  In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructure
segmentation workflow is designed and optimized, which is capable of
efficiently processing multi-sourced geospatial data including LiDAR data and
satellite imagery data. We used an expanded definition of pedestrian
infrastructure inventory which goes beyond the traditional transportation
elements to include street furniture objects that are important for
accessibility but are often omitted from the traditional definition. Our
contributions lie in producing the necessary knowledge to answer the following
two questions. First, which data representation can facilitate zero-shot
segmentation of infrastructure objects with SAM? Second, how well does the
SAM-based method perform on segmenting pedestrian infrastructure objects? Our
findings indicate that street view images generated from mobile LiDAR point
cloud data, when paired along with satellite imagery data, can work efficiently
with SAM to create a scalable pedestrian infrastructure inventory approach with
immediate benefits to GIS professionals, city managers, transportation owners,
and walkers, especially those with travel-limiting disabilities, such as
individuals who are blind, have low vision, or experience mobility
disabilities.
