2 citations · 3 across the 4 of their papers we have counts for
5 papers
Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings
Steven Ndung'u, Adel Daoud, Ismael Yacoubou Djima +2
Progress in inclusive household surveys has strengthened socioeconomic evidence for forcibly displaced populations, providing indispensable benchmarks on living conditions and welf…
Beyond Point Predictions: Uncertainty-Aware Satellite Poverty Mapping for Public Policy
Markus B. Pettersson, James Bailie, Mohammad Kakooei +2
Despite their critical importance for policy and research, high-resolution poverty data remain limited across much of Africa. Machine learning (ML) with earth observation (EO) imag…
Leveraging Compact Satellite Embeddings and Graph Neural Networks for Large-Scale Poverty Mapping
Markus B. Pettersson, Adel Daoud
Accurate, fine-grained poverty maps remain scarce across much of the Global South. While Demographic and Health Surveys (DHS) provide high-quality socioeconomic data, their spatial…
Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach
Mohammad Kakooei, Klaudia Solska, Adel Daoud
Reducing global poverty is a key objective of the Sustainable Development Goals (SDGs). Achieving this requires high-frequency, granular data to capture neighborhood-level changes,…
A High Resolution Urban and Rural Settlement Map of Africa Using Deep Learning and Satellite Imagery
Mohammad Kakooei, James Bailie, Markus B. Pettersson +3
Accurate and consistent mapping of urban and rural areas is crucial for sustainable development, spatial planning, and policy design. It is particularly important in simulating the…