ESQUINA MULTIMEDIA – MUSEUM EXHIBITION FOR THE VISUALIZATION OF CHAN CHAN ARCHAEOLOGICAL SITE

[EN] Chan Chan, an archaeological site located at Trujillo, Peru, is a huge historical settlement very large and difficult to visit and some well-conserved architecture, like Huaca Arco Iris, is very far from the core centre of the site. Furthermore many other heavy factors, as illegal excavations, marine salt transported by the wind and the sometime devastating phenomenon of the Niño, are the reasons of the lost of many decorative elements, which are covered due to conservation issues. To overcome the aforesaid problems, we designed, developed and realized the museum exhibition called “Esquina Multimedia”, providing the tourists with interactive and enjoyable applications. An Augmented Reality application has been developed in order to discover ancient artefacts that are invisible because covered by the earth (or by protection structures). A web-browser has been specifically designed to show bas-relieves, with HD visualization and with anaglyph stereoscopic view. Herewith, a wall-mounted panel representing a metric 3D reconstruction by an accurate survey of the building helps the user to find the artefact position.Pierdicca, R.; Malinverni, ES.; Frontoni, E.; Colosi, F.; Orazi, R. (2016). ESQUINA MULTIMEDIA – MUSEUM EXHIBITION FOR THE VISUALIZATION OF CHAN CHAN ARCHAEOLOGICAL SITE. En 8th International congress on archaeology, computer graphics, cultural heritage and innovation. Editorial Universitat Politècnica de València. 274-276. https://doi.org/10.4995/arqueologica8.2016.3191OCS27427

Mo.Se.: Segmentación de mosaico de imágenes basado en aprendizaje profundo en cascada

[EN] Mosaic is an ancient type of art used to create decorative images or patterns combining small components. A digital version of a mosaic can be useful for archaeologists, scholars and restorers who are interested in studying, comparing and preserving mosaics. Nowadays, archaeologists base their studies mainly on manual operation and visual observation that, although still fundamental, should be supported by an automatized procedure of information extraction. In this context, this research explains improvements which can change the manual and time-consuming procedure of mosaic tesserae drawing. More specifically, this paper analyses the advantages of using Mo.Se. (Mosaic Segmentation), an algorithm that exploits deep learning and image segmentation techniques; the methodology combines U-Net 3 Network with the Watershed algorithm. The final purpose is to define a workflow which establishes the steps to perform a robust segmentation and obtain a digital (vector) representation of a mosaic. The detailed approach is presented, and theoretical justifications are provided, building various connections with other models, thus making the workflow both theoretically valuable and practically scalable for medium or large datasets. The automatic segmentation process was tested with the high-resolution orthoimage of an ancient mosaic by following a close-range photogrammetry procedure. Our approach has been tested in the pavement of St. Stephen's Church in Umm ar-Rasas, a Jordan archaeological site, located 30 km southeast of the city of Madaba (Jordan). Experimental results show that this generalized framework yields good performances, obtaining higher accuracy compared with other state-of-the-art approaches. Mo.Se. has been validated using publicly available datasets as a benchmark, demonstrating that the combination of learning-based methods with procedural ones enhances segmentation performance in terms of overall accuracy, which is almost 10% higher. This study’s ambitious aim is to provide archaeologists with a tool which accelerates their work of automatically extracting ancient geometric mosaics.Highlights:A Mo.Se. (Mosaic Segmentation) algorithm is described with the purpose to perform robust image segmentation to automatically detect tesserae in ancient mosaics.This research aims to overcome manual and time-consuming procedure of tesserae segmentation by proposing an approach that uses deep learning and image processing techniques, obtaining a digital replica of a mosaic.Extensive experiments show that the proposed framework outperforms state-of-the-art methods with higher accuracy, even compared with publicly available datasets.[ES] El mosaico es un tipo de arte antiguo utilizado para crear imágenes decorativas o patrones de pequeños componentes. Una versión digital de un mosaico puede ser útil a los arqueólogos, estudiosos y restauradores que están interesados en el estudio, la comparación y la preservación de los mosaicos. Hoy en día, los arqueólogos basan sus estudios principalmente en la operación manual y la observación visual que, aunque sigue siendo fundamental, debe ser apoyada con la ayuda de un procedimiento automatizado de extracción de la información. En este contexto, esta investigación tiene la intención de superar el procedimiento manual y lento del dibujo de teselas en mosaico proponiendo Mo.Se. (Mosaic Segmentation), un algoritmo que explota técnicas de aprendizaje profundo y segmentación de imagen; específicamente, la metodología combina la red U-Net 3 con el algoritmo Watershed. El propósito final es definir un flujo de trabajo que establezca los pasos para realizar una segmentación robusta y obtener una representación digital (vectorial) de un mosaico. Se presenta el procedimiento detallado y se proporcionan justificaciones teóricas, construyendo varias conexiones con otros modelos, haciendo que el flujo de trabajo sea teóricamente valioso y prácticamente escalable en conjuntos de datos medianos o grandes. El proceso de segmentación automática se probó con la ortoimagen de alta resolución de un mosaico antiguo, siguiendo un procedimiento de fotogrametría de objeto cercano. Nuestro enfoque se ha probado en el pavimento de la Iglesia de San Esteban en Umm ar-Rasas, un sitio arqueológico de Jordania, ubicado a 30 km al sureste de la ciudad de Madaba (Jordania). Los resultados experimentales muestran que este marco generalizado produce buenos rendimientos, obteniendo una mayor precisión en comparación con otros enfoques de vanguardia. Mo.Se. se ha validado utilizando conjuntos de datos disponibles públicamente como punto de referencia, lo que demuestra que la combinación de métodos basadosen el aprendizaje con métodos procedimentales mejora el rendimiento de la segmentación en casi un 10% en términos de exactitud en general. El ambicioso objetivo de este estudio es proporcionar a los arqueólogos una herramienta que acelere su trabajo de extracción automática de mosaicos geométricos antiguos.This work was partially found within the framework of the project Innovative technologies and training activities for the conservation and enhancement of the archaeological site of Umm er-Rasas (Jordan) funded by Ministero degli Affari Esteri e della Cooperazione Internazionale. The authors would like to express their gratitude to the ISPC CNR and in particular to Dott. Roberto Gabrielli (project leader) and Alessandra Albiero for providing the dataset.Felicetti, A.; Paolanti, M.; Zingaretti, P.; Pierdicca, R.; Malinverni, ES. (2021). Mo.Se.: Mosaic image segmentation based on deep cascading learning. Virtual Archaeology Review. 12(24):25-38. https://doi.org/10.4995/var.2021.14179OJS25381224Bartoli, A., Fenu, G., Medvet, E., Pellegrino, F. A., & Timeus, N. (2016, November). Segmentation of Mosaic Images Based on Deformable Models Using Genetic Algorithms. In International Conference on Smart Objects and Technologies for Social Good (pp. 233-242). Springer, Cham. https://doi.org/10.1007/978-3-319-61949-1_25Battiato, S., Di Blasi, G., Farinella, G. M., & Gallo, G. (2007, December). Digital mosaic frameworks‐an overview. In computer graphics forum (Vol. 26, No. 4, pp. 794-812). Oxford, UK: Blackwell Publishing Ltd. https://doi.org/10.1111/j.1467-8659.2007.01021.xBeucher, S., & Lantuéjoul, C. (1979). Use of watersheds in contour detection. International workshop on image processing: Real-time edge and motion detection/estimation. Rennes, France.Benyoussef, L., & Derrode, S. (2011). Analysis of ancient mosaic images for dedicated applications. Digital Imaging for Cultural Heritage Preservation: Analysis, Restoration, and Reconstruction of Ancient Artworks, 385.Bonfigli, R., Felicetti, A., Principi, E., Fagiani, M., Squartini, S., & Piazza, F. (2018). Denoising autoencoders for non-intrusive load monitoring: improvements and comparative evaluation. Energy and Buildings, 158. https://doi.org/10.1016/j.enbuild.2017.11.054Bordoni, L., & Mele, F. (Eds.). (2016). Artificial intelligence for cultural heritage. Cambridge Scholars Publishing.Bourke, P. (2014, December). Novel imaging of heritage objects and sites. In 2014 International Conference on Virtual Systems & Multimedia (VSMM) (pp. 25-30). IEEE. 10.1109/VSMM.2014.7136666Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016, October). 3D U-Net: learning dense volumetric segmentation from sparse annotation. In International conference on medical image computing and computer-assisted intervention (pp. 424-432). Springer, Cham. https://doi.org/10.1007/978-3-319-46723-8_49Cipriani, L., & Fantini, F. (2017). Digitalization culture VS archaeological visualization: integration of pipelines and open issues. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 195. https://doi.org/10.5194/isprs-archives-XLII-2-W3-195-2017Djibril, M. O., & Thami, R. O. H. (2008). Islamic geometrical patterns indexing and classification using discrete symmetry groups. Journal on Computing and Cultural Heritage (JOCCH), 1(2), 1-14. https://doi.org/10.1145/1434763.1434767Djibril, M. O., Thami, R. O. H., Benslimane, R., & Daoudi, M. (2005). Une nouvelle technique pour l'indexation des arabesques basée sur la dimension fractale. Univ. Mohamed V, Maroc.Falk, T., Mai, D., Bensch, R., Çiçek, Ö., Abdulkadir, A., Marrakchi, Y., Böhm, A., Deubner, J., Jäckel, Z., Seiwald, K., & Dovzhenko, A. (2019). U-Net: deep learning for cell counting, detection, and morphometry. Nature methods, 16(1), 67-70. https://doi.org/10.1038/s41592-018-0261-2Felicetti, A., Albiero, A., Gabrielli, R., Pierdicca, R., Paolanti, M., Zingaretti, P., & Malinverni, E. S. (2018). Automatic Mosaic Digitalization: a Deep Learning approach to tessera segmentation. In METROARCHEO, IEEE International Conference on Metrology for Archaeology and Cultural Heritage. Cassino. https://doi.org/10.1109/MetroArchaeo43810.2018.13606Fenu, G., Jain, N., Medvet, E., Pellegrino, F. A., & Namer, M. P. (2015, March). On the Assessment of Segmentation Methods for Images of Mosaics. In VISAPP (3) (pp. 130-137). https://doi.org/10.13140/RG.2.1.3025.6489Fenu, G., Medvet, E., Panfilo, D., & Pellegrino, F. A. (2020). Mosaic Images Segmentation using U-net. In International Conference on Pattern Recognition Applications and Methods (pp. 485-492). Scitepress. http://dx.doi.org/10.5220/0008967404850492Fontanella, F., Molinara, M., Gallozzi, A., Cigola, M., Senatore, L. J., Florio, R., Clini, P., & Celis, F. (2019, June). HeritageGO (HeGO) A Social Media Based Project for Cultural Heritage Valorization. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization (pp. 377-382). https://doi.org/10.1145/3314183.3323863Gil, F. A., Gomis, J. M., & Pérez, M. (2009). Reconstruction Techniques for Image Analysis of Ancient Islamic Mosaics. International Journal of Virtual Reality, 8(3), 5-12. https://doi.org/10.20870/IJVR.2009.8.3.2735Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.Kohl, S., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K., Eslami, S.M.A, Rezende, D.J., & Ronneberger, O. (2018). A probabilistic u-net for segmentation of ambiguous images. In Advances in Neural Information Processing Systems (pp. 6965-6975). https://arxiv.org/abs/1806.05034Liciotti, D., Paolanti, M., Pietrini, R., Frontoni, E., & Zingaretti, P. (2018, August). Convolutional networks for semantic heads segmentation using top-view depth data in crowded environment. In 2018 24th international conference on pattern recognition (ICPR) IEEE. https://doi.org/10.1109/ICPR.2018.8545397Maghrebi, W., Ammar, A. B., Alimi, A. M., & Khabou, M. A. (2013). An Intelligent mutli-object retrieval system for historical mosaics. Editorial Preface, 4(4). https://doi.org/10.14569/IJACSA.2013.040417Maghrebi, W., Baccour, L., Khabou, M. A., & Alimi, A. M. (2007, November). An indexing and retrieval system of historic art images based on fuzzy shape similarity. In Mexican International Conference on Artificial Intelligence (pp. 623-633). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_59Maghrebi, W., Borchani, A., Khabou, M. A., & Alimi, A. M. (2007, September). A system for historic document image indexing and retrieval based on xml database conforming to mpeg7 standard. In International Workshop on Graphics Recognition (pp. 114-125). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88188-9_12Malinverni, E. S., Pierdicca, R., Di Stefano, F., Gabrielli, R., & Albiero, A. (2019). Virtual museum enriched by GIS data to share science and culture. Church of Saint Stephen in Umm Ar-Rasas (Jordan). Virtual Archaeology Review, 10(21). https://doi.org/10.4995/var.2019.11919M'hedhbi, M., Mezhoud, R., M'hiri, S., & Ghorbel, F. (2006, April). A new content-based image indexing and retrieval system of mosaic images. In 2006 2nd International Conference on Information & Communication Technologies (Vol. 1, pp. 1715-1719). IEEE. https://doi.org/10.1109/ICTTA.2006.1684644Pierdicca, R., Frontoni, E., Malinverni, E. S., Colosi, F., & Orazi, R. (2016). Virtual reconstruction of archaeological heritage using a combination of photogrammetric techniques: Huaca Arco Iris, Chan Chan, Peru. Digital Applications in Archaeology and Cultural Heritage, 3(3). https://doi.org/10.1016/j.daach.2016.06.002Pierdicca, R., Frontoni, E., Zingaretti, P., Malinverni, E. S., Colosi, F., & Orazi, R. (2015, August). Making visible the invisible. Augmented reality visualization for 3D reconstructions of archaeological sites. In International Conference on Augmented and Virtual Reality (Blinded for peer review). Springer, Cham. https://doi.org/10.1007/978-3-319-22888-4_3Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). 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An application of image processing in the medieval mosaic conservation. Pattern analysis and applications, 7(1), 18-25. https://doi.org/10.1007/s10044-003-0200-

insar decorrelation to assess and prevent volcanic risk

SAR� can� be� invaluable� describing� pre�eruption� surface� deformation� and� improving� the� understanding� of� volcanic� processes.� This� work� studies� correlation� of� pairs� of� SAR� images� focusing� on� the� inༀ䃻uence� of� surface,� climate� conditions� and� acquisition� band.� Chosen� L�band� and� C�band� images� (ENVISAT,� ERS� and� ALOS)� cover� most� of� the� Yellowstone� caldera� (USA)� over� a� span� of� 4� years,� sampling� all� the� seasons.� Interferograms� and� correlation� maps� are� generated� and� studied� in� relation� to� snow� depth� and� temperature.� To� isolate� temporal� decorrelation� pairs� of� images� with� the� shortest� baseline� are� chosen.� Results� show� good� performance� during� winter,� bad� attitude� towards� wet� snow� and� good� coherence� during� summer� with� L�band� performing� better� over� vegetation

3D visualization tools to explore ancient architectures in South America

[EN] Chan Chan is a wide archaeological site located in Peru. Its knowledge is limited to the visit of Palacio Tschudi, the only restored up to now, whilst the majority of the site remains unknown to the visitors. The reasons are manifold. The site is very large and difficult to visit. Some well-conserved architectures, such as Huaca Arco Iris, are very far from the core centre. Furthermore, there are heavy factors of decay, mainly caused by illegal excavations, by marine salt and by the devastating phenomenon of El Niño. For these reasons, the majority of the decorative elements are protected by new mud brick walls. Finally, the vastness of the buildings makes difficult to understand their real value, even through a direct visit of the site. In order to overcome the aforesaid problems, we designed, developed and realized the museum exhibition presented in this paper. We named Esquina Multimedia an installation where every corner is aimed to solve a specific problem, providing the tourists with interactive and enjoyable applications. The virtual tour allows reaching also the unreachable areas. An Augmented Reality (AR) application has been developed in order to show ancient artefacts covered by the earth. A web-browser has been specifically designed to show bas-reliefs, with HD visualization, anaglyph stereoscopic view and a 3D virtual model of both the structures and the bas-reliefs. At the same time, a wall-mounted panel representing a metric 3D reconstruction of the building helps the user to find the artefact position. Descriptions of the hardware components and of the software details are presented, with particular focus regarding the implementation of the application, arguing how the digital approach could represent the only answer towards a full exploitation of archaeological sites. The paper also deals with the implementation of a web tool, specifically designed to display and browse 3D-Models.Pierdicca, R.; Malinverni, ES.; Frontoni, E.; Colosi, F.; Orazi, R. (2016). 3D visualization tools to explore ancient architectures in South America. Virtual Archaeology Review. 7(15):44-53. doi:10.4995/var.2016.5904.SWORD445371

Digitalization and Spatial Documentation of Post-Earthquake Temporary Housing in Central Italy: An Integrated Geomatic Approach Involving UAV and a GIS-Based System

Geoinformation and aerial data collection are essential during post-earthquake emergency response. This research focuses on the long-lasting spatial impacts of temporary solutions, which have persisted in regions of Central Italy affected by catastrophic seismic events over the past 25 years, significantly and permanently altering their landscapes. The paper analyses the role of geomatic and photogrammetric tools in documenting the emergency process and projects in post-disaster phases. An Atlas of Temporary Architectures is proposed, which defines a common semantic and geometric codification for mapping temporary housing from territorial to urban and building scales. The paper presents an implementation of attribute specification in existing official cartographic data, including geometric entities in a 3D GIS data model platform for documenting and digitalising these provisional contexts. To achieve this platform, UAV point clouds are integrated with non-metric data to ensure a complete description in a multiscalar approach. Accurate topographic modifications can be captured by extracting very high-resolution orthophotos and elevation models (DSM and DTM). The results have been validated in Visso (Macerata), a small historical mountain village in Central Italy which was heavily damaged by the seismic events of 2016/2017. The integrated approach overcomes the existing gaps and emphasizes the importance of managing heterogeneous geospatial emergency data for classification purposes. It also highlights the need to enhance an interoperable knowledge base method for post-disaster temporary responses. By combining geomatic tools with architectural studies, these visualization techniques can support national and local organizations responsible for post-earthquake management through a 3D modelling method to aid future transformations or interventions following other natural disasters

Virtual museum enriched by GIS data to share science and culture. Church of Saint Stephen in Umm Ar-Rasas (Jordan)

[EN] Umm ar-Rasas is a Jordan archaeological site, located 30 km southeast of the city of Madaba, in the northern part of Wadi Mujib. It preserves findings dating back the period from the end of 3rd to the 9th century AD and, since 2004, it belongs to the world heritage list of UNESCO. In 2015 a multidisciplinary work was undertaken over the archaeological site, mainly focusing on the Church of Saint Stephen, with the main purpose of enhancing the knowledge and documenting the conservation state of the polychrome mosaic floor, which covers the entire surface of the hall and presbytery. A huge amount of data has been collected, coming from archaeological and historical investigations, geophysics and geodetic inspections and geomatics surveying, which produced also a true orthophoto of the mosaic floor. Data has been organized in a geo-database, facilitating the exchange of information between different actors. Moreover, the management of data within a dedicated Geographic Information System (GIS), has allowed in-depth analysis for understanding the evolution of the iconographic repertoire that, over the centuries, has undergone several disfigurements due to the iconoclastic age. The knowledge of the mosaic has also been vital for the implementation of multimedia applications and for the creation of virtual experiences, in which the information can be conveyed and visualized directly on the virtual reconstruction of the whole archaeological site. The innovation of the proposed work, is therefore in the management of a data flow that can be exploited by different actors through different platforms: experts, thanks to the use of GIS, and visitors with the use of multimedia applications (such as Augmented Reality (AR) or highresolution web visualization) for dissemination purposes, in order to preserve this priceless mankind heritage.Highlights:Definition of a complete pipeline ranging from data acquisition to visualization in multi-channel multimedia applications.Management of heterogeneous data in Geographic Information Systems (GIS) and their exploitation in Augmented and Virtual Reality (AR/VR).GIS applied to the archaeological domain for expert and non-expert users.[ES] Umm er-Rasas es un sitio arqueológico de Jordania, ubicado a 30 km al sureste de la ciudad de Madaba, en la parte norte de Wadi Mujib. Conserva hallazgos que datan del período comprendido entre finales del siglo III y IX d.C. y, desde 2004, pertenece a la lista del patrimonio mundial de la UNESCO. En 2015, se realizó un trabajo multidisciplinar en el sitio arqueológico, que se centró principalmente en la Iglesia de San Esteban, con el propósito principal de mejorar el conocimiento y la documentación del estado de conservación del suelo con el mosaico policromado que cubre toda la superficie de la sala y el presbiterio. Se ha recopilado una gran cantidad de datos provenientes de investigaciones arqueológicas e históricas, inspecciones geofísicas y geodésicas y levantamientos geomáticos, que produjeron también una ortofoto verdadera del suelo con el mosaico. Los datos se han organizado en una geodatabase, facilitando el intercambio de información entre diferentes actores. Además, la gestión de los datos en un Sistema de Información Geográfica (SIG) dedicado, ha permitido un análisis profundo que facilita la comprensión de la evolución del repertorio iconográfico que, a lo largo de los siglos, ha sufrido varias desfiguraciones debido a la era iconoclasta. El conocimiento del mosaico también ha sido vital en la implementación de aplicaciones multimedia y en la creación de experiencias virtuales, en las que la información se puede transmitir y visualizar directamente en la reconstrucción virtual de todo el sitio arqueológico. La innovación del trabajo propuesto está, por lo tanto, en la gestión del flujo de datos que puede ser explotado por diferentes actores a través de diferentes plataformas: expertos, gracias al uso del SIG, y visitantes con el uso de las aplicaciones multimedia (como son la Realidad Aumentada (AR) o la visualización web de alta resolución) para fines de divulgación, con el fin de preservar este patrimonio incalculable de la humanidad.Malinverni, ES.; Pierdiccaa, R.; Di Stefano, F.; Gabrielli, R.; Albiero, A. (2019). Museo virtual enriquecido con datos GIS para compartir ciencia y cultura. La Iglesia de San Esteban en Umm er-Rasas (Jordania). Virtual Archaeology Review. 10(21):31-39. https://doi.org/10.4995/var.2019.11919SWORD31391021Anichini, F., Bini, D., Bini, M., Dubbini, N., Fabiani, F., Gattiglia, G., ... Steffè, S. (2012). MAPPAproject: Methodologies applied to archaeological potential predictivity. MapPapers, 1en-I, 23-43.Anichini, F., Fabiani, F., Gattiglia, G., & Gualandi, M. L. (2012). A database for archaeological data recording and analysis. MapPapers, 1en-II, 21-38.Baik, A., Yaagoubi, R., & Boehm, J. (2015). Integration of Jeddah historical BIM and 3D GIS for documentation and restoration of historical monument. International Society for Photogrammetry and Remote Sensing, XL-5/W7, 29-34. https://doi.org/10.5194/isprsarchives-XL-5-W7-29-2015Barrile, V., Fotia, A., Bilotta, G., & De Carlo, D. (2019). Integration of geomatics methodologies and creation of a cultural heritage app using augmented reality. Virtual Archaeology Review, 10(20), 40-51. https://doi.org/10.4995/var.2019.10361Blanco-Pons, S., Carrión-Ruiz, B., Lerma, J. L., & Villaverde, V. (2019). Design and implementation of an augmented reality application for rock art visualization in Cova dels Cavalls (Spain). Journal of Cultural Heritage. https://doi.org/10.1016/j.culher.2019.03.014Bruno, F., Bruno, S., De Sensi, G., Luchi, M. L., Mancuso, S., & Muzzupappa, M. (2010). From 3D reconstruction to virtual reality: A complete methodology for digital archaeological exhibition. Journal of Cultural Heritage, 11(1), 42-49. https://doi.org/10.1016/j.culher.2009.02.006Colosi, F., Fangi, G., Gabrielli, R., Orazi, R., Angelini, A., & Bozzi, C. A. (2009). Planning the Archaeological Park of Chan Chan (Peru) by means of satellite images, GIS and photogrammetry. Journal of Cultural Heritage, 10 (SUPPL. 1), 27-34. https://doi.org/10.1016/j.culher.2009.08.002d'Annibale, E., Tassetti, A. N., & Malinverni, E. S. (2014). Finalizing a low-cost photogrammetric workflow: from panoramic photos to Heritage 3D documentation and visualization. International Journal of Heritage in the Digital Era, 3(1), 33-49. https://doi.org/10.1260/2047-4970.3.1.33Dilek, A. P. S. E., Doğan, M., & Kozbe, G. (2019). The Influences of the Interactive Systems on Museum Visitors' Experience: A Comparative Study from Turkey. Journal of Tourism Intelligence and Smartness, 2(1), 27-38. Retrieved from http://dergipark.org.tr/jtis/issue/44975/559246Felicetti, A., Albiero, A., Gabrielli, R., Pierdicca, R., Paolanti, M., Zingaretti, P.,& Malinverni, E. S. (2018). Automatic Mosaic Digitalization: a Deep Learning approach to tessera segmentation. In METROARCHEO, IEEE International Conference on Metrology for Archaeology and Cultural Heritage. Cassino.Gabrielli, R., Portarena, D., & Franceschinis, M. (2017). Tecniche di documentazione dei tappeti musivi del sito archeologico di Umm Al-Rasas-Kastron Mefaa (Giordania). Archeologia e Calcolatori, 28(1), 201-218.Gabrielli, R., & Greco, G. (2018). Umm Ar-Rasas: The Application of Integrated Methodologies for the Valorization of a Unesco Site. Global Journal of Archaeology & Anthropology, 6(3), 555688. https://doi.org/10.19080/GJAA.2018.06.555688Han, D.-I. D., Weber, J., Bastiaansen, M., Mitas, O., & Lub, X. (2019). Virtual and augmented reality technologies to enhance the visitor experience in cultural tourism. In M. 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Evaluating Augmented and Virtual Reality in Education Through a User-Centered Comparative Study

none5Augmented and virtual reality proved to be valuable solutions to convey contents in a more appealing and interac- tive way. Given the improvement of mobile and smart devices in terms of both usability and computational power, contents can be easily conveyed with a realism level never reached in the past. Despite the tremendous number of researches related with the presentation of new fascinating applications of ancient goods and artifacts augmenta- tion, few papers are focusing on the real effect these tools have on learning. Within the framework of SmartMarca project, this chapter focuses on assessing the potential of AR/VR applications specifically designed for cultural heritage. Tests have been conducted on classrooms of teenagers to whom different learning approaches served as an evaluation method about the effectiveness of using these technologies for the education process. The chapter argues on the necessity of developing new tools to enable users to become producers of contents of AR/VR experiences.openPierdicca, Roberto; Frontoni, Emanuele; Puggioni, Maria Paola; Malinverni, Eva Savina; Paolanti, MarinaPierdicca, Roberto; Frontoni, Emanuele; Puggioni, Maria Paola; Malinverni, Eva Savina; Paolanti, Marin

Identifying the use of a park based on clusters of visitors\u27 movements from mobile phone data

Planning urban parks is a burdensome task, requiring knowledge of countless variables that are impossible to consider all at the same time. One of these variables is the set of people who use the parks. Despite information and communication technologies being a valuable source of data, a standardized method which enables landscape planners to use such information to design urban parks is still broadly missing. The objective of this study is to design an approach that can identify how an urban green park is used by its visitors in order to provide planners and the managing authorities with a standardized method. The investigation was conducted by exploiting tracking data from an existing mobile application developed for Cardeto Park, an urban green area in the heart of the old town of Ancona, Italy. A trajectory clustering algorithm is used to infer the most common trajectories of visitors, exploiting global positioning system and sensor-based tracks. The data used are made publicly available in an open dataset, which is the first one based on real data in this field. On the basis of these user-generated data, the proposed data-driven approach can determine the mission of the park by processing visitors\u27 trajectories whilst using a mobile application specifically designed for this purpose. The reliability of the clustering method has also been confirmed by an additional statistical analysis. This investigation reveals other important user behavioral patterns or trends

Deep learning for semantic segmentation of 3D point cloud.

Cultural Heritage is a testimony of past human activity, and, as such, its objects exhibit great variety in their nature, size and complexity; from small artefacts and museum items to cultural landscapes, from historical building and ancient monuments to city centers and archaeological sites. Cultural Heritage around the globe suffers from wars, natural disasters and human negligence. The importance of digital documentation is well recognized and there is an increasing pressure to document our heritage both nationally and internationally. For this reason, the three-dimensional scanning and modeling of sites and artifacts of cultural heritage have remarkably increased in recent years. The semantic segmentation of point clouds is an essential step of the entire pipeline; in fact, it allows to decompose complex architectures in single elements, which are then enriched with meaningful information within Building Information Modelling software. Notwithstanding, this step is very time consuming and completely entrusted on the manual work of domain experts, far from being automatized. This work describes a method to label and cluster automatically a point cloud based on a supervised Deep Learning approach, using a state-of-the-art Neural Network called PointNet++. Despite other methods are known, we have choose PointNet++ as it reached significant results for classifying and segmenting 3D point clouds. PointNet++ has been tested and improved, by training the network with annotated point clouds coming from a real survey and to evaluate how performance changes according to the input training data. It can result of great interest for the research community dealing with the point cloud semantic segmentation, since it makes public a labelled dataset of CH elements for further tests

A connectivist approach to smart city learning : Valletta city case-study

A connectivist approach will be adopted to design and evaluate learning in technology-enhanced open spaces in Valletta city. Learning is considered as a process of creating connections between learner’s inner cognitive and affective systems with the external physical and social worlds. These interactions are organised within a model comprising dimensions and levels of interactions. The experience for a learner in a technology-enhanced historical place will be designed considering interactions with the content domain (history, botany, art), the technological dimension (interaction between handheld devices and the available signals such as 3/4G, Wifi or GNSS) and the social dimension comprising interactions with fellow learners /citizens and domain experts. The levels of interactions are related to learner’s experience within the subject domain, with technology and one’s status or role in learning community or community of practice. Thus learning experiences have to be designed considering acquisition level for novice learners, participatory learning for more experience learners and contributory learning for highly competent learners. This connectivist model will be applied to identified places of historical or educational interest in Valletta city to design different modes of learning mediated through interactive technologies. The concept of Personal Learning Environments in Smart cities [1] will be used to provide technology-enhanced experiences in Playful learning, Seamless learning, Geo-learning, Citizen enquiry and Crowd learning. A number of these technology-enhanced learning experiences, developed in collaboration with CYBERPARKS ACTION’s WG1, will be contextualized in Valletta city. University of Malta will provide the domain content and resources, together with the pedagogical strategy for each learning experience. Researchers from WG1 will design and develop the technological model and infrastructure, mainly the Android-based Way-Cyberparks App that will integrate GNSS-based learning, Augmented Reality, Navigation tracing and other functionalities used for specific tasks and type of data collection. An interactions-based methodology will be used to evaluate learning along the identified dimensions.Funded by the Horizon 2020 Framework Programme of the European Union.peer-reviewe