Automating Infrastructure Surveying: A Framework for Geometric Measurements and Compliance Assessment Using Point Cloud Data

2025-05-09

AI Summary

To address the low automation, insufficient accuracy, and poor scalability of geometric measurement and compliance assessment in infrastructure inspection, this paper proposes the first end-to-end point cloud analysis framework, integrating deep learning (for object detection and instance segmentation), point cloud geometric modeling, signal processing, and a 3D rule engine. Focusing on ADA-compliant curb ramps as a representative use case, we construct and publicly release the first large-scale, manually annotated curb ramp point cloud dataset, thereby advancing domain standardization and reproducible research. Experimental results on real-world scans demonstrate that our framework achieves mean absolute errors of less than 2.1 cm for critical ramp geometric parameters—including slope, width, and transition zone—matching manual surveying accuracy. This significantly improves assessment consistency, operational efficiency, and scalability while reducing inspection costs.

Technology Category

• Computer Vision: Scene Analysis & Understanding
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Automating geometric measurements in infrastructure surveying using point clouds
• Assessing compliance with standards like ADA via automated curb ramp evaluation
• Reducing manual effort in infrastructure assessment through deep learning techniques

Innovation

• Deep learning-based detection and segmentation
• Geometric and signal processing techniques
• Public annotated dataset for model training