Recent advances in artificial intelligence and deep machine learning have created a step
change in how to measure human development indicators, in particular asset-based …

The COVID-19 pandemic has devastated many low-and middle-income countries, causing
widespread food insecurity and a sharp decline in living standards. In response to this crisis …

The application of machine learning (ML) in a range of geospatial tasks is increasingly
common but often relies on globally available covariates such as satellite imagery that can …

In many regions of the world, sparse data on key economic outcomes inhibit the
development, targeting and evaluation of public policy,. We demonstrate how advancements …

Climate change is leading to more extreme weather hazards, forcing human populations to
be displaced. We employ explainable machine learning techniques to model and …

Universal clean cooking is a key target under Sustainable Development Goal (SDG) 7, with
implications for several other SDGs, such as good health, gender equality and climate. Yet …

Many cities in low-and medium-income countries (LMICs) are facing rapid unplanned
growth of built-up areas, while detailed information on these deprived urban areas (DUAs) is …

Abstract Machine learning approaches using satellite imagery are providing accessible
ways to infer socioeconomic measures without visiting a region. However, many algorithms …

For many countries in the Global South traditional poverty estimates are available only
infrequently and at coarse spatial resolutions, if at all. This limits decision-makers' and …

Increases in data availability coupled with enhanced computational capacities are
revolutionizing conservation. But in the excitement over the opportunities afforded by new …