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Abandoned industrial sites are generally characterized by soil and subsoil contamination. The paradigm currently employed for their remediation is “tabula rasa”, i.e., remediation of the entire site before its repurpose. However, this method is not economically, socially, or technologically sustainable: it delays the reuse of large areas, often well-connected to infrastructures, whose reuse may prevent further soil consumption. A possible solution to this problem is the application of adaptive reuse principles. This study, conducted at FULL (Future Urban Legacy Lab) in Politecnico di Torino, presents an interdisciplinary approach to spatialize, visualize, and manage interactions between reclamation and urban design for the transformation of contaminated urban areas. The core is based on a decision support parametric toolkit, named AdRem, developed to compare available remediation techniques and schematic urban design solutions. AdRem uses a 3D modeling interface and VPL scripting. Required input data are a geometric description of the site, data on the contamination status, viable remediation techniques, and associated features, and schematic urban design recommendations. A filtering process selects the techniques compatible with the site use foreseen. The output is an optimized remediation and reuse plan that can support an interdisciplinary discussion on possible site regeneration options.
Valerio Palma; Federico Accorsi; Alessandro Casasso; Carlo Bianco; Sarah Cutrì; Matteo Robiglio; Tiziana Tosco. AdRem: An Integrated Approach for Adaptive Remediation. Sustainability 2020, 13, 28 .
AMA StyleValerio Palma, Federico Accorsi, Alessandro Casasso, Carlo Bianco, Sarah Cutrì, Matteo Robiglio, Tiziana Tosco. AdRem: An Integrated Approach for Adaptive Remediation. Sustainability. 2020; 13 (1):28.
Chicago/Turabian StyleValerio Palma; Federico Accorsi; Alessandro Casasso; Carlo Bianco; Sarah Cutrì; Matteo Robiglio; Tiziana Tosco. 2020. "AdRem: An Integrated Approach for Adaptive Remediation." Sustainability 13, no. 1: 28.
This paper presents the most recent developments in a project aimed to the documentation, storage and dissemination of the cultural heritage. The subject of the project are more than 70 Baroque atria in Turin, recognized by critics for their particular unitary vaulted systems Our research team is currently working on digitizing documents and studying ways to enhance and share these results through ICT. In particular, we want to explore possibilities for recognizing and tracing three-dimensional objects in augmented reality (AR) applications connected to the collected data. Recent developments in this field relate to the technology available on widespread mobile devices such as tablets and smartphones, allowing for real-time 3D scanning. Using software prototypes, we want to introduce some problems involved in integrating this technology into digital archives.
V. Palma; R. Spallone; M. Vitali. AUGMENTED TURIN BAROQUE ATRIA: AR EXPERIENCES FOR ENHANCING CULTURAL HERITAGE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-2/W9, 557 -564.
AMA StyleV. Palma, R. Spallone, M. Vitali. AUGMENTED TURIN BAROQUE ATRIA: AR EXPERIENCES FOR ENHANCING CULTURAL HERITAGE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-2/W9 ():557-564.
Chicago/Turabian StyleV. Palma; R. Spallone; M. Vitali. 2019. "AUGMENTED TURIN BAROQUE ATRIA: AR EXPERIENCES FOR ENHANCING CULTURAL HERITAGE." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W9, no. : 557-564.
In recent years, the diffusion of large image datasets and an unprecedented computational power have boosted the development of a class of artificial intelligence (AI) algorithms referred to as deep learning (DL). Among DL methods, convolutional neural networks (CNNs) have proven particularly effective in computer vision, finding applications in many disciplines. This paper introduces a project aimed at studying CNN techniques in the field of architectural heritage, a still to be developed research stream. The first steps and results in the development of a mobile app to recognize monuments are discussed. While AI is just beginning to interact with the built environment through mobile devices, heritage technologies have long been producing and exploring digital models and spatial archives. The interaction between DL algorithms and state-of-the-art information modeling is addressed, as an opportunity to both exploit heritage collections and optimize new object recognition techniques.
V. Palma. TOWARDS DEEP LEARNING FOR ARCHITECTURE: A MONUMENT RECOGNITION MOBILE APP. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-2/W9, 551 -556.
AMA StyleV. Palma. TOWARDS DEEP LEARNING FOR ARCHITECTURE: A MONUMENT RECOGNITION MOBILE APP. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-2/W9 ():551-556.
Chicago/Turabian StyleV. Palma. 2019. "TOWARDS DEEP LEARNING FOR ARCHITECTURE: A MONUMENT RECOGNITION MOBILE APP." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W9, no. : 551-556.
This contribution focuses on the relationship between informative models, databases and innovative visualization tools, as crucial inputs for the knowledge and management of cultural heritage and for the interaction with the design process. Along with the ever-growing development of survey, storage and analysis technologies, upgrading the representation tools can support the interpretation and processing of the new available information assets. The Tu-CULT research project has the purpose to acquire, store, process and disseminate data regarding the monuments of the city of Padua, studying how the information and communication technologies (ICT) contribute to the management of the architectural heritage and to the design of transformational scenarios involving the monuments. Specifically, we address the issues of digital survey and archiving, 3D information modelling, and multimedia visualization for a multi-level access to the collected documents and the developed scenarios.
Cristina Cecchini; Maria Rosaria Cundari; Valerio Palma; Federico Panarotto. Data, Models and Visualization: Connected Tools to Enhance the Fruition of the Architectural Heritage in the City of Padova. Graphic Imprints 2018, 633 -646.
AMA StyleCristina Cecchini, Maria Rosaria Cundari, Valerio Palma, Federico Panarotto. Data, Models and Visualization: Connected Tools to Enhance the Fruition of the Architectural Heritage in the City of Padova. Graphic Imprints. 2018; ():633-646.
Chicago/Turabian StyleCristina Cecchini; Maria Rosaria Cundari; Valerio Palma; Federico Panarotto. 2018. "Data, Models and Visualization: Connected Tools to Enhance the Fruition of the Architectural Heritage in the City of Padova." Graphic Imprints , no. : 633-646.