Abstract

A curb cut is a ramp that connects the sidewalk to a street crossing, thereby making it accessible for physically disabled pedestrians. Initially, ArcGIS Pro and machine learning algorithms in Python was utilized to classify a dataset of curb cut spectral signatures, leveraging Sentinel-2 imagery with a 10-m resolution. Initially, multispectral visible and near-infrared (NIR) Sentinel-2 sensors, along with machine learning geoprocessing tools in ArcGIS Pro, were utilized to generate signatures from curb cuts manually identified using Google Earth in Hillsborough County, Florida. Subsequently, we interpolated these capture points using a Python-modified Bayesian Maximum Likelihood Estimator to produce a cross-county signature map. The resulting layer was overlaid onto a zip-code gridded land use land cover (LULC) map and analyzed using a semi-parametric eigendecomposition eigen-spatial filtering approach. The hot/cold spot residuals represented independent curb cut clustering propensities. Utilizing higher sub-resolution satellite signals can optimize the identification of LULC classifiable, zip-code gridded capture point signatures, thereby improving the predictive mapping of other curb cut geolocations, such as those in school parking lots and homeless shelters. A real-time satellite mapping system can utilize sub-meter resolution data in a mobile application to retrieve a ranked list of visually similar curb cut geolocations.

Key words: Curb cuts, curb ramps, satellite data, ArcGIS, python, eigenvectors, Hillsborough County.