Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.

Natural hazards pose significant threats to different communities and various places around the world. Failing to identify and support the most vulnerable communities is a recipe for disaster. Many studies have proposed social vulnerability indices for measuring both the sensitivity of a population to natural hazards and its ability to respond and recover from them. Existing techniques, however, have not accounted for the unique strengths that exist within different communities to help minimize disaster loss. This study proposes a more balanced approach referred to as the strength-based social vulnerability index (SSVI). The proposed SSVI technique, which is built on sound sociopsychological theories of how people act during disasters and emergencies, is applied to assess comparatively the social vulnerability of different suburbs in the Wollongong area of New South Wales, Australia. The results highlight suburbs that are highly vulnerable, and demonstrates the usefulness of the technique in improving understanding of hotspots where limited resources should be judiciously allocated to help communities improve preparedness, response, and recovery from natural hazards.

The irreversible marriage between digital technology and physical urban infrastructure has given rise to the concept of smart infrastructure. The potential benefits of smart infrastructure are significant; however, their realisation will depend on society’s ability to address pressing issues, such as the need to develop a common language to describe terms and processes. This paper aims to lay out the foundations of such a common language. First, the authors review academic literature in order to outline key characteristics of so-called smart infrastructure systems. Importantly, the authors define and differentiate between smart and intelligent infrastructure systems. Then, the authors use an LVP framework to describe the levels (L), values (V) and principles (P) of inherently smart infrastructure systems. Finally, the authors argue that the study of smart infrastructure is a multidisciplinary field of research that reaches beyond traditional engineering and information technology disciplines. The authors expect that eliminating ambiguity and fragmentation in the definition of smart infrastructure systems will enhance research on and practice of these systems.

Migrants, ethnic minorities and people from culturally and linguistically diverse (CALD) communities are often more vulnerable to natural disasters due to cultural barriers and limited proficiency in the dominant language, which sometimes undermine their ability to access, interpret and respond to warnings. Technology can assist in engendering culturally and linguistically appropriate communication with CALD communities if key challenges are identified. This study contributes by reviewing relevant literature with the aim of ascertaining the most pressing challenges requiring technological interventions. Three broad issues (i.e., trust, message tailoring, and message translation) are identified and discussed, and potential solutions for addressing these issues are recommended.

Social media platforms have proved to be vital sources of information to support disaster response and recovery. A key issue, though, is that social media conversation about disasters tends to tail off after the immediate disaster response phase, potentially limiting the extent to which social media can be relied on to support recovery. This situation motivates the present study of social media usage patterns, including who contributes to social media around disaster recovery, which recovery activities they contribute to, and how well that participation is sustained over time. Utilising Twitter data from the 2019–20 Australian bushfires, we statistically examined the participation of different groups (citizens, emergency agencies, politicians and others) across categories of disaster recovery activity such as donations & financial support or mental health & emotional support, and observed variations over time. The results showed that user groups differed in how much they contributed on Twitter around different recovery activities, and their levels of participation varied with time. Recovery-related topics also varied significantly with time. These findings are valuable because they increase our understanding of which aspects of disaster recovery currently benefit most from social media and which are relatively neglected, indicating where to focus resources and recovery effort.