This paper presents an urban footpath image dataset captured through crowdsourcing using the mapillary service (mobile ap- plication) and demonstrating its use for data analytics applications by employing object detection and image segmentation. The study was motivated by the unique, individual mobility challenges that many people face in navigating public footpaths, in particular those who use mobility aids such as long cane, guide digs, crutches, wheelchairs, etc., when faced with changes in pavement surface (tactile pavements) or obstacles such as bollards and other street furniture. Existing image datasets are generally captured from an instrumented vehicle and do not provide sufficient or adequate images of the footpaths from the pedestrian perspective. A citizen science project (Crowd4Access) worked with user groups and volunteers to gather a sample image dataset resulting in a set of 39,642 images collected in a range of different conditions. Preliminary studies to detect tactile pavements and perform semantic segmentation using state-of-the-art computer vision models demonstrate the utility of this dataset to enable better understanding of urban mobility issues.