This paper examines the feasibility of using satellite imagery and artificial intelligence to develop an efficient and cost-effective way to determine and predict the condition of roads in the Asia and Pacific region.
This paper compiles granular population data for the Philippines and Thailand, demonstrating cost-effective ensemble methods that can support public sector planning.
This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand.
This study outlines a computational framework to enhance the spatial granularity of government-published poverty estimates, citing data from the Philippines and Thailand.