<p>3D modelling of archaeological and historical structures is the new frontier in the field of conservation science. Similarly, the identification of buried finds, which enhances their multimedia diffusion and restoration, has gained relevance. As such sites often have a high level of structural complexity and complicated territorial geometries, accuracy in the creation of 3D models and the use of sophisticated algorithms for georadar data analysis are crucial. This research is the first step in a larger project aimed at reclaiming the ancient villages located in the Greek area of southern Italy. The present study focuses on the restoration of the village of Africo (RC), a village hit by past flooding. The survey began with a laser scan of the church of St. Nicholas, using both the Faro Focus3D and the Riegl LMS-Z420i laser scanner. At the same time, georadar analyses were carried out in order to pinpoint any buried objects. In the processing phase, our own MATLAB algorithms were used for both laser scanner and georadar datasets and the results compared with those obtained from the scanners’ respective proprietary software. We are working to develop a tourism app in both augmented and virtual reality environments, in order to disseminate and improve access to cultural heritage. The app allows users to see the 3D model and simultaneously access information on the site integrated from a variety of repositories. The aim is to create an immersive visit, in this case, to the church of St. Nicholas.</p><p><strong>Highlights:</strong></p><ul><li><p>Use of different algorithms for registration of terrestrial laser scans and analysis of the data obtained.</p></li><li><p>3D acquisition, processing and restitution methodology from georadar data.</p></li><li><p>Implementation of a tourist app in both virtual and augmented reality by integrating geomatics methodologies.</p></li></ul>
<p><strong>Abstract.</strong> Modern surveying techniques, with the combined use of Unmanned Aerial Vehicles (UAV) with low-cost photographic sensors, and photogrammetric techniques, allows obtaining a precise virtual reconstruction of environment and object with centimetre accuracy. Recently, the diffusion of UAV allows the survey of extensive areas significantly reducing survey time and costs. The raw output obtainable from such survey operations consists of a three-dimensional point cloud. Numerous applications in architecture, monitoring and surveying and structural analysis require objects identification in the 3d scene to classify different element in the acquired scene and extract relevant information. Point cloud analysis, and in particular segmentation and classification techniques, are actually used to identify objects within the scenes, assign to a specific class and use them for subsequent studies. These techniques represent an open research theme and the key to add value to the entire process. Actual methodologies are based on 3d spatial analysis on the point cloud. In this paper, starting from photogrammetric reconstruction, a methodology for segmentation and classification of point cloud based on image analysis is presented. The object identification on the image’s dataset is performed using a Neural Network and subsequently the identified object on dataset are transfer into the 3d environment. This classification is performed to segment structural parts of bridges and viaduct, acquire geometric information, and perform a structural analysis to preserve relevant and ancient structure. A case study for the segmentation of the point cloud acquired with an aerial survey of a Viaduct is presented. The performed segmentation allows obtaining structural elements of different type of viaduct and bridges, is propaedeutic to verify the health of the structure and schedule maintenance intervention. The methodology can be applied to different type of bridges, from reinforced concrete to ancient masonry to preserve the state of conservation.</p>
Geomatics is important for agriculture 4.0; in fact, it uses different types of data (remote sensing from satellites, Unmanned Aerial Vehicles-UAVs, GNSS, photogrammetry, laser scanners and other types of data) and therefore it uses data fusion techniques depending on the different applications to be carried out. This work aims to present on a study area concerning the integration of data acquired (using data fusion techniques) from remote sensing techniques, UAVs, autonomous driving machines and data fusion, all reprocessed and visualised in terms of results obtained through GIS (Geographic Information System). In this work we emphasize the importance of the integration of different methodologies and data fusion techniques, managing data of a different nature acquired with different methodologies to optimise vineyard cultivation and production. In particular, in this note we applied (focusing on a vineyard) geomatics-type methodologies developed in other works and integrated here to be used and optimised in order to make a contribution to agriculture 4.0. More specifically, we used the NDVI (Normalized Difference Vegetation Index) applied to multispectral satellite images and drone images (suitably combined) to identify the vigour of the plants. We then used an autonomous guided vehicle (equipped with sensors and monitoring systems) which, by estimating the optimal path, allows us to optimise fertilisation, irrigation, etc., by data fusion techniques using various types of sensors. Everything is visualised on a GIS to improve the management of the field according to its potential, also using historical data on the environmental, climatic and socioeconomic characteristics of the area. For this purpose, experiments of different types of Geomatics carried out individually on other application cases have been integrated into this work and are coordinated and integrated here in order to provide research/application cues for Agriculture 4.0.
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