An Application of Machine Learning Algorithms by Synergetic Use of SAR and Optical Data for Monitoring Historic Clusters in Cypriot Cities

Maria Spyridoula Tzima (), Athos Agapiou, Vasiliki Lysandrou, Georgios Artopoulos (), Paris Fokaides and Charalambos Chrysostomou
Additional contact information
Maria Spyridoula Tzima: Computation-Based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus
Athos Agapiou: Earth Observation Cultural Heritage Research Lab, Department of Civil Engineering & Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus
Vasiliki Lysandrou: Earth Observation Cultural Heritage Research Lab, Department of Civil Engineering & Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus
Georgios Artopoulos: Science and Technology in Archaeology and Culture Research Center, The Cyprus Institute, Nicosia 2121, Cyprus
Paris Fokaides: School of Engineering, Frederick University, Nicosia 1036, Cyprus
Charalambos Chrysostomou: Computation-Based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus

Energies, 2023, vol. 16, issue 8, 1-20

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.

Keywords: machine learning; remote sensing; Sentinel-1; Sentinel-2; SNAP; land cover classification; change detection; urban heritage; historic architecture clusters (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/8/3461/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/8/3461/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:8:p:3461-:d:1124006

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().