Published in Geomatics, 2021
This paper fine-tunes a U-Net model for LiDAR-based lane marking extraction, achieving high accuracy while enabling automated detection of worn-out markings.
Recommended citation: Patel, A., et al. Transfer Learning for LiDAR-Based Lane Marking Detection and Intensity Profile Generation. Geomatics 2021, 1, 287-309
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Published in Remote Sensing, 2021
This paper dives into lane marking extraction using LiDAR intensity normalization and deep learning, achieving higher accuracy for automated road monitoring.
Recommended citation: Patel, A., et al. (2020). Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds. Remote Sensing, 12(9), 1379.
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Published in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020
This paper improves lane marking extraction across asphalt and concrete using intensity normalization and deep learning.
Recommended citation: Patel, A., et al. (2020). LiDAR-based Lane Marking Extraction through Intensity Thresholding and Deep Learning Approaches: A Pavement-based Assessment. ISPRS Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Published in Smoky Mountains Computational Sciences and Engineering Conference, 2021
This paper leverages graph embeddings and machine learning to predict undiscovered links in a COVID-19 knowledge graph, improving biomedical literature discovery.
Recommended citation: Patel, A., et al. Finding Novel Links in COVID-19 Knowledge Graph Using Graph Embedding Techniques. In Smoky Mountains Computational Sciences and Engineering Conference (pp. 430-441). Cham: Springer International Publishing
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