research-article

Detection of Disaster-Affected Cultural Heritage Sites from Social Media Images Using Deep Learning Techniques

Authors:

Muhammad Imran,

Carlos CastilloAuthors Info & Claims

Journal on Computing and Cultural Heritage (JOCCH), Volume 13, Issue 3

Article No.: 23, Pages 1 - 31

https://doi.org/10.1145/3383314

Published: 16 August 2020 Publication History

Abstract

This article describes a method for early detection of disaster-related damage to cultural heritage. It is based on data from social media, a timely and large-scale data source that is nevertheless quite noisy. First, we collect images posted on social media that may refer to a cultural heritage site. Then, we automatically categorize these images according to two dimensions: whether they are indeed a photo in which a cultural heritage resource is the main subject, and whether they represent damage. Both categorizations are challenging image classification tasks, given the ambiguity of these visual categories; we tackle both tasks using a convolutional neural network. We test our methodology on a large collection of thousands of images from the web and social media, which exhibit the diversity and noise that is typical of these sources, and contain buildings and other architectural elements, heritage and not-heritage, damaged by disasters as well as intact. Our results show that while the automatic classification is not perfect, it can greatly reduce the manual effort required to find photos of damaged cultural heritage by accurately detecting relevant candidates to be examined by a cultural heritage professional.

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Cited By

Silva COliveira L(2024)Artificial Intelligence at the Interface between Cultural Heritage and Photography: A Systematic Literature ReviewHeritage10.3390/heritage70701807:7(3799-3820)Online publication date: 17-Jul-2024
https://doi.org/10.3390/heritage7070180
Buragohain DMeng YDeng CLi QChaudhary S(2024)Digitalizing cultural heritage through metaverse applications: challenges, opportunities, and strategiesHeritage Science10.1186/s40494-024-01403-112:1Online publication date: 13-Aug-2024
https://doi.org/10.1186/s40494-024-01403-1
Alam FAlam TOfli FImran M(2024)Robust Training of Social Media Image Classification ModelsIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.323083911:1(546-565)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2022.3230839
Show More Cited By

Index Terms

Detection of Disaster-Affected Cultural Heritage Sites from Social Media Images Using Deep Learning Techniques
1. Applied computing
  1. Arts and humanities
    1. Architecture (buildings)
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image Journal on Computing and Cultural Heritage

Journal on Computing and Cultural Heritage Volume 13, Issue 3

October 2020

211 pages

ISSN:1556-4673

EISSN:1556-4711

DOI:10.1145/3411173

Editor:
Franco Niccolucci
VAST-LAB at PIN, University of Florence, Italy

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Publication History

Published: 16 August 2020

Online AM: 07 May 2020

Accepted: 01 February 2020

Revised: 01 January 2020

Received: 01 September 2019

Published in JOCCH Volume 13, Issue 3

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Cited By

Silva COliveira L(2024)Artificial Intelligence at the Interface between Cultural Heritage and Photography: A Systematic Literature ReviewHeritage10.3390/heritage70701807:7(3799-3820)Online publication date: 17-Jul-2024
https://doi.org/10.3390/heritage7070180
Buragohain DMeng YDeng CLi QChaudhary S(2024)Digitalizing cultural heritage through metaverse applications: challenges, opportunities, and strategiesHeritage Science10.1186/s40494-024-01403-112:1Online publication date: 13-Aug-2024
https://doi.org/10.1186/s40494-024-01403-1
Alam FAlam TOfli FImran M(2024)Robust Training of Social Media Image Classification ModelsIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.323083911:1(546-565)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2022.3230839
Mishra MLourenço P(2024)Artificial intelligence-assisted visual inspection for cultural heritage: State-of-the-art reviewJournal of Cultural Heritage10.1016/j.culher.2024.01.00566(536-550)Online publication date: Mar-2024
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