This paper presents an end-to-end, agent-driven pipeline for the LoD3 reconstruction of facade openings in 3D city models, producing directly usable CityGML-conform outputs. In contrast to existing approaches that rely on supervised semantic segmentation and therefore require large amounts of manually annotated training data, the proposed method employs a zero-shot segmentation strategy. This substantially reduces the annotation effort while still achieving strong performance in our benchmark on the eTRIMS dataset. A further key contribution is the enforcement of correct partonomic hierarchies, thereby producing CityGML-conform LoD3 building models. Beyond the reconstruction pipeline itself, this work also introduces a novel evaluation metric for facade reconstruction, termed Facade Feature Distance (FFD). Unlike conventional metrics such as mIoU or FRDS, which assess similarity primarily through pixel-wise overlap, FFD measures distance in a high-level feature space derived from a vision transformer. In doing so, it captures both semantic correctness and architectural layout, providing a more suitable assessment of facade reconstruction quality. The proposed pipeline and evaluation strategy together offer a practical and scalable contribution toward the automated generation and analysis of semantically enriched 3D city models. The developed code is published at: https://github.com/hcu-cml/citydb-SVI2LoD3-ai.
Elmehdi Kanna, Lukas Arzoumanidis, Huynh Duc An Son Nguyen et al.· 0 citations
Abstract. Semantically rich 3D city models play a vital role in a variety of applications, such as urban planning. Enhancing these models with currently unavailable attributes, such as building storey numbers, can unlock new opportunities to address pressing challenges, including sustainable urban development. In this work, we present an end-to-end pipeline for the automatic estimation of the number of storeys to semantically enrich 3D city models. We employ volunteered geographic information street-view imagery from Mapillary, using a COCO-pretrained object detection model to identify windows in fac¸ade images as key visual indicators for inferring building storey counts. Our detection pipeline, based on the YOLOv3 architecture, estimates storey numbers using an ensemble of clustering methods including Gaussian Mixtures and DBSCAN and enables the automatic augmentation of CityGMLbased 3D city models by filling in missing attributes. This enrichment supports advanced applications, such as assessing buildingscale energy demand, evaluating vertical urban growth patterns or population density estimations. We validated the feasibility of our approach with unfiltered Mapillary and applied it to a district in the city of Heidelberg, Germany. The paper also includes a detailed discussion of learning process quality, integration workflows, and visualization of the enriched 3D city model. The developed code is available at: https://github.com/hcu-cml/citydb-buildingstoreys-ai.
Lukas Arzoumanidis, Al Maimun As Samee, Elmehdi Kanna et al.· ISPRS Annals of the Photogra...· 0 citations
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