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Lessons from Haiti: applying innovative, affordable, and replicable solutions using drones and spatial data Format FRANCESCO VAROTTO, GIOVANNI MICHELE TOGLIA & CLAUDIA SOTO In 2016, Category 4 Hurricane Matthew made landfall in the south of Haiti, causing destruction unseen in more than a decade during the hurricane season, with winds as high as 230 kilometers per hour and 600 millimeters of rain in less than 24 hours. The toll was very high, with a total of 546 victims, 128 missing, 439 injured, and 2.1 million people affected. Following the disaster, the Government of Haiti (GoH) was confro...

Article, 08 December 2020 This case study examines the deliberative and participatory processes used by Slum Dwellers International (SDI), focusing on two affiliated federations in Kenya and Malawi. It describes the ways in which participatory mechanisms have allowed SDI federations to self-organise and engage with policymakers at local, national and global level to respond to challenges, including COVID-19. It also reflects on the use of digital tools to strengthen community mobilisation, community data generation and learning processes. Data collection in Lilongwe at Kawale settlement, Mal...

Lessons from Haiti: applying innovative, affordable, and replicable solutions using drones and spatial data   In 2016, Category 4 Hurricane Matthew made landfall in the south of Haiti, causing destruction unseen in more than a decade during the hurricane season, with winds as high as 230 kilometers per hour and 600 millimeters of rain in less than 24 hours. The toll was very high, with a total of 546 victims, 128 missing, 439 injured, and 2.1 million people affected. Following the disaster, the Government of Haiti (GoH) was confronted with the challenge of determining the overall economic im...

To harness machine learning for climate resilience, first address ethics challenges From mapping hazard-prone urban areas in Tanzania to providing drought early warnings, disaster risk management professionals are finding new applications of machine learning (ML) at a rapid pace. Highlighted by the UK government's Frontier Data Report as a technology with "vast potential" to support climate adaptation and the Sustainable Development Goals, ML technology now represents a global industry valued at $27 billion per year. In disaster risk management, ML can help create actionable information faste...

Geohazards, or geological hazards, are often defined as “events caused by geological, geomorphological, and climatic conditions or processes that represent serious threats to human lives, property, and the natural and built environment.” Falls, collapses, landslides, flows, and erosions are all categorized as geohazards (Figure 1). In addition, less probable but high impact geohazards include glacial lake outburst floods (GLOF) or avalanches. Geohazards result in the loss of human lives, extensive physical damage to buildings and critical infrastructure, and economic impact due to disru...