1,721,154 research outputs found
IFCALIGNMENT FOR RASTER-TO-VECTOR GIS RAILWAY CENTRELINE: A CASE STUDY IN THE SOUTH OF ITALY
Full text linkBuilt environment Asset Management (AM) is evolving and renewing itself through the development of new technologies. Building Information Modelling (BIM) is the main methodology for the digitisation process of existing data and information. Although BIM was originally intended for buildings, in the last few years Infrastructure Building Information Modelling (I-BIM) and Civil Information Modelling (CIM) are emerging to manage civil infrastructure. The interaction of infrastructure with the surrounding environment is a fundamental aspect and it requires data-sharing between different sources and systems. Geographic Information Systems (GIS) is used to store and elaborate Earth's surface information, and it is, therefore, necessary to achieve a complete BIM/GIS interoperability. This paper aims to test the popular BIM open-standard Industry Foundation Classes (IFC) capabilities and potentialities in storing GIS data. A case study of a disused railway in the south of Italy was used to test the methodology presented: rail-centreline (alignment) extraction from GIS raster data, and a conversion of the alignment to an IFCAlignment element. The possibility to export a rail alignment in IFC was confirmed
RETROSPECTIVE STUDY OF VERTICAL GROUND DEFORMATION IN COMO, NORTHERN ITALY: INTEGRATION OF LEVELLING AND PSI MEASUREMENTS
Full text linkSubsidence-related vertical ground deformation due to natural and anthropogenic factors may lead to considerable damages to structures and infrastructures, and may increase the occurrence probability of consequential events, such as floods, especially on sea and lake shores. Como city, placed in the north of Italy, adjacent to Como Lake, is subjected to significant subsidence phenomenon, which has been monitored by geodetic levelling networks. In this work the historical geodetic levelling measurements and satellite-based Synthetic Aperture Radar (SAR) data archive are integrated to assess the accuracy of Atmospheric Delay and Deformation Rate estimations obtained through Persistent Scatter Interferometry (PSI) techniques. Tree levelling measurement datasets acquired in 1990, 1997 and 2004 are used in order to obtain the precise deformation rate at the benchmarks for two periods of 1990-1997 and 1997-2004. For multi-temporal InSAR analysis, 106 SAR images (1992-2004) and 41 SAR images (1992-2004) in Ascending Track orbit from ERS-1/2 missions are used in this study. The assessment is performed through a statistical comparison between two sets of vertical land deformation rates obtained by integrated methods. The results of the validation represented a good consistency between deformation rates derived by both techniques. Also, this study has revealed the potential of SAR images acquired in gyro-less mode by ERS-2 mission (2001-2004) in terms of the estimation of ground deformation
A 3D INDOOR-OUTDOOR BENCHMARK DATASET FOR LoD3 BUILDING POINT CLOUD SEMANTIC SEGMENTATION
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Label-efficient deep learning-based semantic segmentation of building point clouds at LOD3 level
Full text linkIn recent research, fully supervised Deep Learning (DL) techniques and large amounts of pointwise labels are employed to train a segmentation network to be applied to buildings' point clouds. However, fine-labelled buildings' point clouds are hard to find and manually annotating pointwise labels is time-consuming and expensive. Consequently, the application of fully supervised DL for semantic segmentation of buildings' point clouds at LoD3 level is severely limited. To address this issue, we propose a novel label-efficient DL network that obtains per-point semantic labels of LoD3 buildings' point clouds with limited supervision. In general, it consists of two steps. The first step (Autoencoder - AE) is composed of a Dynamic Graph Convolutional Neural Network-based encoder and a folding-based decoder, designed to extract discriminative global and local features from input point clouds by reconstructing them without any label. The second step is semantic segmentation. By supplying a small amount of task-specific supervision, a segmentation network is proposed for semantically segmenting the encoded features acquired from the pre-trained AE. Experimentally, we evaluate our approach based on the ArCH dataset. Compared to the fully supervised DL methods, we find that our model achieved state-of-the-art results on the unseen scenes, with only 10% of labelled training data from fully supervised methods as input
EUROPEAN GROUND MOTION SERVICE FOR BRIDGE MONITORING: TEMPORAL AND THERMAL DEFORMATION CROSS-CHECK USING COSMO-SKYMED INSAR
Full text linkMulti-station Ground-based Real-aperture Radar for Quasi-static Deformation Measurement
Full text linkGround-based Real-aperture Radar (GBRAR) has been applied in recent years for the dynamic analysis of civil constructions. The same technology could be also exploited for the high-precision quasi-static deformation measurement. Unfortunately, in this modality GBRAR still suffers from important drawbacks (accurate repositioning for long-term monitoring, target ambiguity, mitigation of atmospheric effects) which make its application less competitive w.r.t. other techniques. After reviewing a set of experiments to evaluate the instrumental performances of IBIS-S sensor by former IDS Sistemi Italian company, a solution based on the use of multiple stations (‘stereo-radar’) is discussed. This approach may help discriminate target ambiguity and improve the geometric definition of spatial displacements. ‘Stereo-radar’ is based on the use of at least two GBRAR sensors to work concurrently to monitor quasi-static observations. Here a preliminary test to demonstrate the feasibility of this technique is reported
TOWARDS DESCRIBING FULL-SECTION DEFORMATIONS USING TERRESTRIAL LASER SCANNING in the BADALING TUNNEL (China)
Full text linkThis paper focuses on the analysis of point clouds from terrestrial laser scanning to interpret possible deformations of the new Badaling Tunnel that was built for the Winter Olympics 2022 in the nearby of Beijing, China. A reference framework is established to compare data corresponding to various days with blocks of uniform columns and rows from an estimated tunnel axis. Filling holes and detecting outliers are performed for quasi-planar estimation, and refinement transformation is used to adjust the data errors between different days. Finally, the full-section deformations are detected in the form of distance discrepancies of representative points and are verified against total station measurements
Distance-Training for image-based 3d modelling of archeological sites in remote regions
Full text linkThe impressive success of Structure-from-Motion Photogrammetry (SfM) has spread out the application of image-based 3D reconstruction to a larger community. In the field of Archeological Heritage documentation, this has opened the possibility of training local people to accomplish photogrammetric data acquisition in those remote regions where the organization of 3D surveying missions from outside may be difficult, costly or even impossible. On one side, SfM along with low-cost cameras makes this solution viable. On the other, the achievement of high-quality photogrammetric outputs requires a correct image acquisition stage, being this the only stage that necessarily has to be accomplished locally. This paper starts from the analysis of the well-know "3×3 Rules" proposed in 1994 when photogrammetry with amateur camera was the state-of-The art approach and revises those guidelines to adapt to SfM. Three aspects of data acquisition are considered: geometry (control information, photogrammetric network), imaging (camera/lens selection and setup, illumination), and organization. These guidelines are compared to a real case study focused on Ziggurat Chogha Zanbil (Iran), where four blocks from ground stations and drone were collected with the purpose of 3D modelling
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