PCIer: Pavement Condition Evaluation Using Aerial Imagery and Deep Learning

Sisi Han (), In-Hun Chung, Yuhan Jiang () and Benjamin Uwakweh
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Sisi Han: Department of Civil, Construction and Environmental Engineering, Marquette University, Milwaukee, WI 53233, USA
In-Hun Chung: Department of Construction and Operations Management, South Dakota State University, Brookings, SD 57007, USA
Yuhan Jiang: Department of Built Environment, North Carolina A&T State University, Greensboro, NC 27411, USA
Benjamin Uwakweh: Department of Built Environment, North Carolina A&T State University, Greensboro, NC 27411, USA

Geographies, 2023, vol. 3, issue 1, 1-11

Abstract: This paper aims to explore and evaluate aerial imagery and deep learning technology in pavement condition evaluation. A convolutional neural network (CNN) model, named PCIer , was designed to process aerial images and produce pavement condition index (PCI) estimations, which are classified into four scales of Good (PCI ≥ 70), Fair (50 ≤ PCI < 70), Poor (25 ≤ PCI < 50), and Very Poor (PCI < 25). In the experiment, the PCI datasets were retrieved from the published pavement condition report by the City of Sacramento, CA. Following the retrieved datasets, the authors also collected the corresponding aerial image datasets containing 100 images for each PCI grade from Google Earth. An 80% proportion of datasets were used for PCIer model training, and the remaining were used for testing. Comparisons showed using a 128-channel heatmap layer in the proposed PCIer model and saving the PCIer model with the best validation accuracy would yield the best performance, with a testing accuracy of 0.97, and a weighted average precision, recall, and F1-score of 0.98, 0.97, and 0.97, respectively. Moreover, future research recommendations are provided in the discussion for improving the effectiveness of pavement evaluation via aerial imagery and deep learning.

Keywords: aerial imagery; convolutional neural network (CNN); pavement condition index (PCI) (search for similar items in EconPapers)
JEL-codes: Q1 Q15 Q5 Q53 Q54 Q56 Q57 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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