The evidence supporting climate change and the related effects due to human activities is now overwhelming and compelling. We are already seeing the negative effects of climate change through an increase in extreme weather events and calamities, such as wildfires resulting from soaring temperatures. The United Nations’ 2021 Intergovernmental Panel on Climate Change (IPCC) report stated that without meaningful decarbonization, almost half of the world’s population could be exposed to severe climate hazards in the next couple of decades. Analysis of infrastructure and planning are key to any mitigation efforts.

In order to facilitate geospatial AI predictive modelling, we have adopted the use of Data Cubes. It is a core component we utilize for managing the large, complex, and multidimensional environmental data required for modelling and predictions. A data cube is essentially a multidimensional array that organizes data along various dimensions, such as space (latitude, longitude, elevation/depth), time, and different variables (e.g., temperature, flood depth, fire potential index).

This structure is specifically designed to facilitate efficient multidimensional data analysis through Online Analytical Processing (OLAP) operations, which allows users and analytical models to quickly "slice and dice," drill down, and roll up data from multiple perspectives without having to query the original, raw data sources.