Document Type : Original Research Paper
Authors
1 Department of Surveying and Geoinformatics Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
2 Department of Educational and Curriculum Management and Planning, Faculty of Psychology and Educational Sciences, Shiraz University, Shiraz, Iran
Abstract
Background and Objectives: Surveying and geomatics are moving from an instrument-centered profession toward a connected geospatial information discipline in which measurement, positioning, Earth observation, three-dimensional modelling, databases, web services, cloud computing and artificial intelligence are increasingly combined within the same workflow. This transformation does not reduce the importance of geodetic reference systems, field observations or uncertainty; rather, it changes the context in which these foundations are applied. Undergraduate programs therefore face a curriculum-design problem: adding every emerging technology can produce an overloaded list of software, while preserving a mainly traditional syllabus can leave graduates unable to automate, integrate and communicate modern geospatial data. This study develops an evidence-informed competency framework for the “future surveyor” and converts it into a four-year learning roadmap. The objectives were to identify competencies that should remain common to most geomatics graduates, separate foundational capabilities from advanced and specialist capabilities, define prerequisite relationships between them, and propose project outputs through which students can demonstrate learning in a professional portfolio.
Methods: A targeted narrative review and competency-mapping procedure was used. The evidence base combined professional and educational documents from international surveying and geospatial organizations, standards and implementation guidance for positional accuracy, geospatial interoperability and three-dimensional information, official technical documentation for widely used open and cloud geospatial technologies, and recent peer-reviewed studies on geospatial artificial intelligence and digital twins. Sources were retained when they supported at least one of four functions: defining enduring professional foundations, documenting current digital transformation, specifying interoperable technical practice, or describing an emerging capability with broad relevance to geomatics. Competencies were coded into conceptual groups and compared using three criteria: foundational importance to professional identity, breadth of applicability across employment and research settings, and strategic relevance to future geospatial workflows. Dependencies were then examined so that advanced capabilities were not placed before their mathematical, geodetic, data-management or programming prerequisites. The synthesis produced five connected competency layers and an eight-semester progression, with each stage linked to an assessable project or portfolio artefact.
Findings: The resulting framework contains five layers. The first comprises surveying measurement, geodesy, coordinate reference systems, adjustment, uncertainty, satellite positioning and quality assurance. The second comprises geographic information systems, programming, data models, spatial databases and reproducible automation. The third connects ground, airborne and spaceborne observation through remote sensing, unmanned aerial photogrammetry, laser scanning, point clouds and three-dimensional modelling. The fourth introduces machine learning, deep learning, geospatial artificial intelligence, web application programming interfaces, cloud-native processing, web mapping, building information modelling integration and digital twins. The fifth is cross-cutting and includes standards, ethics, data governance, documentation, version control, reproducibility, teamwork and communication. The roadmap therefore begins with measurement, mathematics, introductory programming and geographic information systems; progresses through geodesy, satellite positioning, spatial databases and Earth observation; and only then moves to advanced three-dimensional processing, machine learning, web and cloud systems, and integrated GeoAI.
Conclusion: A future-ready surveyor is best understood as a geospatial system professional who can preserve measurement traceability while moving data through acquisition, validation, storage, transformation, analysis, modelling and delivery. The proposed curriculum therefore does not replace classical surveying with coding or artificial intelligence. It treats geodetic reference, accuracy and uncertainty as the control layer on which digital competence is built, and it treats programming as a means of making workflows transparent, scalable and reproducible rather than as an end in itself. The most important curricular shift is from teaching isolated applications toward teaching transferable concepts and complete workflows. A graduate should be able to explain why a coordinate is trustworthy, how a dataset is structured, how an analysis can be reproduced, how an interface can exchange the result, and where model or data limitations affect decisions. The framework is intentionally adaptable because labor markets, professional regulation, equipment access and institutional capacity differ between countries. Its main limitation is that it is a synthesis framework rather than an empirical survey of one national labor market, so local validation with employers, professional bodies, students and accreditation requirements remains necessary before implementation.
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