Alex Olson
Biographic Data
| ID | 9037776 |
|---|---|
| NAME | Alex Olson |
| GIVEN NAMES | Alex |
| FAMILY NAME | Olson |
| SIGNATURE | OLSON A |
| AFFILIATIONS | University of Toronto |
| ORCID | 0000-0002-3371-3213 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Image‐based prediction of residential building attributes with deep learning
This study estimates building attributes—floor area and age—using image‐based machine learning. Building age and floor area are key inputs to the studies of urban metabolism, material stocks and flows, and embodied greenhouse gases (GHGs) in the built environment. However, these data are challenging to generate and maintain using traditional survey methods, their availability is uneven and often, even when available, very uncertain. Improving our…
Total- and monomethyl-mercury and major ions in coastal California fog water
Marine fog water samples were collected over two summers (2014–2015) with active strand collectors (CASCC) at eight coastal sites from Humboldt to Monterey counties in California, USA, and on four ocean cruises along the California coastline in order to investigate mercury (Hg) cycling at the ocean-atmosphere-land interface. The mean concentration of monomethylmercury (MMHg) in fog water across terrestrial sites for both years was 1.6 ± 1.9 ng L-…
No prominent works on this page.
Total- and monomethyl-mercury and major ions in coastal California fog water
Marine fog water samples were collected over two summers (2014–2015) with active strand collectors (CASCC) at eight coastal sites from Humboldt to Monterey counties in California, USA, and on four ocean cruises along the California coastline in order to investigate mercury (Hg) cycling at the ocean-atmosphere-land interface. The mean concentration of monomethylmercury (MMHg) in fog water across terrestrial sites for both years was 1.6 ± 1.9 ng L-…
Image‐based prediction of residential building attributes with deep learning
This study estimates building attributes—floor area and age—using image‐based machine learning. Building age and floor area are key inputs to the studies of urban metabolism, material stocks and flows, and embodied greenhouse gases (GHGs) in the built environment. However, these data are challenging to generate and maintain using traditional survey methods, their availability is uneven and often, even when available, very uncertain. Improving our…
Air Quality and Health Impacts (1 work) · Artificial Intelligence (1 work) · Building Energy and Comfort Optimization (1 work) · Chemistry (1 work) · Computer Science (1 work) · Cycling (1 work) · Deep learning (1 work) · Environmental Chemistry (1 work) · Environmental Science (1 work) · Geography (1 work)