Close-Range Sensing and Data Fusion for Built Heritage Inspection and Monitoring - A Review

Built cultural heritage is under constant threat due to environmental pressures, anthropogenic damages, and interventions. Understanding the preservation state of monuments and historical structures, and the factors that alter their architectural and structural characteristics through time, is crucial for ensuring their protection. Therefore, inspection and monitoring techniques are essential for heritage preservation, as they enable knowledge about the altering factors that put built cultural heritage at risk, by recording their immediate effects on monuments and historic structures. Nondestructive evaluations with close-range sensing techniques play a crucial role in monitoring. However, data recorded by different sensors are frequently processed separately, which hinders integrated use, visualization, and interpretation. This article’s aim is twofold: i) to present an overview of close-range sensing techniques frequently applied to evaluate built heritage conditions, and ii) to review the progress made regarding the fusion of multi-sensor data recorded by them. Particular emphasis is given to the integration of data from metric surveying and from recording techniques that are traditionally non-metric. The article attempts to shed light on the problems of the individual and integrated use of image-based modeling, laser scanning, thermography, multispectral imaging, ground penetrating radar, and ultrasonic testing, giving heritage practitioners a point of reference for the successful implementation of multidisciplinary approaches for built cultural heritage scientific investigations

INTEGRATING MULTIBAND PHOTOGRAMMETRY, SCANNING, AND GPR FOR BUILT HERITAGE SURVEYS: THE FAÇADES OF CASTELLO DEL VALENTINO

The conservation of built heritage is a complex process that necessitates co-operative efforts. Holistic, integrated documentation constitutes a crucial step towards conservation by contributing to diagnosis and by extension to the effective decision-making about the required preventive and restorative interventions. It involves the recording of interdisciplinary data to produce objective diagnostical conclusions concerning the state of preservation. Although the developments in close-range sensing techniques allow increasingly accurate and rich data recording for heritage building condition surveys, the problem of combining them (to allow integrated processing) often remains unsolved. This is particularly true when surveys include vastly heterogenous documentation data. This work aims to discuss methodologies and implications of such integrations through a monumental heritage survey case—the Castello del Valentino in Turin (Italy). Visible-spectrum and infrared imagery is combined with photogrammetric techniques, terrestrial LiDAR, and microwave measurements conducted on the historical façades’ surfaces, to examine the comprehensiveness of the data fusion results, as well as conclusions that can be drawn regarding previous interventions and the current condition of the monument

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314

A new trend for knowledge-based decision support systems design

Knowledge-based decision support systems (KBDSS) have evolved greatly over the last few decades. The key technologies underpinning the development of KBDSS can be classified into three categories: technologies for knowledge modelling and representation, technologies for reasoning and inference and web-based technologies. In the meantime, service systems have emerged and become increasingly important to value adding activities in the current knowledge economy. This paper provides a review on the recent advances in the three types of technologies, as well as the main application domains of KBDSS as service systems. Based on the examination of literature, future research directions are recommended for the development of KBDSS in general and in particular to support decision-making in service industry

Enhancing forestry supply chain through innovative integration of digital tools and techniques

Online communities allow problem drinkers to seek help anonymously without the judgement present in the face-to-face world. This article investigates peer to peer online support in an online community of self-identified problem drinkers. A content analysis was performed on a Stop Drinking Reddit.com community and 26 themes of interaction, belonging to 9 categories were identified as key interactions likely to provide value to those seeking to instantiate or maintain sobriety. These themes were arranged into a conceptual model consisting of three dimensions of interaction namely: goal-based interaction; relationship-based interactions; and platform-interactions. The conceptual model created by this research should help those in recovery utilize online communities more effectively and provide insight into how peer to peer online social support can be deliberately utilized to promote sobriety

Design of a data management reference architecture for sustainable agriculture

Effective and efficient data management is crucial for smart farming and precision agri-culture. To realize operational efficiency, full automation, and high productivity in agricultural systems, different kinds of data are collected from operational systems using different sensors, stored in different systems, and processed using advanced techniques, such as machine learning and deep learning. Due to the complexity of data management operations, a data management reference architecture is required. While there are different initiatives to design data management reference architectures, a data management reference architecture for sustainable agriculture is missing. In this study, we follow domain scoping, domain modeling, and reference architecture design stages to design the reference architecture for sustainable agriculture. Four case studies were performed to demonstrate the applicability of the reference architecture. This study shows that the proposed data management reference architecture is practical and effective for sustainable agriculture.Scopus2-s2.0-8510941411