A 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. AI can be used to help vulnerable groups adapt to the changing climate by identifying settlements in satellite data and subsequently communicating their risk from a changing climate; as well as remote monitoring of natural disasters in real time for aid relief.
Case 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 More
Case 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 More
Case 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 More
The 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 More