Facade geometry generation from low-resolution aerial photographs for building energy modeling

Cao, Jun; Metzmacher, Henning; O’Donnell, James; Frisch, Jérôme; Bazjanac, Vladimir; Kobbelt, Leif; Treeck, Christoph van

doi:10.1016/j.buildenv.2017.07.018

Cited by 27 publications

(14 citation statements)

References 26 publications

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“…The focus of this learning is to develop a better understanding of collaboration in a complex built environment. (Gianfrate et al 2017), (van Krugten et al 2016), (Broto 2015), (Cosmi et al 2015), (Dall'O' et al 2013), Head 2010 (Fonseca et al 2016), (Glackin and Dionisio 2016), (Dixon et al 2014), (Eames et al 2013), (Mills 2003 (Cao et al 2017), (Heidarinejad et al 2017), (R. Wu et al 2017), (Alwan 2016), (García Kerdan et al 2016), (Marasco and Kontokosta 2016), (Munarim and Ghisi 2016), (Bomberg, Gibson, and Zhang 2015), (De Lieto Vollaro et al 2015), (Dineen, Rogan, and Ó Gallachóir 2015), (Hsu 2015), (Fawcett and Killip 2014), (Wang et al 2014) (Mohareb et al 2017), (Roberti et al 2017), (Parker et al 2017), (Gupta and Gregg 2016), (Tianzhen Hong et al 2016), (Terés-Zubiaga et al 2016), (Yan et al 2015), (Rhodes et al 2015), (Tianzhen Hong et al 2014), …”

Section: Discussionmentioning

confidence: 99%

“…References Recording Unit -Pattern: scope detected Energy modelling process (Cao et al, 2017) an automatic geometry modelling procedure of existing building facades in order to recover their semantic structure for reuse in the BEM process. (Heidarinejad et al, 2017) a procedure to rapidly create urban scale reduced order building energy models (Wu et al, 2017) a method for a multi-objective and simultaneous optimisation of building energy systems and retrofit (Alwan, 2016) a systematic framework for maintenance and refurbishment in domestic housing sector for utilising BIM processes.…”

Section: Line Of Researc Hmentioning

confidence: 99%

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Transdisciplinarity in energy retrofit. A Conceptual Framework

Sibilla

Kurul

2020

Journal of Cleaner Production

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This study explores the role of Energy Retrofit (ER) in Low Carbon Transition (LCT). The literature recognises the need to move towards a transdisciplinary approach in ER, which encompasses multidisciplinarity and interdisciplinarity. However, the fragmentation between different disciplines remains a significant problem, mainly due to challenges associated with knowledge exchange across the allied disciplines that play a role in ER. The authors posit that ER projects has been conceptualised and implemented using a Systems perspective so that an integrated approach that is akin to transdisciplinarity could become commonplace. Against this background, the aim of this paper is to establish to what extent ER has been conceptualised as a System in the literature so that complexities can effectively be managed through a transdisciplinary approach. This work is based on a literature review of 136 peer-reviewed journal papers. The content analysis demonstrates that current research on transdisciplinarity in ER can be conceptualised in five categories and 15 lines of research. They are presented as a Conceptual Framework, which is this paper's main contribution to existing knowledge. It reveals the direction of innovation in ER for LCT, and is illustrated as a cognitive map. This map exposes the current fragmentation implicit in the literature, and proposes critical connections that need to be established for a transdisciplinary approach. It also shows that the discourse on LCT changed by moving beyond the building scale; and recognising the need to embrace disruptive and local technologies, and integrating the social and technical aspects of ER. Innovative technical solutions and robust information modelling approaches emerge as key vehicles towards making decisions that pay regard to the economic, social and technical factors and that empower the prosumers to play an active role in LCT.

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Section: Discussionmentioning

confidence: 99%

Section: Line Of Researc Hmentioning

confidence: 99%

Transdisciplinarity in energy retrofit. A Conceptual Framework

Sibilla

Kurul

2020

Journal of Cleaner Production

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“…[3]. Die Methoden reichen von der Rekonstruktion einer Oberfläche aus Bilddaten [4] oder Punktwolken [5] bis zum händischen Nachmodellieren bestehender Daten. Durch eine manuelle Nachmodellierung werden allerdings weitere subjektive Einflüsse in das Gebäudemodell eingebracht [3] [6].…”

Section: Stichworte: Gebäudesimulation; Bim; Ifc-basierte Gebäudesimuunclassified

Thermisch‐energetische Gebäudesimulation auf Basis eines Bauwerksinformationsmodells

et al. 2018

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Für eine Abschätzung des Heizwärmebedarfs von Gebäuden und Quartieren können thermisch‐energetische Simulationen eingesetzt werden. Grundlage dieser Simulationen sind geometrische und physikalische Gebäudemodelle. Die Erstellung des geometrischen Modells erfolgt in der Regel auf Basis von Bauplänen oder Vor‐Ort‐Begehungen, was mit einem großen Recherche‐ und Modellierungsaufwand verbunden ist. Spätere bauliche Veränderungen des Gebäudes müssen häufig manuell in das Modell eingearbeitet werden, was den Arbeitsaufwand zusätzlich erhöht. Das physikalische Modell stellt die Menge an Parametern und Randbedingungen dar, welche durch Materialeigenschaften, Lage und Umgebungseinflüsse gegeben sind. Die Verknüpfung beider Modelle wird innerhalb der entsprechenden Simulationssoftware realisiert und ist meist nicht in andere Softwareprodukte überführbar. Mithilfe des Building Information Modeling (BIM) können Simulationsdaten sowohl konsistent gespeichert als auch über Schnittstellen mit entsprechenden Anwendungen ausgetauscht werden. Hierfür wird eine Methode vorgestellt, die thermisch‐energetische Simulationen auf Basis des standardisierten Übergabeformats Industry Foundation Classes (IFC) inklusive anschließender Auswertungen ermöglicht. Dabei werden geometrische und physikalische Parameter direkt aus einem über den gesamten Lebenszyklus aktuellen Gebäudemodell extrahiert und an die Simulation übergeben. Dies beschleunigt den Simulationsprozess hinsichtlich der Gebäudemodellierung und nach späteren baulichen Veränderungen. Die erarbeitete Methode beruht hierbei auf einfachen Modellierungskonventionen bei der Erstellung des Bauwerksinformationsmodells und stellt eine vollständige Übertragbarkeit der Eingangs‐ und Ausgangswerte sicher.

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“…To address these issues, some recent efforts have focused on leveraging sensing technologies such as laser scanning and photogrammetry to capture as-is conditions of buildings and developing approaches that can use the captured data to automatically create as-is BEMs or as-is BIMs for energy analysis purposes [8,9,10,11]. According to the type of input data, these approaches can be roughly categorized into two groups: point cloudbased approaches and image-based approaches.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the accuracy loss of point cloud-based approaches due to noisy and missing data could be effectively addressed by image-based approaches [13,14,15]. In the context of as-is BEM creation, a recent work was conducted by Cao et al [11] who developed an approach to reconstruct building facades from low-resolution aerial images. Building facade components including walls, doors and windows can be recognized.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Constructing Semantic As-is Building Energy Models (BEMs) for Existing Buildings Using Digital Images

Ying¹,

Lu²,

Zhou³

et al. 2018

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Retrofits of existing buildings have great potential to reduce global energy consumption and greenhouse gas emissions. Energy modeling of existing buildings, which is commonly conducted to prioritize retrofit strategies, relies on as-is building energy models (BEMs) that represent actual conditions of buildings. Recent efforts have focused on leveraging sensing technologies such as laser scanning and photogrammetry to capture as-is conditions of buildings and developing automatic methods for creating BEMs using the captured data. However, the majority of these efforts are limited to reconstructing 3D facade geometries with poor semantic information for rough BEM use. To this end, this paper presents a framework for an image-based approach to construct complete and semantic as-is BEM geometry models for existing buildings. The framework consists of four modules: 1) the data capture module that collects digital images of building facades and interior spaces and relevant "placement" information for geometry definition; 2) the building surface geometry reconstruction module that recognizes main building components required by BEM and reconstructs their 3D surface geometries from captured images; 3) the semantic enrichment module that adds the required geometry-related semantic relationships among the reconstructed building elements and interior spaces; and 4) the BEM creation module that stores the semantic geometry model in IDF data model. This framework is expected to extend existing research by creating complete (i.e. include not only building facades but also interior spaces) and semantic-rich asis BEM geometry models.

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Facade geometry generation from low-resolution aerial photographs for building energy modeling

Cited by 27 publications

References 26 publications

Transdisciplinarity in energy retrofit. A Conceptual Framework

Transdisciplinarity in energy retrofit. A Conceptual Framework

Thermisch‐energetische Gebäudesimulation auf Basis eines Bauwerksinformationsmodells

A Framework for Constructing Semantic As-is Building Energy Models (BEMs) for Existing Buildings Using Digital Images

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