Reconstruction of Historical Buildings Using Social Media Data: A Case Study from the World Cultural Heritage List

Nursu Tunalıoğlu; Bahattin Erdoğan; Taylan Öcalan; Duygu Arıcan; Cemali Altuntaş; Tümay Arda; Ali Hasan Doğan; Elif Zeynep Arıkan

Makale detayları

Reconstruction of Historical Buildings Using Social Media Data: A Case Study from the World Cultural Heritage List

Yazarlar:

Nursu Tunalıoğlu | Bahattin Erdoğan | Taylan Öcalan | Duygu Arıcan | Cemali Altuntaş | Tümay Arda | Ali Hasan Doğan | Elif Zeynep Arıkan

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Photogrammetry and Remote Sensing

Dergi:

International Journal of Engineering and Geosciences Vol.11 Issue 1, pp.21-39

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356279

pISSN:

2548-0960

Anahtar kelimeler:

Özet:

 Cultural and natural heritage assets, as a set of tangible and intangible values that reveal the shared past and historical accumulations of the communities living together, are important not only for the past but also for the transfer to future generations and are the responsibility of all nations of the world. Todays digitalized information age, development, and change in science and technology contribute to producing highly accurate three-dimensional 3D inventories of these cultural heritages. However, in addition to natural degradation and destruction processes, unexpected events such as war, terrorist attacks, and natural disasters can hinder the formulation of the traditional inventories. At this point, it is important to carry out 3D modeling studies using crowdsourced images and videos from social media, to enhance modeling accuracy and support digital documentation, virtual museum initiatives, and heritage preservation. This study aims to present a strategy following the Structure-from-Motion approach to create 3D models of cultural heritage assets by using shared crowdsourced images and videos collected via social media platforms and applying a normalization procedure for scale standardization to assess model accuracy. Data obtained during nighttime, snowy conditions, or overly filtered captures were excluded, and a segmentation procedure using the Segment Anything model was implemented to remove irrelevant objects. As a result, an approach that can be used in inventory studies has been presented by using images and videos shared by users on social media platforms through an integrated and mutually supportive methodology. Quantitative analysis using the M3C2 method showed that RMSE values ranged from 0.0010 to 0.0036 across the models, with over 93 of the matched points falling within 1σ. These results confirm the reliability of the proposed approach for large-scale digital heritage documentation.

 Cultural and natural heritage assets, as a set of tangible and intangible values that reveal the shared past and historical accumulations of the communities living together, are important not only for the past but also for the transfer to future generations and are the responsibility of all nations of the world. Todays digitalized information age, development, and change in science and technology contribute to producing highly accurate three-dimensional 3D inventories of these cultural heritages. However, in addition to natural degradation and destruction processes, unexpected events such as war, terrorist attacks, and natural disasters can hinder the formulation of the traditional inventories. At this point, it is important to carry out 3D modeling studies using crowdsourced images and videos from social media, to enhance modeling accuracy and support digital documentation, virtual museum initiatives, and heritage preservation. This study aims to present a strategy following the Structure-from-Motion approach to create 3D models of cultural heritage assets by using shared crowdsourced images and videos collected via social media platforms and applying a normalization procedure for scale standardization to assess model accuracy. Data obtained during nighttime, snowy conditions, or overly filtered captures were excluded, and a segmentation procedure using the Segment Anything model was implemented to remove irrelevant objects. As a result, an approach that can be used in inventory studies has been presented by using images and videos shared by users on social media platforms through an integrated and mutually supportive methodology. Quantitative analysis using the M3C2 method showed that RMSE values ranged from 0.0010 to 0.0036 across the models, with over 93 of the matched points falling within 1σ. These results confirm the reliability of the proposed approach for large-scale digital heritage documentation.

 Cultural and natural heritage assets, as a set of tangible and intangible values that reveal the shared past and historical accumulations of the communities living together, are important not only for the past but also for the transfer to future generations and are the responsibility of all nations of the world. Todays digitalized information age, development, and change in science and technology contribute to producing highly accurate three-dimensional 3D inventories of these cultural heritages. However, in addition to natural degradation and destruction processes, unexpected events such as war, terrorist attacks, and natural disasters can hinder the formulation of the traditional inventories. At this point, it is important to carry out 3D modeling studies using crowdsourced images and videos from social media, to enhance modeling accuracy and support digital documentation, virtual museum initiatives, and heritage preservation. This study aims to present a strategy following the Structure-from-Motion approach to create 3D models of cultural heritage assets by using shared crowdsourced images and videos collected via social media platforms and applying a normalization procedure for scale standardization to assess model accuracy. Data obtained during nighttime, snowy conditions, or overly filtered captures were excluded, and a segmentation procedure using the Segment Anything model was implemented to remove irrelevant objects. As a result, an approach that can be used in inventory studies has been presented by using images and videos shared by users on social media platforms through an integrated and mutually supportive methodology. Quantitative analysis using the M3C2 method showed that RMSE values ranged from 0.0010 to 0.0036 across the models, with over 93 of the matched points falling within 1σ. These results confirm the reliability of the proposed approach for large-scale digital heritage documentation.

 Cultural and natural heritage assets, as a set of tangible and intangible values that reveal the shared past and historical accumulations of the communities living together, are important not only for the past but also for the transfer to future generations and are the responsibility of all nations of the world. Todays digitalized information age, development, and change in science and technology contribute to producing highly accurate three-dimensional 3D inventories of these cultural heritages. However, in addition to natural degradation and destruction processes, unexpected events such as war, terrorist attacks, and natural disasters can hinder the formulation of the traditional inventories. At this point, it is important to carry out 3D modeling studies using crowdsourced images and videos from social media, to enhance modeling accuracy and support digital documentation, virtual museum initiatives, and heritage preservation. This study aims to present a strategy following the Structure-from-Motion approach to create 3D models of cultural heritage assets by using shared crowdsourced images and videos collected via social media platforms and applying a normalization procedure for scale standardization to assess model accuracy. Data obtained during nighttime, snowy conditions, or overly filtered captures were excluded, and a segmentation procedure using the Segment Anything model was implemented to remove irrelevant objects. As a result, an approach that can be used in inventory studies has been presented by using images and videos shared by users on social media platforms through an integrated and mutually supportive methodology. Quantitative analysis using the M3C2 method showed that RMSE values ranged from 0.0010 to 0.0036 across the models, with over 93 of the matched points falling within 1σ. These results confirm the reliability of the proposed approach for large-scale digital heritage documentation.

Makale detayları

Reconstruction of Historical Buildings Using Social Media Data: A Case Study from the World Cultural Heritage List

Yazarlar:

Nursu Tunalıoğlu | Bahattin Erdoğan | Taylan Öcalan | Duygu Arıcan | Cemali Altuntaş | Tümay Arda | Ali Hasan Doğan | Elif Zeynep Arıkan

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Photogrammetry and Remote Sensing

Dergi:

International Journal of Engineering and Geosciences Vol.11 Issue 1, pp.21-39

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356279

pISSN:

2548-0960

Crowd-sourced

Social Media Platforms

SfM

3D Model

Cultural Heritage

Sayı

Makale detayları

Reconstruction of Historical Buildings Using Social Media Data: A Case Study from the World Cultural Heritage List

Yazarlar: Nursu Tunalıoğlu | Bahattin Erdoğan | Taylan Öcalan | Duygu Arıcan | Cemali Altuntaş | Tümay Arda | Ali Hasan Doğan | Elif Zeynep Arıkan

Yayıncı: Murat YAKAR

Ana Başlıklar: Photogrammetry and Remote Sensing

Dergi: International Journal of Engineering and Geosciences Vol.11 Issue 1, pp.21-39

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001356279

pISSN: 2548-0960

Crowd-sourced

Social Media Platforms

SfM

3D Model

Cultural Heritage

Sayı

Yazarlar:

Nursu Tunalıoğlu | Bahattin Erdoğan | Taylan Öcalan | Duygu Arıcan | Cemali Altuntaş | Tümay Arda | Ali Hasan Doğan | Elif Zeynep Arıkan

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Photogrammetry and Remote Sensing

Dergi:

International Journal of Engineering and Geosciences Vol.11 Issue 1, pp.21-39

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356279

pISSN:

2548-0960