Procedural modelling and generative AI for augmented contextual 3D visualisation of planned urban change
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Autor:in (Körperschaft)
Publikationsdatum
2026
Typ der Arbeit
Studiengang
Sammlung
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
International archives of the photogrammetry, remote sensing and spatial information sciences
Themenheft
DOI der Originalpublikation
Link
Zugehörige Forschungsdaten
Reihe / Serie
Reihennummer
Jahrgang / Band
L-4/W2-2026
Ausgabe / Nummer
Seiten / Dauer
221-228
Patentnummer
Verlag / Herausgebende Institution
Copernicus
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Street-level visualisations play an important role in urban street redesign by helping stakeholders and non-specialist audiences understand proposed spatial transformations. However, conventional image-editing workflows are often time-consuming, while purely image-based generative AI approaches provide limited control over object position, scale, and spatial relationships. This study presents a novel, highly automated workflow combining georeferenced street-level smartphone imagery, procedural 3D modelling, semantic image segmentation, and controlled generative AI to produce realistic visualisations of planned street transformations. Two-dimensional CAD drawings of planned changes are procedurally converted into simplified 3D models. Semantic masks are rendered from georeferenced camera viewpoints in the 3D model and combined with masks extracted from the street-level smartphone images. The resulting semantic and inpainting masks condition a Stable Diffusion XL-based inpainting model through segmentation and Canny-edge ControlNets. Text prompts control the overall visual appearance, while an IP-Adapter enables selected elements, such as vegetation and tree species, to be refined using reference images. The workflow was applied to two street redesign scenarios. The results show that planned elements can be generated at positions and with dimensions closely corresponding to the planning data, while different visual and seasonal variants can be explored without changing the spatial configuration. The method therefore provides a scalable approach for generating street-level visualisations while preserving geometric control.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
1682-1750
2194-9034
2194-9034
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
peer-reviewed
Open Access-Status
Diamond
Lizenz
Zitation
Uythoven, S., Wahbeh, W., Reibel, T., Nebiker, S., & Erath, A. (2026). Procedural modelling and generative AI for augmented contextual 3D visualisation of planned urban change. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, L-4/W2-2026, 221–228. https://doi.org/10.5194/isprs-archives-l-4-w2-2026-221-2026