Dan Nyandega
Biographic Data
| ID | 4416936 |
|---|---|
| NAME | Dan Nyandega |
| GIVEN NAMES | Dan |
| FAMILY NAME | Nyandega |
| SIGNATURE | NYANDEGA D |
| AFFILIATIONS | Queensland University of Technology |
| ORCID | 0000-0002-1172-8430 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Machine learning applications for urban geospatial analysis
The application of machine learning (ML) in geospatial analysis has witnessed a significant upsurge, particularly in the last five years. This surge is linked to exponential developments in artificial intelligence (AI) technologies and the extensive demand for their applications in geospatial analysis fields such as urban and environmental studies and planning. Given these rapid developments, understanding the capabilities and applications of ML …
Algorithmic urban greening for thermal resilience
Algorithmic green infrastructure optimisation
Green infrastructure (GI) is a fundamental building block of our cities. It contributes to the sustainability and vitality of cities by offering various benefits such as greening, cooling, water, air quality, and managing carbon emissions. GI plays an essential role in enhancing overall well-being. The utilisation of artificial intelligence (AI) technologies for GI optimisation is perceived as a powerful approach for cities. A knowledge gap, neve…
No prominent works on this page.
Algorithmic green infrastructure optimisation
Green infrastructure (GI) is a fundamental building block of our cities. It contributes to the sustainability and vitality of cities by offering various benefits such as greening, cooling, water, air quality, and managing carbon emissions. GI plays an essential role in enhancing overall well-being. The utilisation of artificial intelligence (AI) technologies for GI optimisation is perceived as a powerful approach for cities. A knowledge gap, neve…
Machine learning applications for urban geospatial analysis
The application of machine learning (ML) in geospatial analysis has witnessed a significant upsurge, particularly in the last five years. This surge is linked to exponential developments in artificial intelligence (AI) technologies and the extensive demand for their applications in geospatial analysis fields such as urban and environmental studies and planning. Given these rapid developments, understanding the capabilities and applications of ML …
Algorithmic urban greening for thermal resilience
Computer Science (3 works) · Ecology (2 works) · Environmental planning (2 works) · Urban Heat Island Mitigation (2 works) · Artificial Intelligence (1 works) · Biology (1 works) · Building Energy and Comfort Optimization (1 works) · Business (1 works) · Climate change (1 works) · Data science (1 works)