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CoUDlabs_WP8_T811_UDC_001 Analysis and assessment of new techniques to build-up the topography/geometry of Urban Drainage infrastructure with high resolution - MaRDI portal

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CoUDlabs_WP8_T811_UDC_001 Analysis and assessment of new techniques to build-up the topography/geometry of Urban Drainage infrastructure with high resolution (Q6699707)

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Dataset published at Zenodo repository.
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English
CoUDlabs_WP8_T811_UDC_001 Analysis and assessment of new techniques to build-up the topography/geometry of Urban Drainage infrastructure with high resolution
Dataset published at Zenodo repository.

    Statements

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    The quality of the results obtained in hydraulic numerical models is strongly conditioned by the accuracy of the input data, especially in the case of shallow waters such as those occurring in urban floods. In addition, urban catchments have kerbs and other urban elements, such as ditches or grates, that condition the flow of the runoff generated during an event. This also occurs in the case of laboratory studies in large-scale facilities, with traditional elevation measurement techniques being too time-consuming to obtain not very high-resolution topographies. Therefore, this dataset includes data from an analysis and assessment of three different devices and techniques to obtain accurate elevation maps with a high resolution: i) conventional camera with the Structure from Motion (SfM) photogrammetric technique, ii) Intel® RealSense™ LiDAR Camera L515 and iii) Depth camera Intel d435i. The measurements from cameras ii) and iii) were combined with a 3D reconstruction software. Finally, a topographic manual survey was used as a base for coordinate referencing and elevation comparison. Surveys data, including the images used during the SfM survey, and resulted DEM were included in this dataset.This dataset is a result from the Joint Research Activity 3 (WP8, Improving Resilience and Sustainability in Urban Drainage solutions), Task 8.1.1. (Development of Scalable Hydrodynamic Performance Protocols.) within Co-UDlabs project, funded under the European Union's Horizon 2020 research and innovation programme under grant agreement No 101008626.
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    13 December 2023
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