An Empirical Analysis of Remote Sensing Teaching Innovations in Postgraduate Built Environment Education

Although remote sensing is an important part of the curriculum for students of built environment, traditional classroom-based courses may not be able to keep up with the pace of modern geospatial technologies in the industry, and students are not equipped with the skills needed by professionals in the field. To solve this problem, an innovative teaching model was adopted in the teaching of MSc remote sensing course Remote Imaging and Processing (GES 713), which combined the advanced technology tools such as satellite data analysis and machine learning image processing with practical applications in true platforms. Students did not merely study theory but faced real-world data challenges that were directly related to industry and the local community and had them present a two-step presentation to industry and academic member – a first. This method was very successful as shown by feedback from 40 students in a questionnaire. The collaborative, tech-driven approach proved to be much more effective (Mean = 4.50) than traditional lectures, and students indicated that that exposure to advanced machine learning and satellite datasets helped to increase their confidence to enter the geospatial workforce (Mean = 4.38). They strongly suggested that other advanced STEM courses should implement this community engaged design (Mean = 4.45), and that transforming intricate technical information for two completely different audiences effectively honed their flexibility in communicating (Mean = 4.05). In conclusion, this research demonstrates that integration of contemporary technological innovation and community-based, experiential learning is very effective in preparing postgraduate students to be job-ready and provides an effective and practical approach to deliver geospatial education in the future.

Remote sensing, teaching, innovation, student feedback

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