UN Working Paper evidence base for conceptual framework of cyclic relationship between climate change and social inequality.
Read MoreOur project aims to raise awareness and conceptual understanding of climate change by bringing the future closer. Conceptual interactive website to show precise and personalised impacts of climate change using AI and climate modelling. Bringing together researchers from different fields, the website aims to act as an educational tool that will produce accurate and vivid […]
Read MoreA case study from ResearchGate We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs). Case Study: Explores the potential of using images from a simulated 3D environment to improve a domain adaptation task carried out by the MUNIT architecture, aiming […]
Read MoreCase Study: Community perspective of the game-changing socio-economic value that could be achieved with better forecasts, especially among vulnerable communities. The paper presents a new way to view this opportunity by better understanding the problem, with the goal of inspiring the Climate Change AI community to contribute to this important aspect of the climate adaptation […]
Read MoreCase Study: Most of Rwanda’s crop production comes from smallholder farms. The country’s agriculture officials have historically had insufficient data on where crops are cultivated or how much yield to expect — a hindrance for government’s future planning. Building on previous work with emerging technologies, machine learning, economics, and agriculture, the paper develops a new […]
Read MoreCase Study: pioneering flood mapping and response organization that uses high-cadence, high-resolution satellite imagery to build country-wide flood monitoring and dynamic analytics systems for the most vulnerable around the world.
Read MoreThe paper provides pillars of action for the AI community, and includes a focus on climate justice where the author recommends that environmental impacts should not be externalised onto the most marginalised populations, and that the gains are not only captured by digitally mature countries in the global north. This will require centring front-line communities […]
Read MoreAI methods can be used to reduce the psychological distance to climate impacts in groups who are not actively experiencing the negative effects of climate change in their daily life.
Read MoreA benefit of using AI methods in climate change solutions is that the potential for remote collection of data and analysis in the cloud allows for far-reaching geographic applications of solutions.
Read MoreThe relationship between climate change and social inequalities is a vicious cycle. Initial inequality causes disproportionate disadvantage from the effects of climate change to certain groups, resulting in further inequality.
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