Skip to main content
SpringerLink
Log in

Graph-Based 3D Building Semantic Segmentation for Sustainability Analysis

  • Published:
Journal of Geovisualization and Spatial Analysis Aims and scope Submit manuscript

Abstract

A graph-based method is proposed to segment the 3D building models into semantically independent components. For each building, we first create a graph (N, E) in which the nodes N represent the surface of the 3D building model and the edges E standard for the shared lines between two surface nodes. Then, the graph is simplified by aggregating the connected coplanar surfaces. Next, the articulation points of the simplified graph are detected and removed literality to segment the graph into biconnected components. The semantic attributes of each component are detected according to its geometry features and spatial relationship with others. Finally, the building components with semantic and geometry information are used to simulate the city sustainability process such as energy consumption. According to the experimental results, the proposed method can effectively extract the semantic data from the LoD3/LoD2 building models for sustainability simulation tools such as EnergyPlus.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  • Bloch T, Sacks R (2018) Comparing machine learning and rule-based inferencing for semantic enrichment of BIM models. Autom Constr 91:256–272

    Article  Google Scholar 

  • Du S, Luo L, Cao K, Shu M (2016) Extracting building patterns with multilevel graph partition and building grouping. ISPRS J Photogramm Remote Sens 122:81–96

    Article  Google Scholar 

  • Fathi A, Saen RF (2018) A novel bidirectional network data envelopment analysis model for evaluating sustainability of distributive supply chains of transport companies. J Clean Prod 184:696–708

    Article  Google Scholar 

  • Günther M, Wiemann T, Albrecht S, Hertzberg J (2017) Model-based furniture recognition for building semantic object maps. Artif Intell 247:336–351

    Article  Google Scholar 

  • He X, Zhang X, Xin Q (2018) Recognition of building group patterns in topographic maps based on graph partitioning and random forest. ISPRS J Photogramm Remote Sens 136:26–40

    Article  Google Scholar 

  • Heitzler M, Lam JC, Hackl J et al (2017) GPU-accelerated rendering methods to visually analyze large-scale disaster simulation data. J Geovisualization Spat Anal 1:3

    Article  Google Scholar 

  • Hopcroft J, Tarjan R (1973) Algorithm 447: efficient algorithms for graph manipulation. Commun ACM 16(6):372–378. https://doi.org/10.1145/362248.362272

    Article  Google Scholar 

  • Kunze C, Hecht R (2015) Semantic enrichment of building data with volunteered geographic information to improve mappings of dwelling units and population. Comput Environ Urban Syst 53:4–18

    Article  Google Scholar 

  • Lior N, Kim D (2018) Quantitative sustainability analysis of water desalination – a didactic example for reverse osmosis. Desalination 431:157–170

    Article  Google Scholar 

  • Rahul TM, Verma A (2018) Sustainability analysis of pedestrian and cycling infrastructure – a case study for Bangalore. Case Stud Transp Policy

  • Serna A, Gerrikagoitia JK, Bernabé U, Ruiz T (2017) Sustainability analysis on Urban Mobility based on Social Media content. Transp Res Proc 24:1–8

    Article  Google Scholar 

  • Shen Y, Chen N, Li W, Li C, Guo R (2019) Distortion visualization techniques for 3D coherent sets: A case study of 3D building property units. Comput Environ Urban Syst 78:101382

    Article  Google Scholar 

  • Sikder SK, Behnisch M, Herold H et al (2019) Geospatial analysis of building structures in megacity Dhaka: the use of spatial statistics for promoting data-driven decision-making. J Geovisualization Spat Anal 3:7

    Article  Google Scholar 

  • Ślusarczyk G, Łachwa A, Palacz W, Strug B, Paszyńska A, Grabska E (2017) An extended hierarchical graph-based building model for design and engineering problems. Autom Constr 74:95–102

    Article  Google Scholar 

  • Tajbakhsh A, Hassini E (2018) Evaluating sustainability performance in fossil-fuel power plants using a two-stage data envelopment analysis. Energy Econ 74:154–178

    Article  Google Scholar 

  • Thiemann F, Sester M (2004) Segmentation of buildings for 3D-generalisation. Proceedings of Workshop on generalisation and multiple representation, https://www.ikg.uni-hannover.de/fileadmin/ikg/Forschung/publications/paper-v1.pdf. Accessed 2019-12-10

  • Vassoney E, Mochet AM, Comoglio C (2017) Use of multicriteria analysis (MCA) for sustainable hydropower planning and management. J Environ Manag 196:48–55

    Article  Google Scholar 

  • Wang K, Siebers P-O, Robinson D (2017) Towards generalized co-simulation of urban energy systems. Proc Eng 198:366–374

    Article  Google Scholar 

  • Wate P, Coors V (2015) 3D data models for urban energy simulation. Energy Procedia 78:3372–3377

    Article  Google Scholar 

  • Wong JK-W, Kuan K-L (2014) Implementing ‘BEAM Plus’ for BIM-based sustainability analysis. Autom Constr 44:163–175

    Article  Google Scholar 

  • Xiong X, Adan A, Akinci B, Huber D (2013) Automatic creation of semantically rich 3D building models from laser scanner data. Autom Constr 31:325–337

    Article  Google Scholar 

  • Zhang C, Mao B (2016) 3D building models segmentation based on K-means++ cluster analysis. Int Arch Photogramm Remote Sens Spat Inf Sci XLII-2/W2. 11th 3D Geoinfo Conference, 20–21 October 2016, Athens, Greece

Download references

Funding

This research was funded by the National Natural Science Foundation of China (41671457) and Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (16KJA170003).

Author information

Authors and Affiliations

  1. College of Information Engineering, Collaborative Innovation Centre for Modern Grain Circulation and Safety, Jiangsu Key Laboratory of Grain Big-data Mining, Nanjing University of Finance & Economics, Nanjing, 210003, Jiangsu, China

    Bo Mao

  2. School of Electrical and Automation Engineering, Jiangsu Maritime Institute, Nanjing, 210000, Jiangsu, China

    Bingchan Li

Authors
  1. Bo Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bingchan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, Bo Mao; funding acquisition, Bo Mao; methodology, Bo Mao and Bingchan Li; software, Bingchan Li; visualization, Bo Mao; writing—original draft, Bo Mao; writing—review and editing, Bingchan Li.

Corresponding author

Correspondence to Bo Mao.

Ethics declarations

Conflicts of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mao, B., Li, B. Graph-Based 3D Building Semantic Segmentation for Sustainability Analysis. J geovis spat anal 4, 4 (2020). https://doi.org/10.1007/s41651-019-0045-y

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s41651-019-0045-y

Keywords

Search

Navigation