Mapping Drainage Structures Using Airborne Laser Scanning by Incorporating Road Centerline Information

Abstract

Wide-area drainage structure (DS) mapping is of great concern, as many DSs are reaching the end of their design life and information on their location is usually absent. Recently, airborne laser scanning (ALS) has been proven useful for DS mapping through manual methods using ALS-derived digital elevation models (DEMs) and hillshade images. However, manual methods are slow and labor-intensive. To overcome these limitations, this paper proposes an automated DS mapping algorithm (DSMA) using classified ALS point clouds and road centerline information. The DSMA begins with removing ALS ground points within the buffer of the road centerlines; the size of the buffer varies according to different road classes. An ALS-modified DEM (ALS-mDEM) is then generated from the remaining ground points. A drainage network (DN) is derived from the ALS-mDEM. Candidate DSs are then obtained by intersecting the DN with the road centerlines. Finally, a refinement buffer of 15 m is placed around each candidate DS to prevent duplicate DS from being generated in close proximity. A total area of 50 km2, including an urban site and a rural site, in Vermont, USA, was used to assess the DSMA. Based on the road functional classification scheme of the Federal Highway Administration (FHWA), the centerline information regarding FHWA roads was obtained from a public data portal. The centerline information on non-FHWA roads, i.e., private roads and streets, was derived from the impervious surface data of a land cover dataset. A benchmark DS dataset was gathered from the transport agency of Vermont and was further augmented using Google Earth Street View images by the authors. The one-to-one correspondence between the benchmark DS and mapped DS for these two sites was then established. The positional accuracy was assessed by computing the Euclidian distance between the benchmark DS and mapped DS. The mean positional accuracy for the urban site and rural site were 13.5 m and 15.8 m, respectively. F1-scores were calculated to assess the prediction accuracy. For FHWA roads, the F1-scores were 0.87 and 0.94 for the urban site and rural site, respectively. For non-FHWA roads, the F1-scores were 0.72 and 0.74 for the urban site and rural site, respectively.

Accuracy Comparison on Culvert-Modified Digital Elevation Models of DSMA and BA Methods Using ALS Point Clouds

“…Only until recently was a cost-effective and automated drainage structure mapping algorithm (DSMA) developed by Wang and Fareed (2021) [14]. The DSMA was developed using classified ALS point clouds and road centerline data.…”

“…The DSMA was developed using classified ALS point clouds and road centerline data. The evaluation of DS mapping was made using a benchmark DS dataset acquired from the Vermont Department of Transportation (VTrans), which was further augmented using Google Earth Street View (GE-SV) images [14]. In their research, the evaluation metrics of the prediction accuracies (e.g., precision (P), recall (R), and F1-Score) and positional accuracies, in terms of Euclidean distances of the mapped DSs compared to the benchmark DSs, were assessed.…”

“…In their research, the evaluation metrics of the prediction accuracies (e.g., precision (P), recall (R), and F1-Score) and positional accuracies, in terms of Euclidean distances of the mapped DSs compared to the benchmark DSs, were assessed. Using the prediction and positional accuracies, the authors addressed all aspects of the evaluation metrics for two different sites in Vermont, USA [14]. The assessment on DS mapping compared to the benchmark DS dataset showed that the DSMA performance was in close agreement with the benchmark DS dataset of two different landscapes (e.g., urban and rural) test sites.…”

“…Therefore, the DSMA is a capable tool for addressing the DS mapping challenges in a wide area capacity. The detailed evaluation metrics of the DSMA can be found in [14]. 1.1.5.…”

“…A common practice among ALS data vendors, including the producers of HR-DEMs, is to classify the bridges as a separate class from ALS ground points as per the Light Detection and Ranging (LiDAR) base specifications developed by the American Society of Photogrammetry and Remote Sensing (ASPRS) [13]. One of the challenges faced while classifying ALS point clouds is the lack of information on the locations of some bridges and, particularly culverts that are difficult to discern from ALS point clouds through visual inspection [14]. Yet, an ASPRS LiDAR-based classification scheme provides a separate class code representing culverts in the classified ALS data.…”