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In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructuresegmentation workflow is designed and optimized, which is capable ofefficiently processing multi-sourced geospatial data including LiDAR data andsatellite imagery data. We used an expanded definition of pedestrianinfrastructure inventory which goes beyond the traditional transportationelements to include street furniture objects that are important foraccessibility but are often omitted from the traditional definition. Ourcontributions lie in producing the necessary knowledge to answer the followingtwo questions. First, which data representation can facilitate zero-shotsegmentation of infrastructure objects with SAM? Second, how well does theSAM-based method perform on segmenting pedestrian infrastructure objects? Ourfindings indicate that street view images generated from mobile LiDAR pointcloud data, when paired along with satellite imagery data, can work efficientlywith SAM to create a scalable pedestrian infrastructure inventory approach withimmediate benefits to GIS professionals, city managers, transportation owners,and walkers, especially those with travel-limiting disabilities, such asindividuals who are blind, have low vision, or experience mobilitydisabilities.

Pedestrian Accessible Infrastructure Inventory: Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types