Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/30036
Title: | An Application of Machine Learning Algorithms by Synergetic Use of SAR and Optical Data for Monitoring Historic Clusters in Cypriot Cities | Authors: | Tzima, Maria Spyridoula Agapiou, Athos Lysandrou, Vasiliki Artopoulos, Georgios Fokaides, Paris Chrysostomou, Charalambos |
Major Field of Science: | Engineering and Technology | Field Category: | Civil Engineering | Keywords: | change detection;historic architecture clusters;land cover classification;machine learning;remote sensing;Sentinel-1;Sentinel-2;SNAP;urban heritage | Issue Date: | 1-Apr-2023 | Source: | Energies, 2023, vol. 16, iss. 8 | Volume: | 16 | Issue: | 8 | Journal: | Energies | Abstract: | In an era of rapid technological improvements, state-of-the-art methodologies and tools dedicated to protecting and promoting our cultural heritage should be developed and extensively employed in the contemporary built environment and lifestyle. At the same time, sustainability principles underline the importance of the continuous use of historic or vernacular buildings as part of the building stock of our society. Adopting a holistic, integrated, multi-disciplinary strategy can link technological innovation with the conservation and restoration of heritage buildings. This paper presents the ongoing research and results of the application of Machine Learning methods for the remote monitoring of the built environment of the historic cluster in Cypriot cities. This study is part of an integrated, multi-scale, and multi-disciplinary study of heritage buildings, with the end goal of creating an online HBIM platform for urban monitoring. | URI: | https://hdl.handle.net/20.500.14279/30036 | ISSN: | 19961073 | DOI: | 10.3390/en16083461 | Rights: | © by the authors Attribution-NonCommercial-NoDerivatives 4.0 International |
Type: | Article | Affiliation : | The Cyprus Institute Cyprus University of Technology Frederick University |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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energies-16-03461.pdf | Full text | 14.15 MB | Adobe PDF | View/Open |
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