Nikola Milojevic-Dupont
Biographic Data
| ID | 6821797 |
|---|---|
| NAME | Nikola Milojevic-Dupont |
| GIVEN NAMES | Nikola |
| FAMILY NAME | Milojevic-Dupont |
| SIGNATURE | MILOJEVIC-DUPONT N |
| AFFILIATIONS | Mercator Research Institute on Global Commons and Climate Change |
| ORCID | 0000-0001-6641-1978 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Towards an integrated understanding of circularity, urban design, planetary stability and well-being
Circularity strategies are increasingly recognized as essential for reducing greenhouse gas (GHG) emissions and improving material efficiency in urban environments. However, existing approaches often analyze sectors in isolation, limiting their ability to capture systemic interactions and spatial dependencies. In this study, we introduce the Integrated Urban Circularity (INTURC) framework that integrates construction, building energy use, transpo…
Predicting building characteristics at urban scale using graph neural networks and street-level context
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass …
Predicting building age from urban form at large scale
Machine learning for geographically differentiated climate change mitigation in urban areas
No prominent works on this page.
Machine learning for geographically differentiated climate change mitigation in urban areas
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass …
Predicting building age from urban form at large scale
Predicting building characteristics at urban scale using graph neural networks and street-level context
Towards an integrated understanding of circularity, urban design, planetary stability and well-being
Circularity strategies are increasingly recognized as essential for reducing greenhouse gas (GHG) emissions and improving material efficiency in urban environments. However, existing approaches often analyze sectors in isolation, limiting their ability to capture systemic interactions and spatial dependencies. In this study, we introduce the Integrated Urban Circularity (INTURC) framework that integrates construction, building energy use, transpo…
Computer Science (4 works) · Climate change (3 works) · Geography (3 works) · Impact of Light on Environment and Health (3 works) · Machine learning (3 works) · Urban planning (3 works) · Air Quality Monitoring and Forecasting (2 works) · Artificial Intelligence (2 works) · Cartography (2 works) · Civil engineering (2 works)