Mapping heat, growing green.

So what did we do about it?

We put on our thinking caps and built a machine learning model that accurately showcases "HotSpots" in Toronto based on vegetation and urban island heat influencing factors.

TLDR; Check for trees and building density around a specific location. Mix it in with temperatures and some special ML magic and you get a heat vulnerability score.

{
  w1: 0.6,  // temperature
  w2: 0.2,  // vegetation
  w3: 0.2   // buildings
}

Google Earth Rasterization of NDVI

NDVI raster shows vegetation health and density across Toronto, with brighter areas indicating more vegetation.

Google Earth Rasterization of LST

LST raster shows land surface temperature across Toronto, with brighter areas indicating higher temperatures (hotter urban heat islands)

*Notice how areas near the waterbody are darker which corresponds to cooler temperatures.

Data Classifications for ML Model

As part of feature extraction, we rasterized both building density and tree density into uniform 100 m grid cells across Toronto.

These become two of the three columns in our feature matrix X (the third being normalized temperature). We then fed X into our Random Forest, which learned how those spatial patterns combine to predict heat‐vulnerability scores.

Tree Density Raster

Tree density raster shows the distribution of tree cover across Toronto. Each pixel in the grid represents the density of trees in that area—brighter areas indicate higher tree density, while darker areas indicate fewer trees.

In ML terms: Vegetation input (proxy for NDVI), representing normalized tree/vegetation cover for each 100 m cell.

Building Density Raster

Building density raster shows the concentration of buildings across Toronto. Each pixel in the grid represents the density of buildings in that area—brighter areas indicate more buildings, while darker areas indicate fewer buildings.

In ML terms: Urbanization input, showing how built-up each grid cell is (m² of roof per 10,000 m²).