01764283

Component

https://annualmeeting.myt...nlineProgram/Details/15826

This study investigates the feasibility of utilizing high density mobile Light Detection and Ranging (LiDAR) to measure streetscape features that are not detectable with publicly-available aerial LiDAR. The authors' previous studies investigated the limits of utilizing USGS QL2 and QL1 data for streetscape extraction, utilizing voxels to measure feature location in a 3D space; the studies found that smaller features – such as landscape furniture, traffic signage, and traffic signals – were not thoroughly represented. This paper’s results suggest that mobile LiDAR’s density allows for much smaller voxels and to thoroughly measure smaller streetscape features in 3D. This includes street trees, light/lampposts, street furniture, traffic and commercial signage, building window proportions, awnings, and enclosed courtyard restaurants. Moreover, mobile LiDAR facilitated the ability to objectively measure and categorize these streetscape features in walkable, downtown-like streetscapes. The ability to compartmentalize such streetscapes into smaller cubic feet voxels to be quantitatively measured and categorized could supplement or replace conventional audit-based streetscape measurement. This study introduces new methods – based on voxel data analysis – to compile objective descriptive statistics of streetscape features and how they can be represented in 3D.

This paper was sponsored by TRB committee AED40 Standing Committee on Geographic Information Science.

TRBAM-21-01069

English

28p

2021

Transportation Research Board 100th Annual Meeting

Digital/other

Figures; Photos; References; Tables

Data analysis; Laser radar; Mathematical models; Metrics (Quantitative assessment); Roadside; Roadside structures; Street lighting; Streetscape; Trees

Data and Information Technology; Highways; Planning and Forecasting; Terminals and Facilities

Transportation Research Board Annual Meeting 2021 Paper #TRBAM-21-01069

TRIS, TRB, ATRI

Dec 23 2020 11:24AM