generated Point Cloud and their limitation in classification tasks; these systems made it possible to classify different tree species and classify targets more precisely by providing features of return waveforms such as amplitude and intensity of return pulses. One of the challenges related to these data is how to decompose return waveforms and generate Point Cloud and additional information related to waveforms. A great deal of research has been done on using discrete LiDAR data and its applications in forest management and 3D city modeling in Iran; However، full waveform LiDAR data، the process of decomposing LiDAR waveforms to Point Cloud and different decomposition methods are still unknown. Some of the most important reasons for this matter are unavailability of these data، lack of enough knowledge about the nature of this type of data، Lack of software especially free ones for processing them and the lack of information from commercial firms producing LiDAR sensors. In this research LiDAR waveforms of a forested area have been investigated and it has been tried to show how to decompose raw full waveform LiDAR data to 3D Point Cloud and extract information and features related to each return waveform. In addition in this research، the results of Point Cloud generated from full waveform LiDAR data is compared with Point Cloud acquired from LiDAR sensor to show how the density of LiDAR Point Cloud can be increased by full waveform analysis. Finally، the generated LiDAR Point Cloud is visualized based on its extracted features such as amplitude، width، intensity and number of return to show their application in clustering and classification tasks.

Over the past decades, urban growth has been known as a worldwide phenomenon that includes widening process and expanding pattern. While the cities are changing rapidly, their quantitative analysis as well as decision making in urban planning can benefit from two-dimensional (2D) and three-dimensional (3D) digital models. The recent developments in imaging and non-imaging sensor technologies, such as airborne Light Detection and Ranging (LiDAR) system, lead to a huge amount of remotely sensed data which can be employed to produce 2D/3D models. Although much of the previous researches have investigated on the performance improvement of the traditional data analyzing techniques, recently, more recent attention has focused on using probabilistic graphical models. However, less attention has paid to Conditional Random Field (CRF) method for the classification of the LiDAR Point Cloud dataset. Moreover, most researchers investigating CRF have utilized cameras or LiDAR Point Cloud; therefore, this paper adopted CRF model to employ both data sources. The methods were evaluated using ISPRS benchmark datasets for Vaihingen dataset on urban classification and 3D building reconstruction. The evaluation of this research shows that the performance of CRF model with an overall accuracy of 89. 06% and kappa value of 0. 84 is higher than other techniques to classify the employed LiDAR Point Cloud dataset.

In the present work a simple, fast, and green procedure is described for the purification and fractionation of carbon dots (CDs) coated by oxygencontaining groups. The CDs were synthesized by thermal pyrolysis of citric acid. The synthesis product was purified by separating them from the synthesis precursors. They also could be fractionated into two distinct kinds of CDs (f4 and f1) using pH-controlled Cloud-Point extraction (CPE) technique. The fractions f4 and f1 were characterized by transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), UV-Vis absorption, and FT-IR spectroscopy techniques. The results obtained were also used for studying and explaining the mechanism of the extraction procedure. It was revealed that the fractionation relies on the different surface chemistry of the CDs (i. e. the number or surface-density of oxygen-containing functional groups) which plays the main role in their different behavior at different pH values. Comparing fluorescence spectra of the separated fractions indicated that the surface-chemistry had also a marked effect on photoluminescence behavior of the CDs. The developed procedure can be used for the preparation of pure CDs or particular pure fractions of them for research laboratory purposes, or potentially for their industrial production, as well.