This paper presents a novel framework for estimating urban tree dimensions and their associated CO₂ sequestration potential using neural network-based image segmentation applied to Google Street View imagery. Our approach combines a dual image acquisition strategy with geometric modelling based on the pinhole camera lens model to automatically extract key tree metrics such as trunk diameter and overall height. By leveraging road width data to approximate the distance to trees and applying trigonometric equations, pixel measurements are converted into real-world dimensions. To facilitate large-scale analysis, a custom neural network that builds on established models was developed – fine-tuning a Faster R-CNN/ResNet-50-FPN architecture with extensive data augmentation from both the Urban Street Tree Dataset and local imagery. Validation against ground truth measurements shows that while tree height estimates achieve high accuracy (within 3% error), diameter estimations exhibit greater variability, highlighting areas for future improvement. The derived tree dimensions are then used to compute CO₂ sequestration using standard forestry equations, demonstrating the method’s potential to inform urban sustainability assessments. Overall, this integrated pipeline offers a scalable solution for urban tree inventory and environmental impact studies.