Utilizing deep Hubble Space Telescope imaging from the two largest field galaxy surveys, the Extended Groth Strip and the Cosmic Evolution Survey (COSMOS), we examine the structural properties, and derive the merger history for 21 902 galaxies with M* > 1010 M⊙ at z < 1.2. We examine the structural concentration, asymmetry and clumpiness (CAS) parameters of these galaxies, deriving merger fractions, at 0.2 < z < 1.2, based on the asymmetry and clumpiness values of these systems. We find that the merger fraction between z= 0.2 and 1.2 increases from roughly fm= 0.04 ± 0.01 to 0.13 ± 0.01. We furthermore detect, at a high significance, an abrupt drop in the merger fraction at z < 0.7, which appears relatively constant from z= 0.7 to 1.2. We explore several fitting formalism for parametrizing the merger fraction, and compare our results to other structural studies and pair methods within the DEEP2, VVDS and Cosmic Evolution Survey (COSMOS) fields. We also examine the basic features of these galaxies, including our selection for mergers, and the inherent error budget and systematics associated with finding mergers through structure. We find that for galaxies selected by M* > 1010 M⊙, the merger fraction can be parametrized by fm=f0× (1 +z)m with the power‐law slope m= 2.3 ± 0.4. By using the best available z= 0 prior the slope increases to m= 3.8 ± 0.2, showing how critical the measurement of local merger properties is for deriving the evolution of the merger fraction. We furthermore show that the merger fraction derived through structure is roughly a factor of 3–6 higher than pair fractions. Based on the latest cosmological simulations of mergers, we show that this ratio is predicted, and that both methods are likely tracing the merger fraction and rate properly. We calculate, utilizing merger time‐scales from simulations and previously published merger fractions within the Hubble Deep and Ultra Deep Fields, that the merger rate of galaxies with M* > 1010 M⊙ increases linearly between z= 0.7 and 3. Finally, we show that a typical galaxy with a stellar mass of M* > 1010 M⊙ undergoes between 1 and 2 major mergers at z < 1.2.

3D modelling of indoor environment plays an important role in various applications such as indoor navigation, BIM (Building Information Modelling), interactive visualization, emergency response, and so on. While automated reconstruction of 3D models from point clouds is receiving more and more attention. Indoor modelling remains a challenging task in terms of dealing with the complexity of indoor environment, the level of automation and restrictions of input data. To address these issues, an automatic indoor reconstruction method that quickly and effectively reconstructs indoor environment of multi-floors and multi-rooms using both point clouds and trajectories from mobile laser scanning (MLS) is proposed. The proposed automatic method of parametric structure modelling comprises of three steps. Firstly, structural elements, such as doors, windows, walls, floors, and ceilings, are extracted based on the geometric and semantic features of point clouds. Then, the point clouds are automatically segmented into disjoint segments likes rooms through a combination of visibility analysis and physical constraints of the structural elements, which ensures the integrity of the room-space partitions and yields priors for the definition of point cloud label for reconstructed model. Finally, 3D models of individual rooms are Manuscript Yang Cui received the B.S. and M.S. degrees in school of surveying and geographical sciences from Liaoning Engineering Technology University, China, in 2013 and 2016. She is currently reading doctorate of the college of information engineering, Shenzhen University. Her research interests include laser scanning and computer graphics, with a focus on 3-D indoor modeling.Qing quan Li received the B.S. degree in engineering survey, the M.S. degree, and the Ph.D. degree in photogrammetry and remote sensing from Wuhan

3D modelling of indoor environment is essential in smart city applications such as building information modelling (BIM), spatial location application, energy consumption estimation, and signal simulation, etc. Fast and stable reconstruction of 3D models from point clouds has already attracted considerable research interest. However, in the complex indoor environment, automated reconstruction of detailed 3D models still remains a serious challenge. To address these issues, this paper presents a novel method that couples linear structures with three-dimensional geometric surfaces to automatically reconstruct 3D models using point cloud data from mobile laser scanning. In our proposed approach, a fully automatic room segmentation is performed on the unstructured point clouds via multi-label graph cuts with semantic constraints, which can overcome the over-segmentation in the long corridor. Then, the horizontal slices of point clouds with individual room are projected onto the plane to form a binary image, which is followed by line extraction and regularization to generate floorplan lines. The 3D structured models are reconstructed by multi-label graph cuts, which is designed to combine segmented room, line and surface elements as semantic constraints. Finally, this paper proposed a novel application that 5G signal simulation based on the output structural model to aim at determining the optimal location of 5G small base station in a large-scale indoor scene for the future. Four datasets collected using handheld and backpack laser scanning systems in different locations were used to evaluate the proposed method. The results indicate our proposed methodology provides an accurate and efficient reconstruction of detailed structured models from complex indoor scenes.

In this paper, an improved method based on a mixture of Gaussian and quadrilateral functions is presented to process airborne bathymetric LiDAR waveforms. In the presented method, the LiDAR waveform is fitted to a combination of three functions: one Gaussian function for the water surface contribution, another Gaussian function for the water bottom contribution, and a new quadrilateral function to fit the water column contribution. The proposed method was tested on a simulated dataset and a real dataset, with the focus being mainly on the performance of retrieving bottom response and water depths. We also investigated the influence of the parameter settings on the accuracy of the bathymetry estimates. The results demonstrate that the improved quadrilateral fitting algorithm shows a superior performance in terms of low RMSE and a high detection rate in the water depth and magnitude retrieval. What’s more, compared with the use of a triangular function or the existing quadrilateral function to fit the water column contribution, the presented method retrieved the least noise and the least number of unidentified waveforms, showed the best performance in fitting the return waveforms, and had consistent fitting goodness for all different water depths.

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