Automating Infrastructure Surveying: A Framework for Geometric Measurements and Compliance Assessment Using Point Cloud Data
- URL: http://arxiv.org/abs/2505.05752v1
- Date: Fri, 09 May 2025 03:24:09 GMT
- Title: Automating Infrastructure Surveying: A Framework for Geometric Measurements and Compliance Assessment Using Point Cloud Data
- Authors: Amin Ghafourian, Andrew Lee, Dechen Gao, Tyler Beer, Kin Yen, Iman Soltani,
- Abstract summary: This paper presents a framework for automation of geometric measurements and compliance assessment using point cloud data.<n>As a proof of concept, we apply this framework to automatically evaluate the compliance of curb ramps with the Americans with Disabilities Act (ADA)<n>The proposed framework lays the groundwork for broader applications in infrastructure surveying and automated construction evaluation.
- Score: 1.0411051000480154
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
Related papers
- Test-time Offline Reinforcement Learning on Goal-related Experience [50.94457794664909]
Research in foundation models has shown that performance can be substantially improved through test-time training.<n>We propose a novel self-supervised data selection criterion, which selects transitions from an offline dataset according to their relevance to the current state.<n>Our goal-conditioned test-time training (GC-TTT) algorithm applies this routine in a receding-horizon fashion during evaluation, adapting the policy to the current trajectory as it is being rolled out.
arXiv Detail & Related papers (2025-07-24T21:11:39Z) - Textured As-Is BIM via GIS-informed Point Cloud Segmentation [0.0]
This paper presents a proof of concept for the automated generation of GIS-informed and BIM-ready as-is Building Information Models for railway projects.<n>The results show a high potential for cost savings and reveal the unemployed resources of freely accessible GIS data within.
arXiv Detail & Related papers (2024-11-28T04:13:08Z) - AutoSurvey: Large Language Models Can Automatically Write Surveys [77.0458309675818]
This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys.
Traditional survey paper creation faces challenges due to the vast volume and complexity of information.
Our contributions include a comprehensive solution to the survey problem, a reliable evaluation method, and experimental validation demonstrating AutoSurvey's effectiveness.
arXiv Detail & Related papers (2024-06-10T12:56:06Z) - Deep Learning-Based Point Cloud Registration: A Comprehensive Survey and Taxonomy [79.66031973540946]
We present a comprehensive survey and taxonomy on deep learning-based point cloud registration (DL-PCR)<n>For supervised DL-PCR methods, we organize the discussion based on key aspects, including the registration procedure.<n>We classify them into correspondence-based and correspondence-free approaches, depending on whether they require explicit identification of point-to-point correspondences.
arXiv Detail & Related papers (2024-04-22T02:05:15Z) - An Integrated Data Processing Framework for Pretraining Foundation Models [57.47845148721817]
Researchers and practitioners often have to manually curate datasets from difference sources.
We propose a data processing framework that integrates a Processing Module and an Analyzing Module.
The proposed framework is easy to use and highly flexible.
arXiv Detail & Related papers (2024-02-26T07:22:51Z) - UAS-based Automated Structural Inspection Path Planning via Visual Data
Analytics and Optimization [1.1496057626375067]
Unmanned Aerial Systems (UAS) have gained significant traction for their application in infrastructure inspections.
One of the core problems in this regard is electing an optimal automated flight path.
This paper presents an effective formulation for the path planning problem in the context of structural inspections.
arXiv Detail & Related papers (2023-12-22T23:07:20Z) - Automated spacing measurement of formwork system members with 3D point
cloud data [0.688204255655161]
The current way to measure the spacing between formwork system members is mostly done using manual measuring tools.
This research proposes a framework to measure the spacing of formwork system members using 3D point cloud data.
arXiv Detail & Related papers (2023-05-23T12:17:31Z) - Discover, Explanation, Improvement: An Automatic Slice Detection
Framework for Natural Language Processing [72.14557106085284]
slice detection models (SDM) automatically identify underperforming groups of datapoints.
This paper proposes a benchmark named "Discover, Explain, improve (DEIM)" for classification NLP tasks.
Our evaluation shows that Edisa can accurately select error-prone datapoints with informative semantic features.
arXiv Detail & Related papers (2022-11-08T19:00:00Z) - UPDesc: Unsupervised Point Descriptor Learning for Robust Registration [54.95201961399334]
UPDesc is an unsupervised method to learn point descriptors for robust point cloud registration.
We show that our learned descriptors yield superior performance over existing unsupervised methods.
arXiv Detail & Related papers (2021-08-05T17:11:08Z) - Attention-based Vehicle Self-Localization with HD Feature Maps [13.368212933272238]
We present a vehicle self-localization method using point-based deep neural networks.
Our approach processes measurements and point features, i.e. landmarks, from a high-definition digital map to infer the vehicle's pose.
arXiv Detail & Related papers (2021-07-16T09:25:25Z) - Automated Machine Learning Techniques for Data Streams [91.3755431537592]
This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time.
The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
arXiv Detail & Related papers (2021-06-14T11:42:46Z) - A Unified Architecture for Data-Driven Metadata Tagging of Building
Automation Systems [0.0]
This article presents a Unified Architecture for automated point tagging of Building Automation System data.
We propose a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS.
The proposed methodology correctly applied 85-90 percent and 70-75 percent of the tags in each of these test scenarios.
arXiv Detail & Related papers (2020-02-27T00:35:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.