
Mohit GuptaArizona State University | ASU · Department of Civil, Environmental and Sustainable Engineering
Mohit Gupta
Doctor of Philosophy
Machine Learning | Built Environment | Annotation efficiency
About
9
Publications
5,637
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
27
Citations
Publications
Publications (9)
Bike lane width is critical information for civil infrastructure planers to promote city sustainability, health, and urban livability. This research presents a feasibility investigation to develop a workflow for the recognition of bike lane markings using city-scale point cloud data. City-scale point cloud data was collected employing a backpack mo...
Any building designed for human occupancy needs to be visually comfortable. Glare from daylight is one of the main causes of visual discomfort. Glare perception is evaluated by empirical glare models either by photometric measurements or by lighting simulations. This study explores an alternate solution that implements deep learning methods to deve...
Imagery is a standard modality of visual data capture on construction sites for documenting construction progress. Assimilating the data from multiple disjointed 2D images into a single 3D format enhances visualization, and scene understanding and increases the data usability for tasks like quantity estimation and progress tracking. Two popular met...
Current computer vision methods for symbol detection in piping and instrumentation diagrams (P&IDs) face limitations due to the manual data annotation resources they require. This paper introduces a versatile two-stage symbol detection pipeline that optimizes efficiency by (1) labeling only data samples with minimal cumulative informational redunda...
Building owners are working on converting their legacy documentation 2D floor plans into digital 3D representations, but the manual process is labor-intensive and time-consuming. In this paper, deep learning is leveraged to automate the process. This automation requires interoperability between artificial neural networks and prevailing 3D modeling...
For successfully training neural networks, developers often require large and carefully labelled datasets. However, gathering such high-quality data is often time-consuming and prohibitively expensive. Thus, synthetic data are used for developing AI (Artificial Intelligence) /ML (Machine Learning) models because their generation is comparatively fa...
Generating digital 3D buildings models from
scratch is time-consuming and labor-intensive. In this paper, we present an automated detection process leveraging computer vision and the information available in 2D drawings to reduce 3D modeling time. The recognition system is limited to walls and has two parts: (1) Image classification on walls by Res...