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INSTRUCTORS: 
Behnam Jamali
Halil Ceylan
Steven Loo
Amit Gajurel

Purpose and Background

AI-Enhanced Unmanned Aerial Systems for Transportation Safety and Traffic Management (17 minutes)

This presentation explores the integration of artificial intelligence with unmanned aerial systems (UAS) to enhance transportation safety and traffic management. It delves into how AI-driven UAS can autonomously monitor traffic conditions, identify hazards, and improve incident response times. Attendees will learn about the latest advancements in AI technologies that are being applied to UAS for real-time data analysis and decision-making. The session is ideal for engineers interested in the intersection of AI and transportation systems.

Airfield Distress Detection Using YOLOV8 on Small Uncrewed Aircraft System Collected Data (15 minutes)

This session focuses on the application of the YOLOV8 deep learning model for detecting airfield distress using data collected from small uncrewed aircraft systems (sUAS). The presentation will cover the methodology of deploying YOLOV8 for accurate and efficient identification of pavement defects. Engineers will gain insights into how AI-powered sUAS can revolutionize airfield maintenance by providing rapid and precise assessments. The session is tailored for professionals working in airfield infrastructure and maintenance.

The View from Above and Below: Houston Public Works Unmanned Aerial Vehicles Data Collection Program (19 minutes)

This presentation highlights the innovative use of unmanned aerial vehicles (UAVs) by Houston Public Works for comprehensive data collection and infrastructure monitoring. It showcases how UAVs are employed to gather critical data from both aerial and ground perspectives, improving the accuracy of infrastructure assessments. Attendees will learn about the operational strategies, data processing techniques, and benefits of integrating UAVs into public works projects. This session is designed for engineers and public works professionals interested in UAV applications for urban infrastructure.

Use of Synthetic Aperture Radar and Uncrewed Aerial Vehicle for Assessing Pavement Condition (18 minutes)

This session explores the combined use of synthetic aperture radar (SAR) and uncrewed aerial vehicles (UAVs) for assessing pavement conditions. It covers how SAR technology, when mounted on UAVs, can provide detailed insights into pavement health, even under challenging conditions like heavy traffic or adverse weather. Engineers will learn about the advantages of this approach, including its ability to detect subsurface issues that are not visible through traditional methods. This presentation is ideal for those involved in pavement management and infrastructure monitoring.

Learning Outcomes and Session Benefits

Upon completion of this course, you will be able to:

• List the key components and technologies involved in AI-enhanced unmanned aerial systems used for transportation safety and traffic management.
• Explain how YOLOV8 algorithms are utilized to detect airfield distress from data collected by small uncrewed aircraft systems.
• Describe the data collection process used by Houston Public Works with unmanned aerial vehicles, including its benefits and challenges.
• Discuss the application of synthetic aperture radar combined with uncrewed aerial vehicles in assessing pavement conditions and its advantages over traditional methods.

Assessment of Learning Outcomes

Learning outcomes are assessed and achieved through passing a 10 multiple choice question post-test with at least a 70%.

Who Should Attend?

• Transportation engineers
• Transportation planners
• Traffic engineers
• Highway engineers
• Materials engineers
• Construction engineers

How to Earn Your PDHs and Receive Your Certificate of Completion

This course is worth 1 PDHs. To receive your certificate of completion, you will need to attend the live session and/or watch the recording(s) and complete the post-session survey. If the course is taken On-Demand, there will also be a 10 multiple choice question post-test.

View Important Policies and System Requirements for this course