Representing geographical uncertainties of utility location data in 3D

Scholtenhuis, Léon Luc olde; Duijn, Xander den; Zlatanova, Sisi

doi:10.1016/j.autcon.2018.09.012

Cited by 16 publications

(15 citation statements)

References 25 publications

(47 reference statements)

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“…• Evaluations of methods proposed by [63] for surface flatness assessment to generate a standardized surface flatness metric. • Development of a fuzzy logic-based uncertainty model for the estimation of the location of structures, similar to the method proposed by [64] for the prediction of the locations of utility data.…”

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confidence: 99%

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

2019

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This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-based reasoning between objects to semantically label point clouds. The framework then compares the extracted objects to the planned building information model (BIM) to automatically identify the as-built schedule and dimensional discrepancies. A novel method was also developed to remove redundant points of a newly acquired scan to detect changes between consecutive scans independent of the planned BIM. Five sets of point cloud data were acquired from the same construction site at different time intervals to assess the effectiveness of the proposed framework. In all datasets, the framework successfully extracted 132 out of 133 columns and achieved an accuracy of 98.79% for removing redundant surfaces. The framework successfully determined the progress of concrete work at each epoch in both activity and project levels through earned value analysis. It was also shown that the dimensions of 127 out of the 132 columns and all the slabs complied with those in the planned BIM.

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confidence: 99%

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

2019

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“…Our research also relates to other works on underground utility mapping [15][16][17][18]. In [15], the authors present the initial results of a framework for 3D mapping of underground utilities especially an overview of methods for primary data capture.…”

Section: Related Workmentioning

confidence: 91%

“…Table 1 gives a summary of all the results from the three datasets in our experiments in terms of precision, recall and F 1 score, as computed by ( 16) and (17). It can be observed from the table that precision, recall and F 1 score have all significantly improved after including more HDs and HDFs.…”

Section: Synthetic Datamentioning

confidence: 99%

“…In [15], the authors present the initial results of a framework for 3D mapping of underground utilities especially an overview of methods for primary data capture. A framework is proposed in [16] for utility data governance from the underground utility data survey to data usage, while [17] proposes an approach that integrates multiple available utility location datasets to represent geographical uncertainties. In [18], registration of utility networks in a cadastre is considered in a 3D + time (=4D) context.…”

Section: Related Workmentioning

confidence: 99%

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3D mapping from partial observations: An application to utility mapping

Dou

Lin

Magee

et al. 2020

Automation in Construction

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Precise mapping of buried utilities is critical to managing massive urban underground infrastructure and preventing utility incidents. Most current research only focuses on generating such maps based on complete information of underground utilities. However, in real-world practice, it is rare that a full picture of buried utilities can be obtained for such mapping. Therefore, this paper explores the problem of generating maps from partial observations of a scene where the actual world is not fully observed. In particular, we focus on the problem of generating 2D/3D maps of buried utilities using a probabilistic based approach. This has the advantage that the method is generic and can be applied to various sources of utility detections, e.g. manhole observations, sensors, and existing records. In this paper, we illustrate our novel methods based on detections from manhole observations and sensor measurements.This paper makes the following new contributions. It is the first time that partial observations have been used to generate utility maps using optimization based approaches. It is the first time that such a large variety of utilities' properties have been considered, such as location, directions, type and size. Another novel contribution is that different kinds of connections are included to reflect the complex layout and structure of buried utilities. Finally, for the first time to the best of our knowledge, we have integrated utility detection, probability calculation, model formulation and map generation into a single framework.The proposed framework represents all detections using a common language of probability distributions and then formulates the mapping problem as an Integer Linear Programming (ILP) problem and the final map is generated based on the solution with the highest probability sum. The effectiveness of this system is evaluated on synthetic and real data using appropriate evaluation metrics.

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“…However, it does not consider the accuracy of the data. Some works begin to extend the existing data model to consider many more details about utility networks, such as [36], represent geographical uncertainties of utility locations based on CityGML Utility Network ADE.…”

Section: The 3d Data Model For Underground Utility Networkmentioning

confidence: 99%

Towards an Underground Utilities 3D Data Model for Land Administration

et al. 2019

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With the pressure of the increasing density of urban areas, some public infrastructures are moving to the underground to free up space above, such as utility lines, rail lines and roads. In the big data era, the three-dimensional (3D) data can be beneficial to understand the complex urban area. Comparing to spatial data and information of the above ground, we lack the precise and detailed information about underground infrastructures, such as the spatial information of underground infrastructure, the ownership of underground objects and the interdependence of infrastructures in the above and below ground. How can we map reliable 3D underground utility networks and use them in the land administration? First, to explain the importance of this work and find a possible solution, this paper observes the current issues of the existing underground utility database in Singapore. A framework for utility data governance is proposed to manage the work process from the underground utility data capture to data usage. This is the backbone to support the coordination of different roles in the utility data governance and usage. Then, an initial design of the 3D underground utility data model is introduced to describe the 3D geometric and spatial information about underground utility data and connect it to the cadastral parcel for land administration. In the case study, the newly collected data from mobile Ground Penetrating Radar is integrated with the existing utility data for 3D modelling. It is expected to explore the integration of new collected 3D data, the existing 2D data and cadastral information for land administration of underground utilities.

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Representing geographical uncertainties of utility location data in 3D

Cited by 16 publications

References 25 publications

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

3D mapping from partial observations: An application to utility mapping

Towards an Underground Utilities 3D Data Model for Land Administration

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