Automation of urban technological census. The historical centre of Bethlehem
DOI:
https://doi.org/10.19229/2464-9309/12162022Keywords:
photogrammetry, Bethlehem, urban monitoring, object recognition, architectural censusAbstract
The research proposal reports the outcomes of a research track concerning the automation of the architectural census of technological elements in urban environments, aiming at the development of a monitoring and management system for the built heritage. The proposal is focused on a set of specific elements (water tanks) stacked on the coverings of the historical centre of Bethlehem and leverages close-range photogrammetric acquisitions to train Deep Learning models. The model lifecycle management, from training to prediction and deployment, as well as the storage of both image data and metadata, is performed through the scalability of a Cloud enterprise architecture. Periodical scheduled monitoring enables comparisons across different periods, allowing the detection of modifications, removals, and additions, therefore identifying the insurgence of potential criticalities. The goal of the project is the definition of a protocol to automate the identification of recurrent elements and monitor their evolution through time.
Downloads
Article Metrics Graph
References
Abueladas, A.-R. A., Niemi. T. M., Al-Zoubi, A., Tibor, G., Kanari, M. and Ben-Avraham, Z. (2021), “Liquefaction susceptibility maps for the Aqaba-Elat region with projections of future hazards with sea-level rise”, in Quarterly Journal of Engineering Geology and Hydrogeology, vol. 54, issue 2, pp. 1-12. [Online] Available at: lyellcollection.org/doi/10.1144/qjegh2020-039 [Accessed 30 September 2022].
Bocconcino, M. M. and Manzone, F. (2019), “Sistemi informativi e strumenti grafici per la manutenzione di manufatti complessi | Information systems and graphic tools for the maintenance of complex buildings”, in Garda, E., Mele, C. and Piantanida, P. (eds), Colloqui.AT.e 2019 – Ingegno e costruzione nell’epoca della complessità – Atti del Convegno – Torino, 25-28 Settembre 2019, Edizioni Politecnico di Torino, Torino, pp. 678-688. [Online] Available at: researchgate.net/publication/348334768_Sistemi_informativi_e_strumenti_ grafici_per_la_manutenzione_di_manufatti_complessi_-_Information_systems_and_graphic_tools_for_the_maintenance_of_complex_ buildings [Accessed 30 September 2022].
Buratti, G., Conte, S. and Rossi, M. (2021), “Artificial Intelligency, Big Data and Cultural Heritage”, in Giordano, A., Russo, M. and Spallone, R. (eds), Representation challenges – Augmented Reality and Artificial Intelligence in Cultural Heritage and Innovative Design Domain, FrancoAngeli, Milano, pp. 29-33. [Online] Available at: re.public.polimi.it/retrieve/e0c31c11-a30c-4599-e053-1705fe0aef77/Buratti_Conte_Rossi.pdf [Accessed 30 September 2022].
Carbone, C., Fawakherji, M., Trianni, V. and Nardi, D. (2022), “Photorealistic simulations of crop fields for remote Sensing with UAV swarms”, in Parrinello, S., Barba, S., Dell’Amico, A. and di Filippo, A. (eds), D-SITE. Drones – Systems of Information on Cultural Heritage for a spatial and social investigation, Pavia University Press, Pavia, pp. 576-581. [Online] Available at: paviauniversitypress.it/catalogo/d-site/6693 [Accessed 12 October 2022].
Corea, F. (2019), An Introduction to Data – Studies in Big Data, vol. 50, Springer, Cham. [Online] Available at: doi.org/10.1007/978-3-030-04468-8 [Accessed 30 September 2022].
Ćosović, M. and Janković, R. (2020), “CNN Classification of the Cultural Heritage Images”, in 2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH), IEEE, pp. 1-6. [Online] Available at: doi.org/10.1109/INFOTEH48170.2020.9066300 [Accessed 30 September 2022].
Cunha, H. S., Sclauser, B. S., Wildemberg, P. F., Fernandes, E. A. M., dos Santos, J. A., de Oliveira Lage, M., Lorenz, C., Barbosa, G. L., Quintanilha, J. A. and Chiaravalloti-Neto, F. (2021), “Water tank and swimming pool detection based on remote sensing and deep learning – Relationship with socioeconomic level and applications in dengue control”, in PLoS ONE, vol. 16, issue 12, pp. 1-24. [Online] Available at: doi.org/10.1371/journal.pone.0258681 [Accessed 12 October 2022].
Dabdoub Nasser, C. and Ḥifẓ al-Turāth al-Thaqāfī, M. (2005), Anatreh Quarter – An Urban and Architectural Study of a Bethlehem Quarter, Centre for Cultural Heritage Preservation, Bethlehem.
Dandabathula, G. B., Saini, S., Parikh, D., Sharma, P., Khandelwal, S. and Rao, S. S. (2019), “Automatic detection of overhead water tanks from satellite images using faster-RCNN”, in International Journal of Advanced Research in Computer Science, vol. 10, n. 5, pp. 34-37. [Online] Available at: dx.doi.org/10.26483/ijarcs.v10i5.6466 [Accessed 12 October 2022].
De Marco, R. (2022), “Dal modello informativo ad un repository sulla stabilità delle strutture urbane – Una riflessione sulla valutazione della sicurezza negli aggregati storici della città | From the information model to a repository on the stability of urban structures – A reflection on the evaluation of safety in the historical aggregates of the city”, in Parrinello, S. (ed.), 3D Bethlehem – Gestione e controllo della crescita urbana per lo sviluppo del patrimonio e miglioramento della vita nella città di Betlemme | Management and control of urban growth for the development of heritage and improvement of life in the city of Bethlehem, vol. 3, Edifir Edizioni, Firenze, pp. 203-224. [Online] Available at: researchgate.net/publication/361305496_Dal_modello_informativo_ad_un_ repository_sulla_stabilita_delle_strutture_urbane_Una_riflessione_sulla_ valutazione_della_sicurezza_negli_aggregati_storici_della_citta_From_the_ information_model_to_a_re [Accessed 30 September 2022].
Doria, E. (2022), “Strategie di strutturazione della conoscenza – Innovazioni e attività programmabili tramite il censimento tecnologico del centro storico di Betlemme | Knowledge structuring strategies – Innovations and programmable activities via the technological census of the historic center of Bethlehem”, in Parrinello, S. (ed.), 3D Bethlehem – Gestione e controllo della crescita urbana per lo sviluppo del patrimonio e miglioramento della vita nella città di Betlemme | Management and control of urban growth for the development of heritage and improvement of life in the city of Bethlehem, vol. 3, Edifir Edizioni, Firenze, pp. 51-67. [Online] Available at: researchgate.net/publication/361217094_Strategie_di_strutturazione_della_ conoscenza_innovazioni_e_attivita_programmabili_tramite_il_censimento_ tecnologico_del_centro_storico_di_Betlemme_Knowledge_structuring_ strategies _innovations_and_prog [Accessed 30 September 2022].
Due Trier, Ø., Reksten, J. H. and Løseth, K. (2021), “Automated mapping of cultural heritage in Norway from airborne lidar data using faster R-CNN”, in International Journal of Applied Earth Observation and Geoinformation, vol. 95, article 102241, pp. 1-19. [Online] Available at: doi.org/10.1016/j.jag.2020.102241 [Accessed 12 October 2022].
Gasparoli, P. (2012), “La manutenzione preventiva e programmata del patrimonio storico tutelato come prima forma di valorizzazione | Planned preventive maintenance in the preservation of historic heritage sites as a first form of valorisation”, in Techne | Journal of Technology for Architecture and Environment, vol. 03, pp. 148-157. [Online] Available at: doi.org/10.13128/Techne-10840 [Accessed 30 September 2022].
Hatir, M. E. and İnce, I. (2021), “Lithology mapping of stone heritage via state-of-the-art computer vision”, in Journal of Building Engineering, vol. 34, article 101921, pp. 1-12. [Online] Available at: doi.org/10.1016/j.jobe.2020.101921 [Accessed 12 October 2022].
He, X., Zhao, K. and Chu, X. (2021), “AutoML – A survey of the state-of-the-art”, in Knowledge-Based Systems, vol. 212, article 106622, pp. 1-27. [Online] Available at: doi.org/10.1016/j.knosys.2020.106622 [Accessed 30 September 2022].
Kouziokas, G. N. and Perakis, K. (2017), “Decision Support System Based on Artificial Intelligence, GIS and Remote Sensing for Sustainable Public and Judicial Management”, in European Journal of Sustainable Development, vol. 6, issue 3, pp. 397-404. [Online] Available at: doi.org/10.14207/ejsd.2017.v6n3p397 [Accessed 30 September 2022].
Lakshmanan, V. (2022), Data Science on the Google Cloud Platform, Editions O’Reilly, Paris.
Lecun, Y. and Bengio, Y. (1998), “Convolutional networks for Images, Speech, and Time-Series”, in Arbib, M. A. (ed.), The Handbook of Brain Theory and Neural Networks, MIT Press, pp. 255-258.
Li, K., Wan, G., Cheng, G., Meng, L. and Han, J. (2020), “Object detection in optical remote sensing images – A survey and a new benchmark”, in ISPRS Journal of Photogrammetry and Remote Sensing, vol. 159, pp. 296-307. [Online] Available at: doi.org/10.1016/j.isprsjprs.2019.11.023 [Accessed 12 October 2022].
Majchrowska, S., Mikołajczyk, A., Ferlin, M., Klawikowska, Z., Plantykow, M. A., Kwasigroch, A. and Majek, K. (2022), “Deep learning-based waste detection in natural and urban environments”, in Waste Management, vol. 138, pp. 274-284. [Online] Available at: doi.org/10.1016/j.wasman.2021.12.001 [Accessed 12 October 2022].
McCarthy, J. (2007), What is Artificial Intelligence?, Standford University, Standford, pp. 1-15. [Online] Available at: jmc.stanford.edu/articles/whatisai/whatisai.pdf [Accessed 30 September 2022].
Miceli, A., Morandotti, M. and Parrinello, S. (2020), “3D survey and semantic analysis for the documentation of built heritage – The case study of Palazzo Centrale of Pavia University”, in Vitruvio | International Journal of Architecture Technology and Sustainability, vol. 5, n. 1, pp. 65-80. [Online] Available at: doi.org/10.4995/vitruvio-ijats.2020.13634 [Accessed 30 September 2022].
Mishra, M. (2021), “Machine learning techniques for structural health monitoring of heritage buildings – A state-of-the-art review and case studies”, in Journal of Cultural Heritage, vol. 47, pp. 227-245. [Online] Available at: doi.org/10.1016/j.culher.2020.09.005 [Accessed 30 September 2022].
Monna, F., Rolland, T., Denaire, A., Navarro, N., Granjon, L., Barbé, R. and Chateau-Smith, C. (2021), “Deep learning to detect built cultural heritage from satellite imagery – Spatial distribution and size of vernacular houses in Sumba, Indonesia”, in Journal of Cultural Heritage, vol. 52, pp. 171-183. [Online] Available at: doi.org/10.1016/j.culher.2021.10.004 [Accessed 12 October 2022].
Montella, M. (2009), Valore e valorizzazione del patrimonio culturale storico, Mondadori Electa, Milano.
Parrinello, S. (ed.) (2019), 3D Bethlehem – Gestione e controllo della crescita urbana per lo sviluppo del patrimonio e miglioramento della vita nella città di Betlemme | Management and control of urban growth for the development of heritage and improvement of life in the city of Bethlehem, vol. 3, Edifir Edizioni, Firenze.
Parrinello, S. and Picchio, F. (2019), “Integration and comparison of close-range SfM methodologies for the analysis and the development of the historical city center of Bethlehem”, in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, vol. XLII-2/W9, pp. 589-595. [Online] Available at: doi.org/10.5194/isprs-archives-XLII-2-W9-589-2019 [Accessed 30 September 2022].
Picchio, F. (2020), “Acquisition protocols for UAV photogrammetric data – Comparison in methodological SfM procedures from architectural till urban scale”, in Barba, S., Dell’Amico, A., Limongello, M. and Parrinello, S. (eds), D-SITE – Drones-Systems of Information on culTural hEritage – For a spatial and social investigation, Pavia University Press, Pavia, pp. 70-79.
Picchio, F. (2019), “La fotogrammetria close range per la costituzione di modelli SfM affidabili sul sistema urbano”, in Parrinello, S. (ed.), 3D Bethlehem – Gestione e controllo della crescita urbana per lo sviluppo del patrimonio e miglioramento della vita nella città di Betlemme | Management and control of urban growth for the development of heritage and improvement of life in the city of Bethlehem, vol. 1, Edifir Edizioni, Firenze, pp. 119-138.
Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016), “You Only Look Once – Unified, Real-Time Object Detection”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – Las Vegas, NV, USA, June 27-30, 2016, IEEE Computer Society, pp. 779-788. [Online] Available at: cv-foundation.org/openaccess/content_cvpr_2016/papers/Redmon_You_Only_ Look_CVPR_2016_paper.pdf [Accessed 30 September 2022].
Rivera, J. D. D. S. (2020), “Object detection with a model trained in Google Cloud AutoML”, in Practical TensorFlow.js, Apress, Berkeley, pp. 164-184. [Online] Available at: doi.org/10.1007/978-1-4842-6273-3_7 [Accessed 30 September 2022].
Wang, X., Wang, H., Zhang, C., He, Q. and Huo, L. (2022), “A Sample Balance-Based Regression Module for Object Detection in Construction Sites”, in Applied Science, vol. 12, n. 13, article 6752, pp. 1-15. [Online] Available at: doi.org/10.3390/app12136752 [Accessed 12 October 2022].
Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S. and Lan, S. (2020), “A review of object detection based on deep learning”, in Multimedia Tools Application, vol. 79, pp. 23729-23791. [Online] Available at: doi.org/10.1007/s11042-020-08976-6 [Accessed 12 October 2022].
Zou, Z., Zhao, X., Zhao, P., Qi, F. and Wang, N. (2019), “CNN-based statistics and location estimation of missing components in routine inspection of historic buildings”, in Journal of Cultural Heritage, vol. 38, pp. 221-230. [Online] Available at: doi.org/10.1016/j.culher.2019.02.002 [Accessed 12 October 2022].
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2022 Elisabetta Doria
This work is licensed under a Creative Commons Attribution 4.0 International License.
This Journal is published under Creative Commons Attribution Licence 4.0 (CC-BY).
License scheme | Legal code
This License allows anyone to:
Share: copy and redistribute the material in any medium or format.
Adapt: remix, transform, and build upon the material for any purpose, even commercially.
Under the following terms
Attribution: Users must give appropriate credit, provide a link to the license, and indicate if changes were made; users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No additional restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices
Users do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give users all of the permissions necessary for their intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
