Color Component–Based Road Feature Extraction from Airborne Lidar and Imaging Data Sets

Liu, Li; Lim, Samsung

doi:10.1061/(asce)su.1943-5428.0000198

Cited by 7 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This kind of research only focuses on the pavement. For overall existing road recognition, Liu and Lim [24] presented a new framework of road feature extraction from colour component-based data fusion of aerial imagery and lidar data. Jasim [19] extracted urban roads from DEM of LiDAR with IKONOS images using machine learning (ML).…”

Section: Existing Road Digitalizitionmentioning

confidence: 99%

Building digital twins of existing highways using map data based on engineering expertise

Feng

Ma²,

Broyd³

et al. 2022

Automation in Construction

View full text Add to dashboard Cite

Section: Existing Road Digitalizitionmentioning

confidence: 99%

Building digital twins of existing highways using map data based on engineering expertise

Feng

Ma²,

Broyd³

et al. 2022

Automation in Construction

View full text Add to dashboard Cite

“…The organizing component moves in a fixed way over the image to extract every pixel of the image object. Unsurprisingly, the organizing components do not apply to images containing directional pixels [15].…”

Section: Figure 1 General Procedures Of Road Identificationmentioning

confidence: 99%

Road Identification Through Efficient Edge Segmentation Based on Morphological Operations

Rani¹,

Majety²,

Pittala³

et al. 2021

View full text Add to dashboard Cite

Road identification from high-precision images is important to programmed mapping, urban planning, and updating geographic information system (GIS) databases. Manual identification of roads is slow, costly, and prone to errors. Therefore, it is a hot topic among remote sensing experts to develop programmed techniques for road identification from satellite images. The main challenge lies in the variation of width and surface contents between roads. This paper presents a road identification and extraction strategy for satellite images. The strategy, denoted as ESMIRMO, recognizes roads in satellite images through edge segmentation, using morphological operations. Specifically, morphological operations were employed to enhance the quality of the original image, laying a good basis for accurate road detection. Next, edge segmentation was adopted to detect the road in the original image accurately. After that, the proposed strategy was compared with traditional methods. The comparison shows that our strategy could identify roads from satellite images more accurately than traditional methods, and overcome many of their limitations. Overall, our strategy manages to enhance the quality of satellite images, pinpoint roads in the enhanced images, and provide drivers and repairers with real-time information on road conditions.

show abstract

“…Scholars at home and abroad have done a lot of research on LIDAR data processing. For example, Liao Lei et al [1] used an object-oriented classification method for changes in plants, houses, etc. in a certain regional geographical national situation monitoring, and processed point cloud laser sensor elevation and first and last echo data.…”

Section: Introductionmentioning

confidence: 99%

Rapid identification and analysis of vegetation information based on onboard LIDAR point cloud data

Fu,

Zheng,

Qian

et al. 2023

Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023)

View full text Add to dashboard Cite

Color Component–Based Road Feature Extraction from Airborne Lidar and Imaging Data Sets

Cited by 7 publications

References 22 publications

Building digital twins of existing highways using map data based on engineering expertise

Building digital twins of existing highways using map data based on engineering expertise

Road Identification Through Efficient Edge Segmentation Based on Morphological Operations

Rapid identification and analysis of vegetation information based on onboard LIDAR point cloud data

Contact Info

Product

Resources

About