PUTTING ROMAN DAMS IN CONTEXT: A VIRTUAL APPROACH

Water resources and management have become a critical global issue. During the half-millennium of its existence, the Roman Empire developed numerous strategies to cope with water management, from large-scale urban aqueduct systems, to industrial-scale water mills designed to cope with feeding growing city populations. Roman engineers encountered, adopted, and adapted indigenous hydraulic systems, and left lasting imprints on the landscape of the Mediterranean and temperate Western Europe by employing a range of water technologies. A recent academic study has enabled the identification of remains of and references to seventy-two dams from the Roman era, constructed in Spain between the 1st and 4th century AD. Such unique heritage, without comparisons in the Mediterranean makes Spain an emblematic case study for the analysis of Roman hydraulic engineering and water management policies. Fifty dams have been located and detailed. The twenty-two outstanding, although identified on the ground, have not been able to be acceptably characterized, due in some cases to their being ruins in a highly degraded state, others due to their being masked by repairs and reconstructions subsequent to the Roman era. A good example of such neglected dams is the buttress dam of Consuegra , in Toledo province (Castilla-La Mancha). Dating to the 3rd - 4th century AD, the Dam of Consuegra, on the basin of the Guadiana, with its over 600 metres length and 4,80 metres height, is a remarkable case of Roman engineering mastery. It had a retaining wall upstream, numerous buttresses and perhaps an embankment downstream, of which no remains are left. The application of 3D digital imaging technique to create a high quality virtual model of such monuments has proved to be successful especially for the study of the technological aspects related its construction. The case study of the Roman dam of Muel (Zaragoza) has shown, in fact, as best practices in digital archaeology can provide an original and innovative perspective on a long time studied monument. In this paper it will be explored how deploying recent computer technologies to the Roman dam at Consuegra can advance our understanding of the history of local and regional landscape change and the technology of water management. In summer 2016, the dam has been documented with terrestrial laser scanning with two FARO Focus 3D x330 and aerial photogrammetry image capturing with a DJI Phantom 4 drone. Data was processed in various 3D software applications to generate 3D representations of the dam including 3D point clouds, animations, and meshed models.

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Can 3D Point Clouds Replace GCPs?

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-5-347-2014 ◽

2014 ◽

Vol II-5 ◽

pp. 347-354 ◽

Cited By ~ 2

Author(s):

G. Stavropoulou ◽

G. Tzovla ◽

A. Georgopoulos

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Large Scale ◽

Point Clouds ◽

Field Work ◽

Surface Model ◽

Control Points ◽

3D Point Clouds ◽

Surface Models

Over the past decade, large-scale photogrammetric products have been extensively used for the geometric documentation of cultural heritage monuments, as they combine metric information with the qualities of an image document. Additionally, the rising technology of terrestrial laser scanning has enabled the easier and faster production of accurate digital surface models (DSM), which have in turn contributed to the documentation of heavily textured monuments. However, due to the required accuracy of control points, the photogrammetric methods are always applied in combination with surveying measurements and hence are dependent on them. Along this line of thought, this paper explores the possibility of limiting the surveying measurements and the field work necessary for the production of large-scale photogrammetric products and proposes an alternative method on the basis of which the necessary control points instead of being measured with surveying procedures are chosen from a dense and accurate point cloud. Using this point cloud also as a surface model, the only field work necessary is the scanning of the object and image acquisition, which need not be subject to strict planning. To evaluate the proposed method an algorithm and the complementary interface were produced that allow the parallel manipulation of 3D point clouds and images and through which single image procedures take place. The paper concludes by presenting the results of a case study in the ancient temple of Hephaestus in Athens and by providing a set of guidelines for implementing effectively the method.

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LANDSLIDE MONITORING AND ASSESSMENT FOR HIGHWAY RETAININGWALL: THE CASE STUDY OF TAŞKENT(TURKEY) LANDSLIDE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w4-603-2018 ◽

2018 ◽

Vol XLII-3/W4 ◽

pp. 603-608

Author(s):

M. Zeybek ◽

İ. Şanloğlu

Keyword(s):

Retaining Wall ◽

Point Clouds ◽

Retaining Walls ◽

Landslide Monitoring ◽

Assessment And Evaluation ◽

Cloud Data ◽

3D Point Clouds ◽

Fast Processing ◽

Cloud Data Processing

<p><strong>Abstract.</strong> Landslide monitoring and assessment of the highways retaining walls are a crucial task. Because there exist a risk and danger with regard to the movement of the wall to the highway by landslide force that may spread further. To evaluate the changing, movements have to be monitored. For this reason, we practised mobile LiDAR surveys on the landslide effected wall on the highway. The usage of the mobile LiDAR systems have significantly increased in recent years, especially for road management. Currently, mobile LiDAR technology is capable of measuring the earth surface with high precision and density as a 3D point clouds. As stated in this study, the point cloud data processing have been analysed further and the wall surface points fitted to a plane object for monitoring of the landslide effects. This study focuses on different plane fitting algorithms which represents the retaining wall, a performance assessment and evaluation of the deformation between two plane models. The analysis indicates that the uncertainty of the measurements between the two epochs on stable areas survey was within &plusmn;2&thinsp;cm. According to the experimental results, the proposed methods performed promising results that can be used for monitoring of retaining walls for fast processing and assessment.</p>

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BIM Supported Surveying and Imaging Combination for Heritage Conservation

Remote Sensing ◽

10.3390/rs13081584 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1584

Author(s):

Pedro Martín-Lerones ◽

David Olmedo ◽

Ana López-Vidal ◽

Jaime Gómez-García-Bermejo ◽

Eduardo Zalama

Keyword(s):

Laser Scanning ◽

Point Clouds ◽

Cloud Data ◽

Industry Foundation Classes ◽

3D Point Clouds ◽

Conservation Actions ◽

Heritage Building ◽

2D Imaging ◽

Digital Survey ◽

Level 2

As the basis for analysis and management of heritage assets, 3D laser scanning and photogrammetric 3D reconstruction have been probed as adequate techniques for point cloud data acquisition. The European Directive 2014/24/EU imposes BIM Level 2 for government centrally procured projects as a collaborative process of producing federated discipline-specific models. Although BIM software resources are intensified and increasingly growing, distinct specifications for heritage (H-BIM) are essential to driving particular processes and tools to efficiency shifting from point clouds to meaningful information ready to be exchanged using non-proprietary formats, such as Industry Foundation Classes (IFC). This paper details a procedure for processing enriched 3D point clouds into the REVIT software package due to its worldwide popularity and how closely it integrates with the BIM concept. The procedure will be additionally supported by a tailored plug-in to make high-quality 3D digital survey datasets usable together with 2D imaging, enhancing the capability to depict contextualized important graphical data to properly planning conservation actions. As a practical example, a 2D/3D enhanced combination is worked to accurately include into a BIM project, the length, orientation, and width of a big crack on the walls of the Castle of Torrelobatón (Spain) as a representative heritage building.

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MATLAB Virtual Toolbox for Retrospective Rockfall Source Detection and Volume Estimation Using 3D Point Clouds: A Case Study of a Subalpine Molasse Cliff

Geosciences ◽

10.3390/geosciences11020075 ◽

2021 ◽

Vol 11 (2) ◽

pp. 75

Author(s):

Dario Carrea ◽

Antonio Abellan ◽

Marc-Henri Derron ◽

Neal Gauvin ◽

Michel Jaboyedoff

Keyword(s):

Point Cloud ◽

Natural Hazard ◽

Point Clouds ◽

Volume Estimation ◽

Natural Phenomena ◽

Frequency Distributions ◽

Erosion Rates ◽

3D Point Clouds

The use of 3D point clouds to improve the understanding of natural phenomena is currently applied in natural hazard investigations, including the quantification of rockfall activity. However, 3D point cloud treatment is typically accomplished using nondedicated (and not optimal) software. To fill this gap, we present an open-source, specific rockfall package in an object-oriented toolbox developed in the MATLAB® environment. The proposed package offers a complete and semiautomatic 3D solution that spans from extraction to identification and volume estimations of rockfall sources using state-of-the-art methods and newly implemented algorithms. To illustrate the capabilities of this package, we acquired a series of high-quality point clouds in a pilot study area referred to as the La Cornalle cliff (West Switzerland), obtained robust volume estimations at different volumetric scales, and derived rockfall magnitude–frequency distributions, which assisted in the assessment of rockfall activity and long-term erosion rates. An outcome of the case study shows the influence of the volume computation on the magnitude–frequency distribution and ensuing erosion process interpretation.

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Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds

Remote Sensing ◽

10.3390/rs11121453 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1453 ◽

Cited By ~ 5

Author(s):

Shanxin Zhang ◽

Cheng Wang ◽

Lili Lin ◽

Chenglu Wen ◽

Chenhui Yang ◽

...

Keyword(s):

Traffic Safety ◽

Laser Scanning ◽

Large Scale ◽

Evaluation Method ◽

Evaluation Model ◽

Point Clouds ◽

Traffic Sign ◽

Traffic Signs ◽

3D Point Clouds ◽

Traffic Environment

Maintaining the high visual recognizability of traffic signs for traffic safety is a key matter for road network management. Mobile Laser Scanning (MLS) systems provide efficient way of 3D measurement over large-scale traffic environment. This paper presents a quantitative visual recognizability evaluation method for traffic signs in large-scale traffic environment based on traffic recognition theory and MLS 3D point clouds. We first propose the Visibility Evaluation Model (VEM) to quantitatively describe the visibility of traffic sign from any given viewpoint, then we proposed the concept of visual recognizability field and Traffic Sign Visual Recognizability Evaluation Model (TSVREM) to measure the visual recognizability of a traffic sign. Finally, we present an automatic TSVREM calculation algorithm for MLS 3D point clouds. Experimental results on real MLS 3D point clouds show that the proposed method is feasible and efficient.

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USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-433-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 433-439 ◽

Cited By ~ 4

Author(s):

Bisheng Yang ◽

Yuan Liu ◽

Fuxun Liang ◽

Zhen Dong

Keyword(s):

Laser Scanning ◽

Human Error ◽

Traffic Accidents ◽

Three Dimensional ◽

Point Clouds ◽

High Accuracy ◽

3D Point Clouds ◽

Assistant Systems ◽

Novel Method ◽

High Flexibility

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

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A novel method for extracting skeleton of fruit tree from 3D point clouds

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962320500518 ◽

2020 ◽

pp. 2050051

Author(s):

Shenglian lu ◽

Guo Li ◽

Jian Wang

Keyword(s):

Laser Scanning ◽

Laser Scanner ◽

Point Clouds ◽

Fruit Trees ◽

Fruit Tree ◽

Tree Canopy ◽

3D Point Clouds ◽

Novel Method ◽

Manual Interaction ◽

Traditional Approaches

Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree. The phenomenon of organs’ mutual occlusion in fruit tree canopy is usually very serious, this should result in a large amount of data missing in directed laser scanning 3D point clouds from a fruit tree. However, traditional approaches can be ineffective and problematic in extracting the tree skeleton correctly when the tree point clouds contain occlusions and missing points. To overcome this limitation, we present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds. The proposed method selects the start point and endpoint of a branch from the point clouds by user’s manual interaction, then a backward searching is used to find a path from the 3D point cloud with a radius parameter as a restriction. The experimental results in several kinds of fruit trees demonstrate that our method can extract the skeleton of a leafy fruit tree with highly accuracy.

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Special Issue on “Terrestrial Laser Scanning”: Editors’ Notes

Sensors ◽

10.3390/s19204569 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4569

Author(s):

Joan R. Rosell-Polo ◽

Eduard Gregorio ◽

Jordi Llorens

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Point Clouds ◽

Special Issue ◽

Alternative Technologies ◽

Cloud Processing ◽

3D Point Clouds ◽

Point Cloud Processing

In this editorial, we provide an overview of the content of the special issue on “Terrestrial Laser Scanning”. The aim of this Special Issue is to bring together innovative developments and applications of terrestrial laser scanning (TLS), understood in a broad sense. Thus, although most contributions mainly involve the use of laser-based systems, other alternative technologies that also allow for obtaining 3D point clouds for the measurement and the 3D characterization of terrestrial targets, such as photogrammetry, are also considered. The 15 published contributions are mainly focused on the applications of TLS to the following three topics: TLS performance and point cloud processing, applications to civil engineering, and applications to plant characterization.

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Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9090535 ◽

2020 ◽

Vol 9 (9) ◽

pp. 535

Author(s):

Francesca Matrone ◽

Eleonora Grilli ◽

Massimo Martini ◽

Marina Paolanti ◽

Roberto Pierdicca ◽

...

Keyword(s):

Deep Learning ◽

Cultural Heritage ◽

Laser Scanning ◽

Semantic Segmentation ◽

Point Clouds ◽

Classification Algorithms ◽

Learning Methods ◽

3D Point Clouds ◽

The Subject

In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented.

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