The field of crisis and disaster studies has proliferated over the past two decades. Attention is bound to grow further as the world negotiates the prolonged challenges of the Covid-19 pandemic. In this review, we provide an overview of the main foci, methods, and research designs employed in the crisis and disaster research fields in the period of 2001-2020. The review documents that the focus and methods used have not changed much over time. Single case studies and exploratory research prevail, the focus has shifted from preparedness to response, and methodological diversity is limited, but gradually increasing. Future challenges are to understand transboundary crisis management and creeping crises. Advancing the field calls for our community to put more effort in drawing lessons beyond the single case to uncover comparable and universal patterns that connect between events or phases, which help to theorize the multifaceted nature of crisis and disaster management.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rhc3.12244

A systematic review of 20 years of crisis and disaster research: Trends and progress

Accurate and timely estimation of incurred damages is a critical component of effective disaster management, usually performed by trained inspectors and experts. The limitations in resources and workforce can hinder the timely acquisition of critical information and make the process costly. Crowdsourcing and participatory disaster damage assessment have emerged as a possible solution to address this challenge. However, such approaches generally suffer from a lack of reliability. This research improves the effectiveness of crowdsourcing in post-disaster damage assessment by enhancing the content and reliability of information gathered through public participation. The paper presents a novel framework for quantification and reduction of uncertainty in the outcome of participatory damage assessment. First, to reduce the complexity and subjectivity, the classification of overall damage state is decomposed into more straightforward microtasks in the form of a questionnaire survey. A decision rule is implemented to infer the damage state of buildings from the participant responses. Second, an information-theoretic model based on a maximum a posteriori probability estimation is presented for obtaining an accurate probabilistic description of the inferred damage states while quantifying and accounting for the reliability of the citizen participants as well as the relative ambiguity of images. A pilot study is presented by involving 70 non-expert citizen participants to assess the post-disaster imagery of 60 buildings collected following Hurricane Harvey. A comparison of the outcome with the available expert labels shows relatively high accuracy. The proposed model also outperforms the common majority-vote approach, especially as the number of unreliable participants increases.

An uncertainty-aware framework for reliable disaster damage assessment via crowdsourcing

This article introduces the Special Issue on Risk Protection for Cultural Heritage and Historic Centres. The article starts by reviewing the gaps in knowledge and practice related to disaster risk management of cultural heritage. It then reviews the contributions to the Special Issue, focussing on their multi-disciplinary findings that address some of the referred gaps. Finally, the article proceeds to discuss, in more detail, certain topics related to disaster risk management and cultural heritage that should be targeted by future research, emphasizing specific issues that need to be addressed. 

Risk protection for cultural heritage and historic centres: Current knowledge and further research needs

This article introduces the Special Issue on Risk Protection for Cultural Heritage and Historic Centres. The article starts by reviewing the gaps in knowledge and practice related to disaster risk management of cultural heritage. It then reviews the contributions to the Special Issue, focussing on their multi-disciplinary findings that address some of the referred gaps. Finally, the article proceeds to discuss, in more detail, certain topics related to disaster risk management and cultural heritage that should be targeted by future research, emphasizing specific issues that need to be addressed.

A different type of disaster response digital volunteer: Looking behind the scenes to reveal coordinating actions of disaster knowledge workers

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.

/pdf/detection-of-disaster-affected-cultural-heritage-sites-from-wc7h6zyu06.pdf

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

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1468-5973.12333

Twitter, disasters and cultural heritage: A case study of the 2015 Nepal earthquake

Illustrating the application of crowdsourcing in disaster response before the Internet age, this paper addresses two key questions: How did the people respond to the cultural heritage damaged during the 1966 Florence Flood? How were they motivated to do so? Content analysis of 180 out of 753 correspondence items from the archives of Fondazione Centro Studi Sull’Arte Licia e Carlo Ludovico Ragghianti in Lucca, Italy shows that the committee received contributions in the form of money, materials, volunteers and knowledge from different parts of the world. The most popular of all contributions, however, was money. Four main factors were found to be motivating people to contribute: 1) the call to participate, 2) the media, 3) influencers, and 4) memory of the city. Of key importance, this paper emphasizes: how to initiate a crowdsourcing campaign to restore cultural heritage, who will contribute or is most likely to contribute and how to motivate people to contribute.

Pakhee Kumar

Papers

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

Twitter, disasters and cultural heritage: A case study of the 2015 Nepal earthquake

Crowdsourcing to rescue cultural heritage during disasters: A case study of the 1966 Florence Flood

Connecting characteristics of social media activities of a heritage organisation to audience engagement

Art Lovers of the World Unite to Aid Florence: Techniques to Motivate the Crowd in Post-Disaster Heritage Crowdsourcing’