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A Deep-Learning Approach to Detect and Classify Heavy-Duty Trucks in Satellite Images.

Xingwei Liu1, Yiqiao Li2, Langting Sizemore3

  • 1School of Information and Computer Science, University of California at Irvine, Irvine, CA 92697 USA.

IEEE Transactions on Intelligent Transportation Systems : a Publication of the IEEE Intelligent Transportation Systems Council
|July 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using satellite images and GIS data to detect heavy-duty trucks, crucial for supply chains. The framework helps assess environmental impacts and truck distribution in urban and port areas.

Keywords:
GISPort-side heavy-duty trucksdeep learningmodel ensembleobject detectionsatellite images

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Area of Science:

  • Environmental Science
  • Geographic Information Science
  • Computer Vision

Background:

  • Heavy-duty trucks are vital for the economy but negatively impact public health and the environment.
  • Accurate tracking of truck distribution is essential for environmental impact assessment and urban planning.

Purpose of the Study:

  • To develop a novel framework for detecting heavy-duty trucks and shipping containers using satellite imagery and GIS data.
  • To assess the environmental impact of heavy-duty trucks in port-adjacent communities and analyze truck density patterns.

Main Methods:

  • A modified CenterNet algorithm was used for detecting randomly oriented trucks in satellite images.
  • Ensembling with Mask RCNN enhanced detection accuracy.
  • Geographic Information System (GIS) data from OpenStreetMap refined the model's predictions.

Main Results:

  • The framework successfully captured the distribution of heavy-duty trucks and shipping containers in on-road and off-road locations.
  • Application in Southern California, including the Port of Los Angeles and Long Beach, provided insights into truck density and environmental impact.

Conclusions:

  • The developed framework offers a scalable solution for monitoring heavy-duty truck distribution and its environmental consequences.
  • This research has significant implications for environmental policy, urban planning, and future logistics studies.