Estimating building stock at the elemental level is essential for a circular economy. However, the lack of information on building stock due to considerable time and costs associated with data collection poses a significant challenge in large-scale analysis. This study employed computer vision-based façade classification and weighted material intensity to estimate building façade material stock using a bottom-up approach. Autonomous semantic enhancements on façade material typologies and opening areas aided better estimation of building-facing materials’ weight. ResNet 50 deep learning architecture was chosen, with an F1 score of 0.77 and 80 % accuracy. Most façade classes have achieved a relatively high level of accuracy of predictions. “Common Brick”, “Face Brick”, “Concrete Blocks”, and “Concrete” were identified as the most widespread façade materials in the model deployed area of interest: Southport (SA3), City of Gold Coast, Australia. The model provides the necessary foundations for progressing circular economy and urban metabolism efforts.