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Three-dimensional point cloud evaluation is used in photogrammetry to validate and assess the accuracy of data acquisition in order to generate various three-dimensional products. This paper determines the optimal accuracy and correctness of a 3D point cloud produced by a low-cost spherical camera in comparison to the 3D point cloud produced by laser scanner. The fisheye images were captured from a chessboard using a spherical camera, which was calibrated using the commercial Agisoft Metashape software (version 2.1). For this purpose, the results of different calibration methods are compared. In order to achieve data acquisition, multiple images were captured from the inside area of our case study structure (an underpass in Wiesbaden, Germany) in different configurations with the aim of optimal network design for camera location and orientation. The relative orientation was generated from multiple images obtained by removing the point cloud noise. For assessment purposes, the same scene was captured with a laser scanner to generate a metric comparison between the correspondence point cloud and the spherical one. The geometric features of both point clouds were analyzed for a complete geometric quality assessment. In conclusion, this study highlights the promising capabilities of low-cost spherical cameras for capturing and generating high-quality 3D point clouds by conducting a thorough analysis of the geometric features and accuracy assessments of the absolute and relative orientations of the generated clouds. This research demonstrated the applicability of spherical camera-based photogrammetry to challenging structures, such as underpasses with limited space for data acquisition, and achieved a 0.34 RMS re-projection error in the relative orientation step and a ground control point accuracy of nearly 1 mm. Compared to the laser scanner point cloud, the spherical point cloud reached an average distance of 0.05 m and acceptable geometric consistency.

Weed management is of crucial importance in precision agriculture to improve productivity and reduce herbicide pollution. In this regard, showing promising results, deep learning algorithms have increasingly gained attention for crop and weed segmentation in agricultural fields. In this paper, the U-Net++ network, a state-of-the-art convolutional neural network (CNN) algorithm, which has rarely been used in precision agriculture, was implemented for the semantic segmentation of weed images. Then, we compared the model performance to that of the U-Net algorithm based on various criteria.The results show that the U-Net++ outperforms traditional U-Net in terms of overall accuracy, intersection over union (IoU), recall, and F1-Score metrics. Furthermore, the U-Net++ model provided weed IoU of 65%, whereas the U-Net gave weed IoU of 56%. In addition, the results indicate that the U-Net++ is quite capable of detecting small weeds, suggesting that this architecture is more desirable for identifying weeds in the early growing season. [ABSTRACT FROM AUTHOR]

This paper focuses on the 3D modeling of the interior spaces of buildings. Three-dimensional point clouds from laser scanners can be considered the most widely used data for 3D indoor modeling. Therefore, the walls, ceiling and floor are extracted as the main structural fabric and reconstructed. In this paper, a method is presented to tackle the problems related to the data including obstruction, clutter and noise. This method reconstructs indoor space in a model-driven approach using watertight predefined models. Employing the two-step implementation of this process, the algorithm is able to model non-rectangular spaces with an even number of sides. Afterwards, an "improvement" process increases the level of details by modeling the intrusion and protrusion of the model. The 3D model is formed by extrusion from 2D to 3D. The proposed model-driven algorithm is evaluated with four benchmark real-world datasets. The efficacy of the proposed method is proved by the range of [77%, 95%], [85%, 97%] and [1.7 cm, 2.4 cm] values of completeness, correctness and geometric accuracy, respectively.

Point clouds are among the most important photogrammetry and remote sensing data that provide three-dimensional information about objects and features. These points have unorganized, irregular structures and do not have uniform spacing. In this paper, two innovative methods for converting the point cloud into a grid are presented. The first method is called the mesh method. It generates all possible 3D grid points covering the point cloud. Then, the distances between the generated grid points and the original points are measured. Based on these distances, the required grid points are determined. The second method is the resampling method. It uses three equations to resample the original point so that the points are moved to new coordinates to create the grid points. The methods are tested on three different point clouds. The results show that the two methods are very fast. However, the resampling method is much faster, especially for very large areas. [ABSTRACT FROM AUTHOR]

Depth estimation from images is an important task using scene understanding and reconstruction. Recently, encoder-decoder type fully convolutional architectures have gained great success in the area of depth estimation. Depth extraction from aerial and satellite images is one of the important topics in photogrammetry and remote sensing. This is usually done using image pairs, or more than two images. Solving this problem using a single image is still a challenging problem and has not been completely solved. Several convolutional neural networks have been proposed to extract depth from a single image, which act as encoders and decoders. In this article, we use one of these networks, which has performed well for depth estimation, in order to extract height from aerial and satellite images. Our main goal is to investigate the performance of Google Earth satellite data in order to produce a digital elevation model. At first, we extracted the digital model of the target area using ISPRS benchmark data, then we did the same thing using Google Earth satellite images. The paper presents a convolutional neural network for computing a high-resolution depth map given a single RGB Google Earth image. The results show the proper performance of Google Earth satellite images for height extraction. We achieved values of 2.07 m and 0.36 m for the RMS and REL metrics, respectively, which are very comparable and acceptable to the values of 2.04 m and 0.39 m obtained from the ISPRS benchmark images. [ABSTRACT FROM AUTHOR]

Vegetation monitoring and mapping are essential for a diverse range of environmental problems such as forest management, food resources, and climate change assessment. Several methods have been developed to classify different vegetation types based on remote sensing (RS) data. Land use classification has been revolutionized with the advent of neural networks. Various vegetation types were classified using multispectral Sentinel-2 satellite images due to their high spatial resolution and spectral information. Deep Convolutional Neural Network is considered a promising method for classifying remote sensing images with high spatial resolution due to its powerful feature extraction capabilities. However, large labeled datasets are required for better classification performance, so we have used pre-trained ResNet networks with 152 layers, 50 layers, and 101 layers trained on Big Earth Net (BEN). In order to obtain the best network performance and evaluate the sensitivity of the parameters in this study, we have performed two experiments: 1) the effect of different patch sizes and 2) increasing the number of images. The results demonstrate that ResNet 152 shows the highest accuracy with patches of 120 × 120 pixels, with an accuracy of 76.62%, and ResNet 50 is the best with an accuracy of 76.2% since the process of this network does not take much time. [ABSTRACT FROM AUTHOR]

In this paper, an improved Tornado method for filtering LiDAR (Light Detection and Ranging) point clouds is presented. The original method uses a vertical cone with a downward vertex and an upward base to remove the points within it as non-ground points. The remaining points are ground points. The cone moves on the ground surface over the entire region of the point cloud. In this work, the regions of the objects are predicted by extracting the vertical features that have points in the vertical plane or vertical column. Therefore, the tornado method is only used in regions that contain objects. In addition, our improved method uses a specific height for a tornado to reduce the Type I error in mountainous areas. Also, a cylinder surrounding the cone is used to reduce the distance calculations between the cone and the point cloud. The results show that this method is very effective and fast compared to the original method. It also has promising results for the Type I error. In addition, this method was tested on the International Society for Photogrammetry and Remote Sensing (ISPRS) datasets and produced outstanding results. The results show that this method achieves high filtering accuracy. Moreover, the proposed method achieves an overall average error of 6.83%, which is lower than most other methods. [ABSTRACT FROM AUTHOR]

In this paper, a new building detection method based on a density of LiDAR point clouds is proposed. In this method, trees, vegetation, and any objects that have points in a vertical plane or column are removed. In the density-based method, a cube is utilized to calculate the density therein. For each point, the cube is used to determine the number of neighbouring points. The density is calculated in two cases: 3D and 2D space. In 3D space, the volumetric density is calculated using the cube. In 2D space, all points are projected onto the horizontal plane, and the surface density is calculated using a square. Next, the two densities are compared and the points with different values in both cases are removed. The method leads to promising results in the removal of vegetation and trees. Moreover, the results achieve more than 94% completeness and correctness at the per-area level. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel 3D segmentation approach using digital elevation data. Building detection has been emerging as an important area of research. It has attracted many applications, such as geomatics, architectonics, computer vision, photogrammetry, remote sensing, industry, disaster management, and city planning. Building detection techniques can basically be divided into two categories: the classical approach and the deep learning approach. The main goal of this study is to compare some commonly used detection techniques in photogrammetry, like segmentation-based and classification-based methods using digital elevation data as input. The 4 different methods of roof detection with their detailed analysis and their final results are presented in this paper. This study encourages researchers to further advance research in building detection techniques. Results show that the 2D region growing can successfully segment the building components like the main facades of the complex roof and provide accurate qualitative and quantitative results compared to the other methodologies used in this study. [ABSTRACT FROM AUTHOR]

Aerial laser scanners find rapidly growing interest in photogrammetry and remote sensing as an efficient tool for reliable three-dimensional extraction and modelling of forest inventory information. In addition to interactive measurements in 3D point clouds, techniques for automatic extraction of objects and determination of geometric parameters form a high and important research issues (Maas, Bienert et al. 2008). This paper presents a novel approach on the extraction and modelling of individual trees from the Idaho National Forest in the USA and calculation of the statistical estimation for each extracted segment for future analysing. Lidar point cloud contains three-dimensional structure information which is used to estimate the statistical information for each tree segments. In this study, we worked on the raster surface made directly from the LiDAR point cloud and two main models, namely the digital terrain model (DTM) and the digital surface model (DSM), are generated when the point clouds are processed by the filtering method. Then we have used the segmentation techniques to extract the tree segments which is a triggering process that facilitates the extraction of statistical information such as crown diameter, eccentricity, and other additional attributes. The proposed individual tree segmentation method results in 73% correctness, 92% completeness and 81% F1-score. [ABSTRACT FROM AUTHOR]

Tinnitus and hearing loss are common problems that can be investigated via subjective and objective approaches. Previous studies have suggested a potential relationship between serum levels of Brain-Derived Neurotrophic Factor (BDNF) and tinnitus, reporting it as a potential objective biomarker for tinnitus. Therefore, the present study aimed to investigate the serum levels of BDNF in patients with tinnitus and/or hearing loss. Sixty patients were divided into 3 groups: Normal hearing with tinnitus (NH-T), hearing Loss with tinnitus (HL-T), and hearing loss without tinnitus (HL-NT). Moreover, 20 healthy participants were assigned to the control group or NH-NT. All participants were assessed using comprehensive audiological evaluations, serum BDNF level assessment, Tinnitus Handicap Inventory (THI), and Beck's Depression Inventory (BDI). There were significant intergroup differences in serum BDNF levels (p < 0.05), with the HL-T group showing the lowest BDNF levels. Moreover, the NH-T group had lower levels of BDNF compared to the HL-NT group. On the other hand, serum BDNF levels were significantly decreased in patients with an increased hearing threshold (p < 0.05). Also, serum BDNF levels had no significant relationship with tinnitus duration and loudness, as well as THI and BDI scores. The present study was the first to illustrate the importance of serum BDNF levels as a possible biomarker for predicting the severity of hearing loss and tinnitus in the affected patients. Also, it is possible that BDNF assessment can help find effective therapeutic methods for patients with hearing problems., Supplementary Information: The online version contains supplementary material available at 10.1007/s12070-023-03600-z., Competing Interests: Conflicts of InterestThe authors declare no conflict of interest in any relevant financial or non-financial forms., (© Association of Otolaryngologists of India 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Lineage B.1.1.7 (the British variant) is a new variant of SARS-CoV-2. The virus was first identified in the UK in October 2020. Since Iran is one of the most disaster risk countries in the world, disaster management is one of the most important issues. One of the effective approaches of this field is community-based disaster management (CBDM). Altogether, planning and policy-making through using various cultural-religious role models with emphasis on the cultural points can be useful to reduce the mortality and morbidity rate caused by the fourth wave of coronavirus in Iran., Competing Interests: Conflict of Interest None., (© Penerbit Universiti Sains Malaysia, 2022.)

Nowadays, Unmanned Aerial System (UAS)-based photogrammetry offers an affordable, fast and effective approach to real-time acquisition of high resolution geospatial information and automatic 3D modelling of objects for numerous applications such as topography mapping, 3D city modelling, orthophoto generation, and cultural heritages preservation. In this paper, the capability of four different state-of-the-art software packages as 3DSurvey, Agisoft Photoscan, Pix4Dmapper Pro and SURE is examined to generate high density point cloud as well as a Digital Surface Model (DSM) over a historical site. The main steps of this study are including: image acquisition, point cloud generation, and accuracy assessment. The overlapping images are first captured using a quadcopter and next are processed by different software to generate point clouds and DSMs. In order to evaluate the accuracy and quality of point clouds and DSMs, both visual and geometric assessments are carry out and the comparison results are reported. [ABSTRACT FROM AUTHOR]