The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVIII-M-6-2025
https://doi.org/10.5194/isprs-archives-XLVIII-M-6-2025-381-2025
https://doi.org/10.5194/isprs-archives-XLVIII-M-6-2025-381-2025
19 May 2025
 | 19 May 2025

Integrating Photogrammetric 3D City Models and CityGML Data into Augmented Reality for Enhanced Urban Planning and Cadastre Management

Salih Yalcin, Bilal Erkek, and Ekrem Ayyildiz

Keywords: Augmented Reality, CityGML, 3D Cadastre, Mobile AR Applications, Land Registry, Spatial Analysis

Abstract. In an era of rapid urbanization, three-dimensional (3D) city models have emerged as crucial tools for managing infrastructure, planning new developments, and maintaining accurate cadastral records. Recent advances in photogrammetry and geographic information systems (GIS) have allowed us to generate incredibly detailed representations of urban spaces. The General Directorate of Land Registry and Cadastre has played a pivotal role in creating 3D city models by capturing aerial imagery and converting it into CityGML-based solid and architectural models. Although CityGML offers a standardized framework for storing and sharing city data, its potential can be further magnified by integrating immersive technologies such as Augmented Reality (AR). This paper presents a mobile AR application, developed with Google’s ARCore, that displays CityGML-based building data at street level, allowing for real-time visualization and interaction. Our pipeline simplifies raw CityGML data, ensuring optimal rendering while preserving essential details. Through effective georeferencing and advanced pose estimation, these digital models are accurately overlaid on real-world scenes, enabling government agencies, urban planners, and cadastral managers to analyze and make informed decisions in situated places. Beyond planning and cadastre, this AR system could benefit broader fields such as environmental monitoring and public infrastructure management. We conclude by discussing future steps, including expanded data layers (e.g., utility networks) and performance optimizations for large-scale city models, thus highlighting the transformative role of AR in shaping next-generation urban environments.

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