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Purpose The purpose of this paper is to describe in-depth a community-based social partnership, emerged in response to the financial crisis in Greece, with members from the private, public and civic sectors, using a case example of a grass-root self-organised national network. Design/methodology/approach Formal and informal interviews as well as written communication with members of the partnership mainly formed the basis for the analysis. Topics covered formation and implementation activities, outcomes, relationship issues, such as trust and links to social capital. Findings A shared community risk and a national media campaign to increase public awareness of the issue were catalysts for individuals’ sensitisation and participation in the partnership. The shared risk was the loss of community’s social cohesion, through poverty aggravated by the financial crisis. Self-organisation led to innovative relationships, whereas trust, collective action and collaboration show social capital attributes in the partnership enabling resilience development. Research limitations/implications The research contributes in the fields of community-based partnerships and engagement in building community and crisis resilience. The findings are based on a case example. More evidence is needed in order to derive generalised statements about the partnership’s contribution to crisis resilience. Practical implications The partnership has shown impact on community engagement, health and well-being. Originality/value This paper presents a partnership type for building community and crisis resilience with the case example of one such partnership in Greece, formed to alleviate community distress caused by the crisis.

Recent years include the world’s hottest year, while they have been marked mainly, besides the COVID-19 pandemic, by climate-related disasters, based on data collected by the Emergency Events Database (EM-DAT). Besides the human losses, disasters cause significant and often catastrophic socioeconomic impacts, including economic losses. Recent developments in artificial intelligence (AI) and especially in machine learning (ML) and deep learning (DL) have been used to better cope with the severe and often catastrophic impacts of disasters. This paper aims to provide an overview of the research studies, presented since 2017, focusing on ML and DL developed methods for disaster management. In particular, focus has been given on studies in the areas of disaster and hazard prediction, risk and vulnerability assessment, disaster detection, early warning systems, disaster monitoring, damage assessment and post-disaster response as well as cases studies. Furthermore, some recently developed ML and DL applications for disaster management have been analyzed. A discussion of the findings is provided as well as directions for further research.