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Sidewalk geodata are essential to understand walking behavior. However, such geodata are scarce, only available at the local jurisdiction and not at the regional level. If they exist, the data are stored in geometric representational formats without network characteristics such as sidewalk connectivity and completeness. This article presents the Split-Match-Aggregate (SMA) algorithm, which automatically conflates sidewalk information from secondary geometric sidewalk data to existing street network data. The algorithm uses three parameters to determine geometric relationships between sidewalk and street segments: the distance between streets and sidewalk segments; the angle between sidewalk and street segments; and the difference between the lengths of matched sidewalk and street segments. The SMA algorithm was applied in urban King County, WA, to 13 jurisdictions’ secondary sidewalk geodata. Parameter values were determined based on agreement rates between results obtained from 72 pre-specified parameter combinations and those of a trained geographic information systems (GIS) analyst using a randomly selected 5% of the 79,928 street segments as a parameter-development sample. The algorithm performed best when the distances between sidewalk and street segments were 12 m or less, their angles were 25° or less, and the tolerance was set to 18 m, showing an excellent agreement rate of 96.5%. The SMA algorithm was applied to classify sidewalks in the entire study area and it successfully updated sidewalk coverage information on the existing regional-level street network data. The algorithm can be applied for conflating attributes between associated, but geometrically misaligned line data sets in GIS. [ABSTRACT FROM AUTHOR]

(1) Background: Public sidewalk GIS data are essential for smart city development. We developed an automated street-level sidewalk detection method with image-processing Google Street View data. (2) Methods: Street view images were processed to produce graph-based segmentations. Image segment regions were manually labeled and a random forest classifier was established. We used multiple aggregation steps to determine street-level sidewalk presence. (3) Results: In total, 2438 GSV street images and 78,255 segmented image regions were examined. The image-level sidewalk classifier had an 87% accuracy rate. The street-level sidewalk classifier performed with nearly 95% accuracy in most streets in the study area. (4) Conclusions: Highly accurate street-level sidewalk GIS data can be successfully developed using street view images. [ABSTRACT FROM AUTHOR]