Jean Pierre Bizimana
Biographic Data
| ID | 6933311 |
|---|---|
| NAME | Jean Pierre Bizimana |
| GIVEN NAMES | Jean Pierre |
| FAMILY NAME | Bizimana |
| SIGNATURE | BIZIMANA J P |
| AFFILIATIONS | University of Rwanda |
| ORCID | 0000-0001-9098-416X |
| 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 |
Rural electrification as multi-sectoral coevolution
Research on multisectoral interactions is gaining attention for its potential to address global challenges, including climate change, food security, and energy access. These dynamics are especially relevant in sub-Saharan Africa, where expanding energy access is a key policy goal. This study contributes to the literature on sectoral coevolution with a case from a novel geographical and sectoral context. Drawing on economic geography and energy an…
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 The reliable data required to monitor these conditions are often unavailable, outdated, or inconsistent.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 …
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 …
Rural electrification as multi-sectoral coevolution
Research on multisectoral interactions is gaining attention for its potential to address global challenges, including climate change, food security, and energy access. These dynamics are especially relevant in sub-Saharan Africa, where expanding energy access is a key policy goal. This study contributes to the literature on sectoral coevolution with a case from a novel geographical and sectoral context. Drawing on economic geography and energy an…
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…
Cartography (1 work) · Climate change impacts on agriculture (1 work) · Coevolution (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) · Electricity (1 work)