AI-driven solutions for reality capture data; [KI-gesteuerte Lösungen für Reality-Capture-Daten]

Abstract

This article examines the use of artificial intelligence for processing and interpreting reality-capture data. Reality capture technologies such as laser scanning, photogrammetry, mobile mapping and imaging systems can generate large volumes of detailed spatial information. The resulting point clouds, images and three-dimensional models contain valuable information, but processing these datasets manually can require considerable time and specialist effort. AI-driven methods can help automate parts of the workflow by identifying objects, classifying features, detecting patterns and extracting useful information from complex datasets. The article focuses on the potential of artificial intelligence to make reality-capture data more usable in practical geospatial applications. Automated interpretation can support surveying, construction, infrastructure management, asset documentation and digital-twin development. The quality of AI results depends strongly on the quality and diversity of training data as well as on the characteristics of the captured information. Different environments, sensors and acquisition conditions can create challenges for reliable classification. Human expertise therefore remains important for checking results and handling situations that automated systems cannot interpret confidently. The combination of AI and reality capture can nevertheless reduce repetitive work and allow professionals to focus more on analysis and decision-making. The article highlights the growing connection between geospatial data acquisition and intelligent data processing. By combining detailed three-dimensional information with machine learning techniques, organizations can move toward faster and more scalable workflows while maintaining the spatial accuracy required for professional applications. This perspective is also relevant for organizations that are planning long-term digital transformation and need practical information rather than technology in isolation. It shows how geospatial methods can connect operational needs with clearer, evidence-based decisions.