This research investigates the degree to which advanced GeoAI techniques, particularly deep learning models, and how these models can reproduce older adults perceptions of urban green spaces and to map the quality of green spaces across the city using street-level imagery. The study compares three popular deep-learning feature extractors to predict and map seniors' perceptual responses to green spaces in Ottawa, utilizing data derived from Mapillary street-level images. In these images, senior citizen volunteers were seated in front of a computer and selected between images based on perceptual dimensions such as safety and aesthetics. A few AI ranking models are constructed for the purpose of identifying how well street-level photographs are valued by seniors within Ottawa’s green spaces. Our focus is mainly on finding the best-performing model and backbone training dataset via experimentation with various generations of deep learning architectures and using statistics to determine how such differences affect human perceptual modelling. Detailed prediction maps were predicted on park photographs from Mapillary that indicated how seniors perceive public spaces across the city. By combining deep learning, image segmentation, and spatial analysis, the research offers an empirical contribution of how AI can contribute insights into how urban planning can be enhanced to meet the needs of an ageing population and creates a foundation for further investigation into age-friendly environments.