33rd Annual Conference of the International Group for Lean Construction (IGLC 33) - Osaka and Kyoto, Japan - 2025

https://doi.org/10.24928/2025/0168

Time-lapse Image Processing for Estimating Construction Equipment Emissions

Vimal Bharathi1, Kepong Hong2 & Jochen Teizer3

1Ph.D. Student, Vimal Bharathi, Department of Civil and Mechanical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark, [email protected], https://orcid.org/0009-0003-9592-0626
2Ph.D. Student, Vimal Bharathi, Department of Civil and Mechanical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark, [email protected], https://orcid.org/0009-0005-0018-0579
3Professor, Department of Civil and Mechanical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark, [email protected], https://orcid.org/0000-0001-8071-895X

Abstract

Non-road mobile machinery (NRMM) contributes significantly to environmental pollution. As part of lean construction methodologies, construction equipment emissions can be monitored remotely by analyzing its working states over time. This offers a viable alternative to expensive, simplified, or portable emission monitoring systems. However, the complex and dynamic aspects of construction sites make it difficult for humans to follow equipment activity. This leads to manual and time-consuming site resource management. This paper proposes automating the task of equipment emission monitoring as part of the more significant goals in lean construction by applying Artificial Intelligence (AI) to photography from a time-lapse camera that oversees a construction site. A Deep Learning model is trained to detect several pieces of equipment on the construction site. The proposed method further tracks the resources to predict whether it is operating between consecutive timestamps. Observing the activity state of the equipment, the respective emission values of the equipment are calculated according to historical and calibrated benchmarks. The results demonstrate a promising method applying existing surveillance technology that, while it also automates parts of construction site operations monitoring of lean schedules, simplifies the process of tracking emissions with respect to construction equipment operations on construction sites. Further work is suggested to acquire higher temporal resolution data and improve data labeling, thereby performing semantic segmentation of equipment for better tracking results for lean construction purposes.

Keywords

Computer vision, Deep Learning, Object detection and tracking, Time-lapse camera, Lean construction equipment operation, Greenhouse gas emissions.

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Reference in APA 7th edition format:

Bharathi, V., Hong, K. & Teizer, J.. (2025). Time-lapse Image Processing for Estimating Construction Equipment Emissions. In Seppänen, O., Koskela, L., & Murata , K. (Eds.), Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33) (pp. 1102–1113). https://doi.org/10.24928/2025/0168

Shortened reference for use in IGLC papers:

Bharathi, V., Hong, K. & Teizer, J.. (2025). Time-lapse Image Processing for Estimating Construction Equipment Emissions. IGLC33. https://doi.org/10.24928/2025/0168

 

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