Engineering pedagogical content knowledge for undergraduate engineering and technology programs: Accelerating graduates' preparedness for the 4IR geospatial industry

Surveying engineering technology (SET) and Geomatics (S/G) programs have significantly been impacted by advances of three-dimensional (3D) geospatial data acquisition technologies coupled with innovation in computational infrastructure over the past decade. Today, large-volume 3D data in the form of point clouds, meshes, or other representations, are frequently collected by sensors such as Light Detection and Ranging (LiDAR) and depth cameras for both industrial purposes and scientific investigations. Traditional surveying techniques are more often integrated with the emerging state-of-the-art geospatial technology and 3D data analytics. Evolving geospatial industry labor markets are challenging the traditional skillsets developed at conventional S/G programs at colleges. Yet, higher education graduates may still lack decision making and project application skills, and most importantly, the ability to apply the body of knowledge from their academic training in college courses to solve real-world problems and meet the skill challenges of the Fourth Industrial Revolution (4IR). To bridge the gap between theory and application of these relevant technologies for industry-ready graduates, hands-on exercises are developed and will be incorporated in a 300-level photogrammetry course for SET and Civil Engineering majors. This case study describes a project-based learning exercise that requires students to process point cloud data and develop spatial literacy skills. Point cloud processing experience ramps up critical geospatial skills through using Open3D, an open-source library that supports rapid visualization and processing of 3D data, and Open3D-ML, an extension of Open3D for 3D machine learning tasks. With a few lines of Python code, students will be presented with complete workflows such as surface registration, scene reconstruction, semantic point cloud segmentation, and object detection, thus gaining a better insight into these complex topics and how they are translated into easy-to-follow operations and applied on real 3D data. It is our hope that our efforts to combine theory, real-world applications, hands-on exercises, and publicly available resources will largely enable students to immerse themselves in the learning, promote their intrinsic motivation to master S/G curricula, and empower them to become active data users and analysts in their professional career.