Automating Infrastructure Surveying: A Framework for Geometric Measurements and Compliance Assessment Using Point Cloud Data
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912367336488960 |
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| author | Ghafourian, Amin Lee, Andrew Gao, Dechen Beer, Tyler Yen, Kin Soltani, Iman |
| author_facet | Ghafourian, Amin Lee, Andrew Gao, Dechen Beer, Tyler Yen, Kin Soltani, Iman |
| contents | Automation can play a prominent role in improving efficiency, accuracy, and scalability in infrastructure surveying and assessing construction and compliance standards. This paper presents a framework for automation of geometric measurements and compliance assessment using point cloud data. The proposed approach integrates deep learning-based detection and segmentation, in conjunction with geometric and signal processing techniques, to automate surveying tasks. As a proof of concept, we apply this framework to automatically evaluate the compliance of curb ramps with the Americans with Disabilities Act (ADA), demonstrating the utility of point cloud data in survey automation. The method leverages a newly collected, large annotated dataset of curb ramps, made publicly available as part of this work, to facilitate robust model training and evaluation. Experimental results, including comparison with manual field measurements of several ramps, validate the accuracy and reliability of the proposed method, highlighting its potential to significantly reduce manual effort and improve consistency in infrastructure assessment. Beyond ADA compliance, the proposed framework lays the groundwork for broader applications in infrastructure surveying and automated construction evaluation, promoting wider adoption of point cloud data in these domains. The annotated database, manual ramp survey data, and developed algorithms are publicly available on the project's GitHub page: https://github.com/Soltanilara/SurveyAutomation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05752 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automating Infrastructure Surveying: A Framework for Geometric Measurements and Compliance Assessment Using Point Cloud Data Ghafourian, Amin Lee, Andrew Gao, Dechen Beer, Tyler Yen, Kin Soltani, Iman Computer Vision and Pattern Recognition Computers and Society Machine Learning Robotics Image and Video Processing Automation can play a prominent role in improving efficiency, accuracy, and scalability in infrastructure surveying and assessing construction and compliance standards. This paper presents a framework for automation of geometric measurements and compliance assessment using point cloud data. The proposed approach integrates deep learning-based detection and segmentation, in conjunction with geometric and signal processing techniques, to automate surveying tasks. As a proof of concept, we apply this framework to automatically evaluate the compliance of curb ramps with the Americans with Disabilities Act (ADA), demonstrating the utility of point cloud data in survey automation. The method leverages a newly collected, large annotated dataset of curb ramps, made publicly available as part of this work, to facilitate robust model training and evaluation. Experimental results, including comparison with manual field measurements of several ramps, validate the accuracy and reliability of the proposed method, highlighting its potential to significantly reduce manual effort and improve consistency in infrastructure assessment. Beyond ADA compliance, the proposed framework lays the groundwork for broader applications in infrastructure surveying and automated construction evaluation, promoting wider adoption of point cloud data in these domains. The annotated database, manual ramp survey data, and developed algorithms are publicly available on the project's GitHub page: https://github.com/Soltanilara/SurveyAutomation. |
| title | Automating Infrastructure Surveying: A Framework for Geometric Measurements and Compliance Assessment Using Point Cloud Data |
| topic | Computer Vision and Pattern Recognition Computers and Society Machine Learning Robotics Image and Video Processing |
| url | https://arxiv.org/abs/2505.05752 |