The Multi-View Stereo (MVS) technology has improved significantly in the last decade, providing a much denser and more accurate point cloud than before. The point cloud now becomes a valuable data for modelling the LOD2 buildings. However, it is still not accurate enough to replace the lidar point cloud. Its relative high level of noise prevents the accurate interpretation of roof faces, e.g. one planar roof face has uneven surface of points therefore is segmented into many parts. The derived roof topology graphs are quite erroneous and cannot be used to model the buildings using the current methods based on roof topology graphs. We propose a parameter-free algorithm to robustly and precisely derive roof structures and building models. The points connecting roof segments are searched and grouped as structure points and structure boundaries, accordingly presenting the roof corners and boundaries. Their geometries are computed by the plane equations of their attached roof segments. If data available, the algorithm guarantees complete building structures in noisypointclouds and meanwhile achieves global optimized models. Experiments show that, when comparing to the roof topology graph based methods, the novel algorithm achieves consistent quality for both lidar and photogrammetricpointclouds. But the new method is fully automatic and is a good alternative for the model-driven method when the processing time is important.
Although few approaches, e.g., (Guillaso et al., 2015, Guillaso et al., 2013, D’Hondt et al., 2012), aiming towards information ex- traction exist, 3-D object modeling/reconstruction from TomoSAR data is still a new field and has not been explored much. Prelimi- nary investigations towards object modeling/reconstruction using spaceborne TomoSAR pointclouds have been demonstrated in (Zhu and Shahzad, 2014, Shahzad and Zhu, 2015, Shahzad and Zhu, 2016) while TomoSAR pointclouds generated over urban and vegetation areas using airborne SAR datasets have been ex- plored in (D’Hondt et al., 2012, Schmitt et al., 2015) respectively. Taking into consideration special characteristics associated to these pointclouds e.g., low positioning accuracy (in the order of 1m), high number of outliers, gaps in the data and rich fac¸ade informa- tion (due to the side looking geometry), this paper demonstrates for the first time the potential of explicitly modelling the indi- vidual roof surfaces to reconstruct 3-D prismatic building models from TomoSAR pointclouds.
2.2 Building Extraction from Airborne Lidar PointClouds In classical methods, building extraction from airborne LiDAR data has been resolved by classifying the LiDAR points to different object types such as terrain, buildings, vegetation or others. Rottensteiner and Briese (2002) proposed a method to remove terrain point using a skew error distribution function and then separate building points from other points by analysing height differences. Sampath and Shan (2007) clustered the building cloud into planar segments, followed by their topologic reconstruction. Dorninger and Pfeifer (2008) proposed a comprehensive method for extracting rooftops from LiDAR pointclouds. A target based graph matching approach was used to handle both complete and incomplete laser data (Elberink and Vosselman, 2009). Rau (2012) proposed a TIN-Merging and Reshaping roof model reconstruction algorithm. Sohn et al. (2012) generalized noisy polylines comprising a rooftop model by maximizing a shape regularity.
Building roofs extraction is the most important feature in our segmentation procedure. But, in some cases, there is an overlap between the building roofs and trees. Thus, it enforces us to classify trees and building roofs to achieve better result of building roofs extraction. In this work, vegetation is extracted by means of spectral information from RGB image. Generally speaking, in RGB image, vegetation is illustrated by the green color. However, this green color is not the same in all pixels of the RGB image. Therefore, we consider the threshold for the green channel of the RGB image and considering those pixels as vegetation which the differences of two other channels from green channel are above the pre-defined threshold. Filtering the extracted vegetation eliminate noisy pixels from the extracted vegetation and fill the gaps between them. In this work, we applied median filter with mask size 14 to remove the noises from the image and achieve better result of building and street extraction. Finally, morphologic dilation can be utilized to fill the gaps between extracted vegetation.
Building Information Modeling (BIM) is a topic of major relevance as the current Brazilian legislation, through BIM BR Strategy - a governmental plan establishing that such technology should be used in the execution of engineering works and services, forcing contractors to adapt to the new reality. In the Brazilian market, the difficulty in attesting the efficient use of BIM is linked to the lack of a certificate to guarantee the services provided by Architecture, Engineering, Construction and Operation (AECO) professionals. The purpose of this study is to create an evaluation model for certification (CUB-e), so that companies can attest the level of BIM in which they operate. For doing so, surveys and researches were carried out through interviews in technical visits to institutions that already operate in BIM, in order to obtain information on the current level of application of the technology in the job market.
Ship tracks represent the best natural laboratory for observ- ing the localized impacts of point source pollution aerosols. Figure 1 shows the microphysical RGB composite imagery of ship tracks in the northeastern Pacific Ocean. Panels a and b show the VIIRS and MODIS images for the same area within overpasses that are 20 min apart and differ in satel- lite zenith angle by 3 ◦ . The MODIS image covers the same area as the NPP, and the higher resolution of VIIRS is evident (Hillger et al., 2013). Here attention is focused on the added information content for understanding cloud processes. This can be done best on ship tracks that occur in such marine stratocumulus, because the cloud-top temperatures were very near 12 ◦ C for the clouds both in and out of the ship tracks. The r e values of the track retrieved by VIIRS and MODIS are
In   Donoho proposes different thresholding technique, but this technique not keep details like edges, to overcome this we proposes new technique. In this paper, we have proposed the threshold and convolution technique. The input image is applied to different noises to get the noisy image. Different types of noise such as white Gaussian, Salt and Pepper, Speckle and Poisson’s added to the image .This image is transformed into the wavelet domain .The Wavelet features are modified by the proposed technique and process which would be reversed by applying Inverse Wavelet Transform to remove the noise from the image. Figure 1, elaborates the process of denoising Image denoising algorithm consists of a few steps, let us consider an input signal x(t) and noisy signal n(t), add both the signals to get y(t) , i.e.
surface removed and with the same number of sampling points, n, as the previous experiment. This experiment achieved a success rate of 71.88% and an average run time of 3.09 seconds. These results can be seen in table II. The total mean run time cost for this experiment is 3.3 seconds, which represents the 3.09 of the grasp detector run time plus the 0.21 seconds spent removing the large planar surfaces from the pointclouds. In this experiment, the tested method was capable of segmenting a point cloud, generate grasp candidates and classifying them with success in 115 of the 160 trials in an average run time of 3.3 seconds.
Embora o conceito de Modelagem de Informações da Construção seja relativamente novo, o processo de trabalho envolvendo essa metodologia já tem mais de 30 anos. O conceito mais antigo já documentado foi um protótipo de trabalho chamado "Building Description System", publicado por Chuck Eastman, na Universidade de Carnegie- Mellon, em 1975 (EASTMAN, 2014). De acordo com Azhar (2011), a importância da Modelagem de Informações da Construção (conceito BIM) consiste no fato de diminuir o custo do projeto, aumentar a produtividade e qualidade, reduzir o tempo de entrega, reduzir retrabalhos e evitar desperdícios. Tem início na fase de simulação virtual de construção e após finalizada esta etapa, as informações geradas servirão de apoio para as atividades de design, compras, cronograma de obra, fabricação e da própria construção. É possível ainda usar tais informações para demonstrar o ciclo de uso do edifício, extrair detalhes de contratos e interligar especificações de construção. Vale ressaltar que o BIM não é apenas um programa computacional, mas sim uma plataforma que integra os mais variados membros do projeto (arquitetos, engenheiros, proprietários, empreiteiros, fornecedores, etc.), fazendo mudanças nos processos e fluxos de trabalho.
These are critical barriers and challenges, which we encounter as well in our research on the usage of BIM software for existing buildings. None of these challenges is particularly “easy to deal with.” For example, it is indeed a time-consuming process to manually model an existing building into a BIM environment. Although some strategies have been presented to fasten this process and make it more accurate, they typically also require a lot of manual efforts. An example strategy is to use laser scanning or photogrammetric tools (for building surveys) associated with BIM tools, perhaps including some semi-automatic or automatic shape recognition functionality. This strategy still requires considerable manual efforts, and, there is an additional hardware and software cost. It appears that, if one wants to obtain qualitative BIMs, one cannot simply expect it to come there effortlessly. Also, the continuous updating of information during the different phases of the building life-cycle is something that requires manual effort, no matter from which perspective you look at it. Finally, handling uncertain data can be done to some extent, but it requires specialized workers that interpret building data and properly
This paper presents a system that uses volumetric sensory data to detect small obstacles in level crossings. For this purpose it relies on the use of dense 3D pointclouds produced by a tilting 2D laser scanner. This way the system is not as dependent on lighting conditions and object’s appearance as it would if relying on binocular vision, and it is also not as dependent on ground planarity as are solutions based on static 2D laser scanners. The system is composed of a software layer that processes the 3D point cloud preceded by an acquisition hardware layer. Obstacle detection is carried out exploiting the assumption that a quasiplanar dominant ground plane exists. A background model of the level crossing is learned from a set of training pointclouds. Obstacles are the objects that show up in the pointclouds captured online that are inexistent in the training dataset. To avoid cumber- some and time-consuming manual on-site calibration, the proposed system includes an automatic calibration process that estimates the pose of both level crossing and railway with respect to the laser scanner.
its classification capabilities using data from multispectral laser scanning for land cover mapping. As a result of the study, the high relative accuracy of Titan system in three spectral bands was proven. The mean differences between digital terrain models (DTMs) were less than 0.03 m. The data density analysis showed the influence of the laser wavelength. The pointclouds had average densities of 25, 23 and 20 points per square metre respectively for green, near-infrared and shortwave-infrared lasers. Wichmann V. et al. (2015) presented an explorative analysis of the first multispectral airborne Lidar Titian ALTM collected data with focus on class specific spectral signatures. Spectral patterns were used for a classification approach, which was evaluated in comparison to a manual reference classification. They showed that this first flight data set was suitable for conventional geometrical classification and mapping procedures. Ahmed Shaker (2015) used multispectral Lidar data collected by Optech Titan system to assessment analysis for land cover classification results in two study areas in the city of Oshawa, ON, Canada. The first study area on the north of Oshawa was about 550m x 380m containing various types of land cover classes. Maximum likelihood classifier was used to classify the intensity data of each channel separately, combined three intensity bands, and a DSM band with three channels. Overall classification accuracy on Channel 1, 2, 3 separately was equal to 38%, 46% and 52.5%. Combined classification results of Channels 1, 2 and 3 intensity data, overall classification accuracy was 69%. A digital surface model (DSM) is added as an additional band to the combined three intensity bands, and overall classification accuracy reached 78%. The second study area on the south of Oshawa is flat urban area. By using object oriented classification method and normalized DSM with combined three intensity bands, the best overall accuracy reaches 94% for classification results.
The geological modeling of stratiform deposits can become very complex, often making use of geological envelopes of small thickness and requiring the use of sub- blocks (based on Cartesian coordinates) to produce a coherent block model. However, geological events after the formation of the deposit (folds, faults, etc.) can change the direction of spatial continuity of certain attributes, with the mixing of samples belong- ing to different formation eras (in the case of stratiform deposits) in the same elevation. This study presents a solution for deposits with stratigraphic grades combined with samples of different origins. The solution is a two-dimensional estimate obtained by accumulating the thicknesses of P 2 O 5 in a phosphate deposit (as compared to tradi- tional statistical analysis in three dimensions).
The currently existing mobile mapping systems equipped with active 3D sensors allow to acquire the environment with high sampling rates at high vehicle velocities. While providing an effective solution for environment sensing over large scale distances, such acquisi- tion provides only a discrete representation of the geometry. Thus, a continuous map of the underlying surface must be built. Mobile acquisition introduces several constraints for the state-of-the-art surface reconstruction algorithms. Smoothing becomes a difficult task for recovering sharp depth features while avoiding mesh shrinkage. In addition, interpolation-based techniques are not suitable for noisy datasets acquired by Mobile Laser Scanning (MLS) systems. Furthermore, scalability is a major concern for enabling real-time rendering over large scale distances while preserving geometric details. This paper presents a fully automatic ground surface recon- struction framework capable to deal with the aforementioned constraints. The proposed method exploits the quasi-flat geometry of the ground throughout a morphological segmentation algorithm. Then, a planar Delaunay triangulation is applied in order to reconstruct the ground surface. A smoothing procedure eliminates high frequency peaks, while preserving geometric details in order to provide a regular ground surface. Finally, a decimation step is applied in order to cope with scalability constraints over large scale distances. Experimental results on real data acquired in large urban environments are presented and a performance evaluation with respect to ground truth measurements demonstrate the effectiveness of our method.
In addition, the models might be georeferenced by the location of some available GCPs which can be displayed clearly and unequivocally on the photos. An ideal control point would be a unique element, static and not easily alterable. Moreover, the distribution of them should be well spread out, covering all the study area and located in different elevation planes being its proper establishment one of the most important steps. Nevertheless, the beaches are too homogeneous media, not characterized by having enough unique and unalterable features such as rocks, walls or other elements to use as GCPs and sometimes this fact may hinder the work. In these cases, conspicuous external elements should be provided to bring heterogeneity to the sandy surface. This problem is obvious in El Saler beach where some surveying rods are used as GCP.
Research in tourism has used the push–pull paradigm for three main purposes. The first one is to explore personal motivations that direct people towards specific behaviors. In this context, some studies attempt to clarify the motivational differences in relation to demographics (Kim et al., 2003). The second one is market segmentation (Frochot & Morrison, 2001) in which the most implemented criteria are the following: segment- ing tourists from a specific source market, tourists to a specific destination, tourists traveling for a specific product within a destination, or any combination of the three ways mentioned. Finally, researchers have in- vestigated the relationships between motivations and satisfactions (Huang et al., 2014; Yoon & Uysal, 2005). In particular, Yoon and Uysal (2005) found that tourist satisfaction in turn connected to loyalty, is directly related to authentic experiences.
In the last few years, multi-cameras and LIDAR systems draw the attention of the mapping community. They have been deployed on different mobile mapping platforms. The different uses of these platforms, especially the UAVs, offered new applications and developments which require fast and accurate results. The successful calibration of such systems is a key factor to achieve accurate results and for the successful processing of the system measurements especially with the different types of measurements provided by the LIDAR and the cameras. The system calibration aims to estimate the geometric relationships between the different system components. A number of applications require the systems be ready for operation in a short time especially for disasters monitoring applications. Also, many of the present system calibration techniques are constrained with the need of special arrangements in labs for the calibration procedures. In this paper, a new technique for calibration of integrated LIDAR and multi-cameras systems is presented. The new proposed technique offers a calibration solution that overcomes the need for special labs for standard calibration procedures. In the proposed technique, 3D reconstruction of automatically detected and matched image points is used to generate a sparse images- driven point cloud then, a registration between the LIDAR generated 3D point cloud and the images-driven 3D point takes place to estimate the geometric relationships between the cameras and the LIDAR.. In the presented technique a simple 3D artificial target is used to simplify the lab requirements for the calibration procedure. The used target is composed of three intersected plates. The choice of such target geometry was to ensure enough conditions for the convergence of registration between the constructed 3D pointcloudsfrom the two systems. The achieved results of the proposed approach prove its ability to provide an adequate and fully automated calibration without sophisticated calibration arrangement requirements. The proposed technique introduces high potential for system calibration for many applications especially those with critical logistic and time constraints such as in disaster monitoring applications.
After large tragedies the demands of the fire fighting and prevention market became strict. In view of this condition, the present work seeks to study the advantages and adopt software from the BIM platform in relation to CAD in publishing projects; where, with the use of the more complex and costly used market, or with the use of companies specialized in this area in countries such as the United Kingdom and the United States, using optimization in the publishing life cycle. Thus, when analyzing the use of a CAD platform, despite being a pioneering model, it has become obsolete for the current market landscape, as it depends on the level of knowledge and professional detail, a BIM platform presents better results by incorporation as properties of the elements used in the projects without total human dependence. Thus, for a better management of fire and fire protection projects, whether in the project or during the lifetime of the BIM platform, it presents better results.
Vehicle-mounted: The segmentation of dense vehicle-mounted laser scanner points is a challenging task due to the existence of varied kinds of road furnitures which contain signs and light poles, road barricades, billboards, the ground and vehicles. In this work, two vehicle-mounted datasets were tested as shown in Figure 6(a) captured from an urban street of 355 meters long with 217k points and in Figure 6(b) captured from a small partial of a city in details containing 120k points. From Figure 6(a) contain- ing road, buildings, street lamps, vehicles and trees, we observe that the road surface was clustered into a complete one and sepa- rated entirely from other objects. Also, the building facades were discovered quite well. From Figure 6(b), we observe that most of the building facades were segmented well despite that their densities vary in a wide range. Those facades perpendicular to the road (the green slice within the big red ellipse frame) and the small slices connected with other big ones (the two slices with- in the small red ellipse frames) were recovered quite well. Fig- ure 6(c) shows a detailed look of the segmentation result with the red quadrangles representing different slices, which means that the segmentation result of the proposed method can be applied on the extraction of street patches with more specific operations. Aerial: The first aerial data set tested is composed of 3433k points, which covers an urban area of 5km×5km. There are buildings, ground, road and vegetations in this data set. Fig- ure 7(a) shows a partial result of the whole data set, from which we can see that the ground was separated by the road (left bottom, in orange) into two parts, in purple and lemon yellow, respective- ly, and the ground in purple was segmented into a whole surface despite of the various objects on the ground. The roofs of build- ings were segmented into a whole part in general, while some small structures can also be kept. Figure 7 (b) shows a detailed view of the segmentation result of the area in red frame in Fig- ure 7(a), from which we can see that the proposed method can preserve the details well. The second tested aerial data set is the ISPRS commission III/4 benchmark on Urban Classification and 3D Building Reconstruction and Semantic Labeling 2 , the result- s of which are shown in Figure 7(c) and (d). We can see that the objects including the trees and building are generally sepa- rated from the ground which is segmented into a single part, and the details of the roofs are preserved well as Figure 7(d) shows.