01750957

Component

https://doi.org/10.1177/0361198120944172

This study investigates the feasibility of extracting streetscape features from high-density United States Geological Survey (USGS) quality level 1 (QL1) light detection and ranging (LiDAR) and quantifying the features into three-dimensional (3D) volumetric pixel (voxel) zones. As the USGS embarks on a national LiDAR database with the goal of collecting LiDAR across the continuous U.S.A., the USGS primarily requires QL2 or QL1 as a collection standard. The authors’ previous study thoroughly investigated the limits of extracting streetscape features with QL2 data, which was primarily limited to buildings and street trees. Recent studies published by other researchers that utilize advanced digital mapping techniques for streetscape measuring acknowledge that most features outside of buildings and street trees are too small to detect. QL1 data, however, is four times denser than QL2 data. This study divides streetscapes into Thiessen proximal polygons, sets voxel parameters, classifies QL1 LiDAR point cloud data, and computes quantitative statistics where classified point cloud data intersects voxels within the streetscape polygons. It demonstrates how most other common streetscape features are detectable in a standard urban QL1 dataset. In addition to street trees and buildings, one can also legitimately extract and statistically quantify walls, fences, landscape vegetation, light posts, traffic lights, power poles, power lines, street signs, and miscellaneous street furniture. Furthermore, as these features are processed into their appropriate voxel height zones, this study introduces a new methodology for obtaining comprehensive tabular descriptive statistics describing how streetscape features are truly represented in 3D.

The contents of this paper reflect the views of the authors, who are responsible for the facts and accuracy of the information presented. © National Academy of Sciences: Transportation Research Board 2020.

English

pp 192-206

2020-11

Transportation Research Record: Journal of the Transportation Research Board

Web

References (48)

Aerial surveying; Descriptive statistics; Laser radar; Measurement; Methodology; Streetscape; Surveying by feature

Data and Information Technology; Design; Highways; Planning and Forecasting

TRIS, TRB, ATRI

Aug 27 2020 3:03PM