Automated workflow enhances data quality in photogrammetry and GIS, suggesting increased accessibility for researchers.
Efficient photogrammetric data processing and seamless integration with Geographic Information Systems (GIS) are crucial for large-scale digital heritage and scientific research. By leveraging pre-documented processes and AI-generated scripts, CINDAQ developed an automated photogrammetry workflow designed to enhance efficiency while ensuring consistent data quality. This system not only streamlines image-to-model processing but also integrates directly with a GIS-based Single Source of Truth (SSOT), enabling real-time spatial data updates and improved metadata management. By automating model generation, linking photogrammetric datasets to spatial layers, and enforcing standardized metadata structures, we have significantly reduced processing time and enhanced data accessibility for researchers. Additionally, the system lowers the technical barriers to entry, allowing non-specialists to engage with photogrammetry tools, thereby democratizing access to 3D modeling and spatial analysis. By simplifying workflows and reducing manual effort, this approach enables large-scale model production, increasing scientific collaboration and accelerating discoveries. The findings highlight a scalable, reproducible strategy for bridging photogrammetry and GIS, ultimately providing a structured, efficient, and inclusive digital archive for subterranean and underwater research, which can be replicated to other study areas.
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Fortin et al. (2025) studied this question.
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