Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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
ORCID logo ,
Vasiliki Lysandrou
,
Georgios Artopoulos
,
Paris Fokaides
ORCID logo ,
Charalambos Chrysostomou
Version 1 : Received: 25 February 2023 / Approved: 27 February 2023 / Online: 27 February 2023 (03:24:59 CET)

A peer-reviewed article of this Preprint also exists.

Tzima, M.S.; Agapiou, A.; Lysandrou, V.; Artopoulos, G.; Fokaides, P.; Chrysostomou, C. An Application of Machine Learning Algorithms by Synergetic Use of SAR and Optical Data for Monitoring Historic Clusters in Cypriot Cities. Energies 2023, 16, 3461. Tzima, M.S.; Agapiou, A.; Lysandrou, V.; Artopoulos, G.; Fokaides, P.; Chrysostomou, C. An Application of Machine Learning Algorithms by Synergetic Use of SAR and Optical Data for Monitoring Historic Clusters in Cypriot Cities. Energies 2023, 16, 3461.

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 bridge technological innovation with conserving and restoring heritage buildings. The paper presents 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-discipline study of heritage buildings towards the creation of 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

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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