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The 3D modeling of urban buildings has become a common research area in various disciplines such as photogrammetry and computer vision, with different applications such as intelligent city management, navigation of self-driving cars and architecture, just to name a few. The objective of this study is to produce a 3D model of the external facade of the buildings with the required precision, accuracy and level of detail according to the user's requirements, while minimizing time and cost. This research focuses on the production of 3D models for blocks of residential buildings in Tehran, Iran. The Insta 360 One X2 spherical camera is selected to capture the data due to its low cost and 360 × 180° field of view. The camera specifications have facilitated more efficient data collection in terms of both time and cost. The proposed modeling method is based on extracting lines of external walls through the utilization of the point cloud density concept. Initially, photogrammetric point clouds are generated in with a reconstruction precision of 0.24 m from spherical camera images. In the next step, the 3D point cloud is projected into a 2D point cloud by setting the height component to zero. The 2D point cloud is then rotated based on the direction angle determined by the Hough transform so that the perpendicular walls are parallel to the axes of the coordinate system. Next, a 2D point cloud density analysis is performed by voxelizing the point cloud and counting the number of points in each voxel in both the horizontal and vertical directions. By determining the peaks in the density plot, the lines of the external vertical and horizontal walls are extracted. To extract the diagonal external walls, the density analysis is performed in the direction of the first principal component. Finally, by determining the height of each wall in the point cloud, a 3D model is created at the level of detail one. The resulting model has a precision of 0.32 m compared to real sizes, and the 2D plan has a precision of 0.31 m compared to the ground truth map. The use of the spherical camera and point cloud density analysis makes this method efficient and cost-effective, making it a promising approach for future urban modeling projects. [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. [ABSTRACT FROM AUTHOR]

Windows, as key components of building facades, have received increasing attention in facade parsing. Convolutional neural networks have shown promising results in window extraction. Most existing methods segment a facade into semantic categories and subsequently employ regularization based on the structure of manmade architectures. These methods merely concern the optimization of individual windows, without considering the spatial areas or relationships of windows. This paper presents a novel windows instance segmentation method based on Mask R-CNN architecture. The method features a spatial attention region proposal network and a relation module-enhanced head network. First, an attention module is introduced in the region proposal network to generate a spatial attention map, then the attention map is multiplied with the objectness scores of the classification branch. Second, for the head network, relation modules are added to model the spatial relationships between proposals. Appearance and geometric features are combined for instance recognition. Furthermore, we constructed a new window instance segmentation dataset with 1200 annotated images. With our dataset, the average precisions of our method on detection and segmentation increased from 53.1% and 53.7% to 56.4% and 56.7% compared with Mask R-CNN. A comparison with state-of-the-art methods also proves the predominance of our proposed method. [ABSTRACT FROM AUTHOR]

Road networks are very important features in geospatial databases. Even though high-resolution optical satellite images have already been acquired for more than a decade, tools for automated extraction of road networks from these images are still rare. One consequence of this is the need for manual interaction which, in turn, is time and cost intensive. In this paper, a multi-stage approach is proposed which integrates structural, spectral, textural, as well as contextual information of objects to extract road networks from very high resolution satellite images. Highlights of the approach are a novel linearity index employed for the discrimination of elongated road segments from other objects and customized tensor voting which is utilized to fill missing parts of the network. Experiments are carried out with different datasets. Comparison of the achieved results with the results of seven state-of-the-art methods demonstrated the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for correlating products derived by Synthetic Aperture Radar (SAR) measurements and laser profilometric road roughness surveys. The procedure stems from two previous studies, in which several Machine Learning Algorithms (MLAs) have been calibrated for predicting the average vertical displacement (in terms of mm/year) of road pavements as a result of exogenous phenomena occurrence, such as subsidence. Such algorithms are based on surveys performed with Persistent Scatterer Interferometric SAR (PS-InSAR) over an area of 964 km2 in the Tuscany Region, Central Italy. Starting from this basis, in this paper, we propose to integrate the information provided by these MLAs with 10 km of in situ profilometric measurements of the pavement surface roughness and relative calculation of the International Roughness Index (IRI). Accordingly, the aim is to appreciate whether and to what extent there is an association between displacements estimated by MLAs and IRI values. If a dependence exists, we may argue that road regularity is driven by exogenous phenomena and MLAs allow for the replacement of in situ surveys, saving considerable time and money. In this research framework, results reveal that there are several road sections that manifest a clear association among these two methods, while others denote that the relationship is weaker, and in situ activities cannot be bypassed to evaluate the real pavement conditions. We could wrap up that, in these stretches, the road regularity is driven by endogenous factors which MLAs did not integrate during their training. Once additional MLAs conditioned by endogenous factors have been developed (such as traffic flow, the structure of the pavement layers, and material characteristics), practitioners should be able to estimate the quality of pavement over extensive and complex road networks quickly, automatically, and with relatively low costs. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning Algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., Regression Tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted Regression Trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km2), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a Bayesian Optimization Algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms; in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose three case studies on critical stretches of two-lane rural roads for evaluating the reliability of the procedure. Road authorities could consider the proposed methodology for their monitoring, management, and planning activities. [ABSTRACT FROM AUTHOR]

The expansion of the railway industry has increased the demand for the three-dimensional modeling of railway tracks. Due to the increasing development of UAV technology and its application advantages, in this research, the detection and 3D modeling of rail tracks are investigated using dense point clouds obtained from UAV images. Accordingly, a projection-based approach based on the overall direction of the rail track is proposed in order to generate a 3D model of the railway. In order to extract the railway lines, the height jump of points is evaluated in the neighborhood to select the candidate points of rail tracks. Then, using the RANSAC algorithm, line fitting on these candidate points is performed, and the final points related to the rail are identified. In the next step, the pre-specified rail piece model is fitted to the rail points through a projection-based process, and the orientation parameters of the model are determined. These parameters are later improved by fitting the Fourier curve, and finally a continuous 3D model for all of the rail tracks is created. The geometric distance of the final model from rail points is calculated in order to evaluate the modeling accuracy. Moreover, the performance of the proposed method is compared with another approach. A median distance of about 3 cm between the produced model and corresponding point cloud proves the high quality of the proposed 3D modeling algorithm in this study. [ABSTRACT FROM AUTHOR]