Build2Vec: Building Representation in Vector Space

• URL: http://arxiv.org/abs/2007.00740v1
• Date: Wed, 1 Jul 2020 20:39:39 GMT
• Title: Build2Vec: Building Representation in Vector Space
• Authors: Mahmoud Abdelrahman, Adrian Chong, and Clayton Miller
• Abstract summary: We represent a methodology of a graph embeddings algorithm that is used to transform labeled property graphs. We used node2Vec with biased random walks to extract semantic similarities between different building components. A case study implementation is conducted on a net-zero-energy building located at the National University of Singapore.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we represent a methodology of a graph embeddings algorithm that is used to transform labeled property graphs obtained from a Building Information Model (BIM). Industrial Foundation Classes (IFC) is a standard schema for BIM, which is utilized to convert the building data into a graph representation. We used node2Vec with biased random walks to extract semantic similarities between different building components and represent them in a multi-dimensional vector space. A case study implementation is conducted on a net-zero-energy building located at the National University of Singapore (SDE4). This approach shows promising machine learning applications in capturing the semantic relations and similarities of different building objects, more specifically, spatial and spatio-temporal data.

Related papers

• Open-Vocabulary Octree-Graph for 3D Scene Understanding [54.11828083068082]
  Octree-Graph is a novel scene representation for open-vocabulary 3D scene understanding. An adaptive-octree structure is developed that stores semantics and depicts the occupancy of an object adjustably according to its shape.
  arXiv  Detail & Related papers  (2024-11-25T10:14:10Z)
• SSIG: A Visually-Guided Graph Edit Distance for Floor Plan Similarity [11.09257948735229]
  We propose a simple yet effective metric that measures structural similarity between visual instances of architectural floor plans. In this paper, an effective evaluation metric for judging the structural similarity of floor plans, coined S SIG, is proposed based on both image and graph distances.
  arXiv  Detail & Related papers  (2023-09-08T14:28:28Z)
• A Hybrid Semantic-Geometric Approach for Clutter-Resistant Floorplan Generation from Building Point Clouds [2.0859227544921874]
  This research proposes a hybrid semantic-geometric approach for clutter-resistant floorplan generation from laser-scanned building point clouds. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.
  arXiv  Detail & Related papers  (2023-05-15T20:08:43Z)
• FoldingNet Autoencoder model to create a geospatial grouping of CityGML building dataset [0.0]
  This study uses 'FoldingNet,' a 3D autoencoder, to generate the latent representation of each building from the LoD 2 CityGML dataset. The efficacy of the embeddings is analyzed by dataset reconstruction, latent spread visualization, and hierarchical clustering methods. A geospatial model is created to iteratively find the geographical groupings of buildings.
  arXiv  Detail & Related papers  (2022-12-28T17:16:23Z)
• Learning Implicit Feature Alignment Function for Semantic Segmentation [51.36809814890326]
  Implicit Feature Alignment function (IFA) is inspired by the rapidly expanding topic of implicit neural representations. We show that IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions. Our method can be combined with improvement on various architectures, and it achieves state-of-the-art accuracy trade-off on common benchmarks.
  arXiv  Detail & Related papers  (2022-06-17T09:40:14Z)
• GraphDCA -- a Framework for Node Distribution Comparison in Real and Synthetic Graphs [72.51835626235368]
  We argue that when comparing two graphs, the distribution of node structural features is more informative than global graph statistics. We present GraphDCA - a framework for evaluating similarity between graphs based on the alignment of their respective node representation sets.
  arXiv  Detail & Related papers  (2022-02-08T14:19:19Z)
• Towards Similarity-Aware Time-Series Classification [51.2400839966489]
  We study time-series classification (TSC), a fundamental task of time-series data mining. We propose Similarity-Aware Time-Series Classification (SimTSC), a framework that models similarity information with graph neural networks (GNNs)
  arXiv  Detail & Related papers  (2022-01-05T02:14:57Z)
• BuildingNet: Learning to Label 3D Buildings [19.641000866952815]
  BuildingNet: (a) large-scale 3D building models whose exteriors consistently labeled, (b) a neural network that labels building analyzing and structural relations of their geometric primitives. The dataset covers categories, such as houses, churches, skyscrapers, town halls and castles.
  arXiv  Detail & Related papers  (2021-10-11T01:45:26Z)
• Finding Geometric Models by Clustering in the Consensus Space [61.65661010039768]
  We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. We present a number of applications where the use of multiple geometric models improves accuracy. These include pose estimation from multiple generalized homographies; trajectory estimation of fast-moving objects.
  arXiv  Detail & Related papers  (2021-03-25T14:35:07Z)
• Graph-Based Generative Representation Learning of Semantically and Behaviorally Augmented Floorplans [12.488287536032747]
  We present a floorplan embedding technique that uses an attributed graph to represent the geometric information as well as design semantics and behavioral features of the inhabitants as node and edge attributes. A Long Short-Term Memory (LSTM) Variational Autoencoder (VAE) architecture is proposed and trained to embed attributed graphs as vectors in a continuous space. A user study is conducted to evaluate the coupling of similar floorplans retrieved from the embedding space with respect to a given input.
  arXiv  Detail & Related papers  (2020-12-08T20:51:56Z)
• Spatial Pyramid Based Graph Reasoning for Semantic Segmentation [67.47159595239798]
  We apply graph convolution into the semantic segmentation task and propose an improved Laplacian. The graph reasoning is directly performed in the original feature space organized as a spatial pyramid. We achieve comparable performance with advantages in computational and memory overhead.
  arXiv  Detail & Related papers  (2020-03-23T12:28:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.