https://repos.hcu-hamburg.de:443/handle/hcu/15 2026-06-21T17:52:17Z 2026-06-21T17:52:17Z Weidlich, Ingo https://repos.hcu-hamburg.de:443/handle/hcu/1253 2026-06-20T00:02:22Z 2026-06-19T07:51:46Z

Title: On the theoretical axial resistance of district heating joints Authors: Weidlich, Ingo Abstract: District heating networks consist of different pipe segments, straight pipes, pipe bends and T-branches, which are connected on site. A large number of connections must therefore be made in order to create a branched district heating network. In this paper the state of art in pipeline engineering is reviewed and analyzed for the determination of the theoretical axial resistance of district heating joints, since an anchor effect of the joints was expected. Based on the findings a calculation method for the expected resistance is proposed and in a parameter study the deviations to a straight pipe are shown for typical joint applications.

2026-06-19T07:51:46Z Lottis, Dennis https://repos.hcu-hamburg.de:443/handle/hcu/1255 2026-06-20T00:05:29Z 2026-06-19T07:28:39Z

Title: Fibre optic and embedded sensing concept for long term monitoring of distrct heating pipes at the District-LAB Kassel Authors: Lottis, Dennis Abstract: District heating systems are expected to operate with lower temperatures and a higher share of renewable and volatile heat sources, which increases the need for experimental data on pipe soil interaction and insulation behaviour. This paper presents the concept and realisation of a full-scale district heating test section within the flexible heating grid of the District-LAB at Fraunhofer IEE in Kassel. The measurement setup combines distributed fibre optic temperature sensing on the pipe surface, in the bedding and in the surrounding soil with embedded temperature and humidity sensors in the PUR foam and soil moisture sensors close to the pipes. The paper focuses on the design, installation and initial functional checks of this integrated measurement concept, which provides a platform for future investigations of heat losses, insulation ageing and moisture processes under realistic and dynamic operating conditions.

2026-06-19T07:28:39Z Berger, Hilke Marit Herzog, Rico Kühn, Annika https://repos.hcu-hamburg.de:443/handle/hcu/1228 2026-06-11T00:08:30Z 2026-06-10T15:46:00Z

Title: Up: town: Collaborative Imagination of Resilient Urban Futures Through Serious Gaming Authors: Berger, Hilke Marit; Herzog, Rico; Kühn, Annika Abstract: Visions of urban futures are crucial for shaping social transformation. They should be collaboratively created and critically examined—yet current approaches often rely on quantitative prediction or scenario planning. Exploratory futuring techniques that engage diverse stakeholders and address the later-than-now of cities remain scarce. In this commentary, we reflect on up:town, a serious game that places participants in a fictional but realistic urban setting facing social, economic and ecological shocks. Players take on diverse roles and engage in participatory decision-making, confronting uncertainty and complexity together. Based on nearly 20 international gameplay rounds, we show how up:town (1) opens space for critical reflection, (2) surfaces existing ideas while enabling imaginative worldings and (3) supports participants in re:learning the craft of radical imagination. This experience demonstrates how serious gaming can bridge present challenges and future possibilities, offering a dynamic platform for collective reflection and the co-creation of alternative urban futures.

2026-06-10T15:46:00Z Arzoumanidis, Lukas As Samee, Al Maimun Kanna, Elmehdi Nguyen, Son Dehbi, Youness https://repos.hcu-hamburg.de:443/handle/hcu/1248 2026-06-18T13:39:23Z 2026-05-29T15:02:49Z

Title: Domain-Adaptive Object Detection for Enriching Semantic 3D City Models with Building Storeys from Street-View Images Authors: Arzoumanidis, Lukas; As Samee, Al Maimun; Kanna, Elmehdi; Nguyen, Son; Dehbi, Youness 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 façade 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 CityGML-based 3D city models by filling in missing attributes. This enrichment supports advanced applications, such as assessing building-scale 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.; Semantisch angereicherte 3D-Stadtmodelle spielen eine wichtige Rolle in zahlreichen Anwendungsbereichen, beispielsweise in der Stadtplanung. Die Erweiterung dieser Modelle um bislang fehlende Attribute, wie etwa die Anzahl der Gebäudegeschosse, eröffnet neue Möglichkeiten zur Bewältigung drängender Herausforderungen, darunter die nachhaltige Stadtentwicklung. In dieser Arbeit präsentieren wir eine End-to-End-Pipeline zur automatischen Schätzung der Geschossanzahl, um 3D-Stadtmodelle semantisch anzureichern. Hierfür nutzen wir freiwillig bereitgestellte geografische Straßenansichten von Mapillary sowie ein auf COCO vortrainiertes Modell zur Objekterkennung, um Fenster in Fassadenbildern als zentrale visuelle Indikatoren zur Ableitung der Geschossanzahl zu identifizieren. Unsere auf der YOLOv3-Architektur basierende Detektionspipeline schätzt die Anzahl der Geschosse mithilfe eines Ensembles aus Clustering-Methoden, darunter Gaussian Mixtures und DBSCAN, und ermöglicht die automatische Erweiterung von auf CityGML basierenden 3D-Stadtmodellen durch das Ergänzen fehlender Attribute. Diese Anreicherung unterstützt weiterführende Anwendungen, etwa die Bewertung des gebäudebezogenen Energiebedarfs, die Analyse vertikaler Stadtwachstumsmuster oder die Abschätzung der Bevölkerungsdichte. Wir validierten die Umsetzbarkeit unseres Ansatzes anhand ungefilterter Daten von Mapillary und wendeten ihn auf einen Stadtbezirk in Heidelberg an. Darüber hinaus enthält die Arbeit eine detaillierte Diskussion der Qualität des Lernprozesses, der Integrationsworkflows sowie der Visualisierung des angereicherten 3D-Stadtmodells. Der entwickelte Quellcode ist verfügbar unter: https://github.com/hcu-cml/citydb-buildingstoreys-ai.

2026-05-29T15:02:49Z