Paterne Gahungu
Biographic Data
| ID | 6933312 |
|---|---|
| NAME | Paterne Gahungu |
| GIVEN NAMES | Paterne |
| FAMILY NAME | Gahungu |
| SIGNATURE | GAHUNGU P |
| AFFILIATIONS | African Institute for Mathematical Sciences |
| ORCID | 0000-0002-3335-4716 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Multimodal Deep Learning Framework for Profiling Socio-Economic Indicators and Public Health Determinants in Urban Environments
Urbanization significantly enhances socio-economic conditions, health, and well-being for many by improving access to services, education, and economic opportunities. However, socio-economic and public health disparities are also being exacerbated by urbanization. The reliable data required to monitor these conditions are often unavailable, outdated, or inconsistent. This study introduces a multimodal deep learning framework that integrates satel…
Integrating Machine Learning and Geospatial Data for Mapping Socioeconomic Vulnerability to Urban Natural Hazard
Rapid urbanization and climate change are increasing the risks associated with natural hazards, especially in cities where socio-economic disparities are significant. Current hazard risk assessment frameworks fail to consider socio-economic factors, which limits their ability to effectively address vulnerabilities at the community level. This study introduces a machine learning framework designed to assess flood susceptibility and socio-economic …
City-wide space-time patterns of environmental noise pollution in Kigali, Rwanda
As cities in sub-Saharan Africa become more crowded, noise pollution is also emerging as an important environmental concern, after air pollution. Yet, unlike air pollution, which is enjoying relatively more public attention, there is limited measurement data and policy efforts on environmental noise pollution. We followed a recent city-wide measurement approach used in Accra (Ghana) and characterized environmental noise patterns in Kigali, a cont…
No prominent works on this page.
Integrating Machine Learning and Geospatial Data for Mapping Socioeconomic Vulnerability to Urban Natural Hazard
Rapid urbanization and climate change are increasing the risks associated with natural hazards, especially in cities where socio-economic disparities are significant. Current hazard risk assessment frameworks fail to consider socio-economic factors, which limits their ability to effectively address vulnerabilities at the community level. This study introduces a machine learning framework designed to assess flood susceptibility and socio-economic …
City-wide space-time patterns of environmental noise pollution in Kigali, Rwanda
As cities in sub-Saharan Africa become more crowded, noise pollution is also emerging as an important environmental concern, after air pollution. Yet, unlike air pollution, which is enjoying relatively more public attention, there is limited measurement data and policy efforts on environmental noise pollution. We followed a recent city-wide measurement approach used in Accra (Ghana) and characterized environmental noise patterns in Kigali, a cont…
Multimodal Deep Learning Framework for Profiling Socio-Economic Indicators and Public Health Determinants in Urban Environments
Urbanization significantly enhances socio-economic conditions, health, and well-being for many by improving access to services, education, and economic opportunities. However, socio-economic and public health disparities are also being exacerbated by urbanization. The reliable data required to monitor these conditions are often unavailable, outdated, or inconsistent. This study introduces a multimodal deep learning framework that integrates satel…
Air pollution (1 work) · Animal Vocal Communication and Behavior (1 work) · Cartography (1 work) · Climate change impacts on agriculture (1 work) · Computer Science (1 work) · Computer security (1 work) · Convolutional neural network (1 work) · Data science (1 work) · Deep learning (1 work) · Disaster Management and Resilience (1 work)