1,721,002 research outputs found
Classification of 3D Digital Heritage
Full text linkIn 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
Machine Learning Generalisation across Different 3D Architectural Heritage
Full text linkThe use of machine learning techniques for point cloud classification has been investigated extensively in the last decade in the geospatial community, while in the cultural heritage field it has only recently started to be explored. The high complexity and heterogeneity of 3D heritage data, the diversity of the possible scenarios, and the different classification purposes that each case study might present, makes it difficult to realise a large training dataset for learning purposes. An important practical issue that has not been explored yet, is the application of a single machine learning model across large and different architectural datasets. This paper tackles this issue presenting a methodology able to successfully generalise to unseen scenarios a random forest model trained on a specific dataset. This is achieved looking for the best features suitable to identify the classes of interest (e.g., wall, windows, roof and columns)
Investigating the Sorption/Desorption of the Cationic Herbicide Paraquat in Clay Minerals Using Batch and Electro–Ultrafiltration Techniques
No full textThe sorption/desorption processes of the cationic herbicide paraquat (PQ) onto various clays, namely, kaolinite (KLN), illite (ILT), and montmorillonite (MNT), were investigated. After the attainment of sorption equilibrium, PQ was extracted from the clays by a double-stage desorption process utilizing an electro-ultrafiltration (EUF) procedure. The Freundlich isotherm model and a pseudo-first kinetic release model were found to adequately fit the sorption and desorption data, respectively. The experimental maximum sorbable amounts of paraquat were 5.56, 31.88, and 91.63 mg g-1 for KLN, ILT, and MNT, respectively, consistently with the order of magnitude of the cation-exchange capacity (CEC) of the clay minerals. The desorption experiments revealed that the amounts of PQ retained by the MNT samples were significantly larger than the respective amounts retained by KLN or ILT. The EUF-PQ desorption patterns of differently cation-saturated MNT samples indicated that the presence of monovalent cations could further hamper PQ release, while the opposite seemed to be true for divalent cations. Our results clearly show that a substantial aliquot of PQ is strongly retained by montmorillonite, probably via interlayering, thus suggesting that smectitic clays could act as a stable soil sink for cationic herbicides such as paraquat, favoring soil long-term contamination
Supervised segmentation of 3D cultural heritage
No full textThe use of 3D models for the documentation and dissemination of cultural and archaeological heritage is widespread today. Nevertheless, to provide useful 3D data, it is important to associate semantic information that can help operators understand the heritage. This study aims at carrying out an optimal, repeatable and reliable segmentation procedure to manage various types of 3D survey data and associate them with heterogeneous information and attributes to characterize and describe a surveyed object. The developed method starts from 2D supervised machine learning segmentation of orthoimages or UV maps and then projects the segmentation results on the 3D data
Knowledge Enhanced Neural Networks for Point Cloud Semantic Segmentation
No full textDeep learning approaches have sparked much interest in the AI community during the last decade, becoming state-of-the-art in domains such as pattern recognition, computer vision, and data analysis. However, these methods are highly demanding in terms of training data, which is often a major issue in the geospatial and remote sensing fields. One possible solution to this problem comes from the Neuro-Symbolic Integration field (NeSy), where multiple methods have been defined to incorporate background knowledge into the neural network’s learning pipeline. One such method is KENN (Knowledge Enhanced Neural Networks), which injects logical knowledge into the neural network’s structure through additional final layers. Empirically, KENN showed comparable or better results than other NeSy frameworks in various tasks while being more scalable. Therefore, we propose the usage of KENN for point cloud semantic segmentation tasks, where it has immense potential to resolve issues with small sample sizes and unbalanced classes. While other works enforce the knowledge constraints in post-processing, to the best of our knowledge, no previous methods have injected inject such knowledge into the learning pipeline through the use of a NeSy framework. The experiment results over different datasets demonstrate that the introduction of knowledge rules enhances the performance of the original network and achieves state-of-the-art levels of accuracy, even with subideal training data
A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification
Full text linkThe recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution
A Quali-quantitative evaluation approach to pedodiversity by multivariate analysis: introduction to the concept of "pedocharacter"
Full text linkA model has been developed for the interpretation of the complexity of pedological systems; this is referred to as “pedocharacter”. The main aim of the model was to reduce the variables able to define soils and their relationships with the environment through the following quali-quantitative approach: i) definition of a fair number of qualitative characters; and ii) development of an analytic
function, defined as “Land Relevance of the Factor”
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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