scholarly journals Classification of 3D Digital Heritage

2019 ◽  
Vol 11 (7) ◽  
pp. 847 ◽  
Author(s):  
Eleonora Grilli ◽  
Fabio Remondino
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Point Clouds ◽  
3D Models ◽  
Classification Methods ◽  
Polygonal Mesh ◽  
Archaeological Heritage ◽  
3D Data ◽  
2D Data

In recent years, the use of 3D models in cultural and archaeological heritage for documentation and dissemination purposes is increasing. The association of heterogeneous information to 3D data by means of automated segmentation and classification methods can help to characterize, describe and better interpret the object under study. Indeed, the high complexity of 3D data along with the large diversity of heritage assets themselves have constituted segmentation and classification methods as currently active research topics. Although machine learning methods brought great progress in this respect, few advances have been developed in relation to cultural heritage 3D data. Starting from the existing literature, this paper aims to develop, explore and validate reliable and efficient automated procedures for the classification of 3D data (point clouds or polygonal mesh models) of heritage scenarios. In more detail, the proposed solution works on 2D data (“texture-based” approach) or directly on the 3D data (“geometry-based approach) with supervised or unsupervised machine learning strategies. The method was applied and validated on four different archaeological/architectural scenarios. Experimental results demonstrate that the proposed approach is reliable and replicable and it is effective for restoration and documentation purposes, providing metric information e.g. of damaged areas to be restored.

scholarly journals GEOMETRIC FEATURES ANALYSIS FOR THE CLASSIFICATION OF CULTURAL HERITAGE POINT CLOUDS

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 541-548 ◽  
Author(s):  
E. Grilli ◽  
E. M. Farella ◽  
A. Torresani ◽  
F. Remondino
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cultural Heritage ◽  
Point Clouds ◽  
Geometric Features ◽  
3D Point Clouds ◽  
3D Data ◽  
3D Documentation ◽  
The Impact

<p><strong>Abstract.</strong> In the last years, the application of artificial intelligence (Machine Learning and Deep Learning methods) for the classification of 3D point clouds has become an important task in modern 3D documentation and modelling applications. The identification of proper geometric and radiometric features becomes fundamental to classify 2D/3D data correctly. While many studies have been conducted in the geospatial field, the cultural heritage sector is still partly unexplored. In this paper we analyse the efficacy of the geometric covariance features as a support for the classification of Cultural Heritage point clouds. To analyse the impact of the different features calculated on spherical neighbourhoods at various radius sizes, we present results obtained on four different heritage case studies using different features configurations.</p>

scholarly journals Comparing Different Machine Learning Techniques for Classifying Multi Label Data

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Shriya Salunkhe ◽  
◽  
Kiran Bhowmick ◽  
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Machine Learning Techniques ◽  
Classification Methods ◽  
Label Data ◽  
Huge Data ◽  
Problem Transformation ◽  
Learning Techniques ◽  
Single Instance

In recent years, multi-label classifications have become common. Multi label classification is a classification in which a collection of labels is associated with a single instance, which may be a variation of the classification of a single label. The problem of huge data is the classification in which each instance is of different kind which further can be identified with more than one class. The various machine learning strategies for classifying multi-label data are discussed in this paper. Many researches have been carried out that specify the grouping of multiple labels. Here we will compare various classification machine learning techniques that involve two approaches: the adapted algorithm approach and the method of problem transformation. Here we are using naive multinomial bayes and logistic regression. We use certain evaluation metrics to predict the differences as well. Better classification methods are discussed in this paper.

scholarly journals APPLICATION OF MACHINE AND DEEP LEARNING STRATEGIES FOR THE CLASSIFICATION OF HERITAGE POINT CLOUDS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 447-454 ◽  
Author(s):  
E. Grilli ◽  
E. Özdemir ◽  
F. Remondino
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Point Clouds ◽  
Learning Approaches ◽  
Automated Classification ◽  
Machine Learning Methods ◽  
3D Data ◽  
Classification Procedures

Abstract. The use of heritage point cloud for documentation and dissemination purposes is nowadays increasing. The association of semantic information to 3D data by means of automated classification methods can help to characterize, describe and better interpret the object under study. In the last decades, machine learning methods have brought significant progress to classification procedures. However, the topic of cultural heritage has not been fully explored yet. This paper presents a research for the classification of heritage point clouds using different supervised learning approaches (Machine and Deep learning ones). The classification is aimed at automatically recognizing architectural components such as columns, facades or windows in large datasets. For each case study and employed classification method, different accuracy metrics are calculated and compared.

scholarly journals Monitoring and Computation of the Volumes of Stockpiles of Bulk Material by Means of UAV Photogrammetric Surveying

2019 ◽  
Vol 11 (12) ◽  
pp. 1471 ◽  
Author(s):  
Grazia Tucci ◽  
Antonio Gebbia ◽  
Alessandro Conti ◽  
Lidia Fiorini ◽  
Claudio Lubello
Keyword(s):  
Bulk Material ◽  
Laser Scanner ◽  
Ground Truth ◽  
Point Clouds ◽  
3D Models ◽  
Satellite System ◽  
3D Data ◽  
Current Survey ◽  
Close Range ◽  
Global Navigation Satellite

The monitoring and metric assessment of piles of natural or man-made materials plays a fundamental role in the production and management processes of multiple activities. Over time, the monitoring techniques have undergone an evolution linked to the progress of measure and data processing techniques; starting from classic topography to global navigation satellite system (GNSS) technologies up to the current survey systems like laser scanner and close-range photogrammetry. Last-generation 3D data management software allow for the processing of increasingly truer high-resolution 3D models. This study shows the results of a test for the monitoring and computing of stockpile volumes of material coming from the differentiated waste collection inserted in the recycling chain, performed by means of an unmanned aerial vehicle (UAV) photogrammetric survey and the generation of 3D models starting from point clouds. The test was carried out with two UAV flight sessions, with vertical and oblique camera configurations, and using a terrestrial laser scanner for measuring the ground control points and as ground truth for testing the two survey configurations. The computations of the volumes were carried out using two software and comparisons were made both with reference to the different survey configurations and to the computation software.

scholarly journals Classification of Photogrammetric and Airborne LiDAR Point Clouds Using Machine Learning Algorithms

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 104
Author(s):  
Zaide Duran ◽  
Kubra Ozcan ◽  
Muhammed Enes Atik
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Point Clouds ◽  
Machine Learning Algorithms ◽  
Airborne Lidar ◽  
Aerial Photogrammetry ◽  
Feature Spaces ◽  
Wide Range ◽  
Extract Information

With the development of photogrammetry technologies, point clouds have found a wide range of use in academic and commercial areas. This situation has made it essential to extract information from point clouds. In particular, artificial intelligence applications have been used to extract information from point clouds to complex structures. Point cloud classification is also one of the leading areas where these applications are used. In this study, the classification of point clouds obtained by aerial photogrammetry and Light Detection and Ranging (LiDAR) technology belonging to the same region is performed by using machine learning. For this purpose, nine popular machine learning methods have been used. Geometric features obtained from point clouds were used for the feature spaces created for classification. Color information is also added to these in the photogrammetric point cloud. According to the LiDAR point cloud results, the highest overall accuracies were obtained as 0.96 with the Multilayer Perceptron (MLP) method. The lowest overall accuracies were obtained as 0.50 with the AdaBoost method. The method with the highest overall accuracy was achieved with the MLP (0.90) method. The lowest overall accuracy method is the GNB method with 0.25 overall accuracy.

scholarly journals 3D survey for the archaeological study and virtual reconstruction of the “Sanctuary of Isis” in the ancient Lilybaeum (Italy)

2020 ◽  
Vol 11 (22) ◽  
pp. 1 ◽  
Author(s):  
Leonarda Fazio ◽  
Mauro Lo Brutto
Keyword(s):  
Three Dimensional ◽  
Archaeological Site ◽  
3D Models ◽  
Archaeological Remains ◽  
Archaeological Heritage ◽  
Digital Reconstruction ◽  
3D Environment ◽  
3D Data ◽  
Open Questions ◽  
Virtual Reconstruction

<p class="VARKeywords">In recent years, the use of three-dimensional (3D) models in cultural and archaeological heritage for documentation and dissemination purposes has increased. New geomatics technologies have significantly reduced the time spent on fieldwork surveys and data processing. The archaeological remains can be documented and reconstructed in a digital 3D environment thanks to the new 3D survey technologies. Furthermore, the products generated by modern surveying technologies can be reconstructed in a virtual environment on effective archaeological bases and hypotheses coming from a detailed 3D data analysis. However, the choice of technologies that should be used to get the best results for different archaeological remains and how to use 3D models to improve knowledge and dissemination to a wider audience are open questions.</p><p class="VARKeywords">This paper deals with the use of terrestrial laser scanners and photogrammetric surveys for the virtual reconstruction of an archaeological site. In particular, the work describes the study for the 3D documentation and virtual reconstruction of the “Sanctuary of Isis” in <em>Lilybaeum,</em> the ancient city of Marsala (southern Italy). The "Sanctuary of Isis" is the only Roman sacred building known in this archaeological area. Based on the survey data, it has been possible to recreate the original volumes of the ancient building and rebuild the two best-preserved floors –a geometric mosaic and an <em>opus spicatum</em>– for a first digital reconstruction of the archaeological complex in a 3D environment.</p>

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