Comparison of Software for Airborne Laser Scanning Data Processing in Smart City Applications

Badenko, Vladimir; Zotov, Dmitry; Muromtseva, Nataliya; Volkova, Yulia; Chernov, P.

doi:10.5194/isprs-archives-xlii-5-w2-9-2019

Cited by 10 publications

(8 citation statements)

References 24 publications

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“…In the FoF, the Digital Shadow technology refers to the production process and the product, while Building Information Modelling provides digitalization of the production infrastructure [5,6]. The FoF supports the entire production lifecycle from the development and planning stage and ends with the creation of a Digital Shadow [4], a BIM model and the concept of a smart and virtual factory [7]. Thereby the virtual factory stands for the digital representation of a real production site and a smart factory describes a self-organizing production environment.…”

Section: Theoretical Basics 21 Factory Of the Futurementioning

confidence: 99%

“…Particularly in today's global markets, constant changes in requirements are inevitable, therefore, modern factory planning must react with sustainable and long-term concepts. This in turn, increases the requirements in the planning process [4]. The development of technologies in the area of Industry 4.0 has led to the emergence of the concept of a digital FoF as a system of integrated technological solutions that provide the shortest time to design and manufacturers globally competitive next-generation products.…”

Section: Factory Of the Futurementioning

confidence: 99%

“…BIM models contain detailed information about three-dimensional geometry of the FoF facilities, the appropriate rich semantics of structure elements and all engineering networks in buildings, as well as detailed information on the function of the infrastructure supporting the production process [8]. The Digital Shadow represents the entire production of the FoF and its processes, including its products and peripherals [4,7]. The scienti c community predicts cross-sectorally that the integration of BIM and DS technologies are inevitable for the future-oriented development of the FoF.…”

Section: Potentials Of the Integration Of Bim And Dsmentioning

confidence: 99%

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Integration concept of Digital Shadows and BIM for a long-term optimal decision support in factory planning

Badenko

Bolshakov

Fedotov

et al. 2021

Preprint

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Industrial objects nowadays rapidly transform due to the development of digital technologies. The concept of the Factory of the Future (FoF) involves digitization of all parts of the factory. In this paper two technologies are motivated and considered as the basic technologies that should be used in FoF: Digital Shadow (DS) and Building Information Modelling (BIM). Basic theory on these issues is given and potentials of BIM and DS integration is formulated. Based on the ability of digital technologies, their integration and convergence to generate value, definition Digital Asset is introduced from the economic point of view as a digital resource which brings economic benefit. A concept of integrating BIM and DS technologies for decision support in factory planning is formulated, including Life Cycle Assessment (LCA) and semantic modeling. The concept includes a description of the aggregate of technologies and their interconnections as a Digital Asset of the FoF. Further research objectives are focused on integration of BIM and DS which requires their interoperability ensured by an Ontology-Based Data Access (OBDA) approach, based on the Semantic web.

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Section: Theoretical Basics 21 Factory Of the Futurementioning

confidence: 99%

Section: Factory Of the Futurementioning

confidence: 99%

Section: Potentials Of the Integration Of Bim And Dsmentioning

confidence: 99%

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Integration concept of Digital Shadows and BIM for a long-term optimal decision support in factory planning

Badenko

Bolshakov

Fedotov

et al. 2021

Preprint

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“…A). The classification of data from airborne laser scanning is due to mapping techniques and resulting density of point cloud well elaborated by many authors (Li et al 2019, Pirotti et al 2019, Ullrich and Pfennigbauer 2019 and implemented in multiple software (Badenko et al 2019). In our case study, we classify ALS data with LAStools software using the lasground and lasclassify tools.…”

Section: -D Surveying and Raw Data Processingmentioning

confidence: 99%

Semi-automatic LiDAR point cloud denoising using a connected-component labelling method

Kaňuk¹,

Šupinský²,

Šašak³

et al. 2019

Geographia Cassoviensis

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The Smart City concept requires new, fast methods for collection of 3-D data representing features of urban landscape. Laser scanning technology (LiDAR - Light Detection and Ranging) enables such approach producing dense 3-D point clouds of millions of points, which, however, contain noise. Therefore, we developed a new approach allowing for a semi-automatic elimination of data noise resulting from motion of objects within the scanned scene such as persons. We used a connected-component labelling method to filter out the noise points from terrestrial laser scanning point clouds. Our approach was based on a step-by-step object classification with a proper parameterisation. In the first step, all points located close to the predicted terrain were selected. In the second step, the points representing the terrain and floor were classified using the surface filter tool implemented in the RiScan Pro software by RIEGL. The rest of points were classified using point cloud clustering via the connected-component labelling method implemented in the CloudCompare software. In the final step, the operator manually decides whether the point cluster represents the noise. The method was applied to the Cathedral of Saint Elizabeth, a sacral object located in the historical centre of the city of Košice in Slovakia during normal operating hours. We managed to capture approximately 80% of the data noise in total. The method provides a better flexibility in surveying overcrowded city locations using the laser scanning technology.

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“…These classifications use point cloud geometry as a unique input and, in most cases, are based on a height normalization followed by multiple elevation thresholding and local texture analyses. References in the literature to these methods are numerous [29][30][31][32][33][34], and they often describe the use of existing software (e.g., Terrascan, LasTools, FME, Envi) or combinations of them. One of their main shortcomings is that they fail at differentiating covers with similar local textures and elevations.…”

Section: Introductionmentioning

confidence: 99%

Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds

Roces‐Díaz

Cabo

Prendes³

et al. 2020

Forests

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Accurate mapping of landscape features is key for natural resources management and planning. For this purpose, the use of high-resolution remote sensing data has become widespread and is increasingly freely available. However, mapping some target features, such as small forest patches, is still a challenge. Standard, easily replicable, and automatic methodologies to delineate such features are still missing. A common alternative to automated methods is manual delineation, but this is often too time and resource intensive. We developed a simple and automatic method from freely available aerial light detection and ranging (LiDAR) and aerial ortho-images that provide accurate land use mapping and overcome some of the aforementioned limitations. The input for the algorithm is a coloured point cloud, where multispectral information from the ortho-images is associated to each LiDAR point. From this, four-class segmentation and mapping were performed based on vegetation indices and the ground-elevation of the points. We tested the method in four areas in the north-western Iberian Peninsula and compared the results with existent cartography. The completeness and correctness of our algorithm ranging between 78% and 99% in most cases, and it allows for the delineation of very small patches that were previously underrepresented in the reference cartography.

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Comparison of Software for Airborne Laser Scanning Data Processing in Smart City Applications

Cited by 10 publications

References 24 publications

Integration concept of Digital Shadows and BIM for a long-term optimal decision support in factory planning

Integration concept of Digital Shadows and BIM for a long-term optimal decision support in factory planning

Semi-automatic LiDAR point cloud denoising using a connected-component labelling method

Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds

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