Graph-Based 3D Building Semantic Segmentation for Sustainability Analysis

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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.

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

  2. 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

  3. 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

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

  5. 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

  6. 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

  7. Hopcroft J, Tarjan R (1973) Algorithm 447: efficient algorithms for graph manipulation. Commun ACM 16(6):372–378.

  8. 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

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

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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. Thiemann F, Sester M (2004) Segmentation of buildings for 3D-generalisation. Proceedings of Workshop on generalisation and multiple representation, Accessed 2019-12-10

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

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

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

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

  21. 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

  22. 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

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Author information

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.

Correspondence to Bo Mao.

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Mao, B., Li, B. Graph-Based 3D Building Semantic Segmentation for Sustainability Analysis. J geovis spat anal 4, 4 (2020).

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  • 3D building models
  • Graph analysis
  • Semantic segmentation
  • Sustainability visualization