SAGC-A68: A space access graph dataset for the classification of spaces and space elements in apartment buildings

SAGC-A68: "a Space Access Graph dataset for the Classification of spaces and space elements in Apartment buildings"

Authors: "Amir Ziaee, Georg Suter, Laura Keiblinger"

Copyright: "Design Computing Group TU Wien, 2023"

Credits: "Design Computing Group TU Wien"

License: "GNU GENERAL PUBLIC LICENSE Version 3"

Version: "1.0.1"

Maintainer: "Amir Ziaee"

Email: "amir.ziaee@tuwien.ac.at"

Url: https://github.com/A2Amir/SAGC-A68

Acknowledgments: "The authors gratefully acknowledge support by Grant Austrian Science Fund (FWF): I 5171-N, Laura Keiblinger, and participants in the course '259.428-2021S Architectural Morphology' at TU Wien for data collection. "

Description: "The analysis of building models for usable area, building safety, and energy efficiency requires accurate classification data of spaces and space elements. To reduce input model preparation effort and errors, automated classification of spaces and space elements is desirable. Although existing space function classifiers use space adjacency or connectivity graphs as input, the application of Graph Deep Learning (GDL) to space layout element classification has not been extensively researched due to the lack of suitable datasets. To bridge this gap, we introduce a dataset named SAGC-A68, which comprises access graphs automatically generated from 68 digital 3D models of space layouts of apartment buildings designed or built between 1952 and 2019 in 13 countries. Each access graph contains nodes representing spaces and space elements and edges representing the connection between them. Nodes are uniquely identified and characterized by 16 features including “Position X”, “Position Y”, “Position Z”, “Width”, “Height”, “Depth”, “Area”, “Volume”, “Is_internal”, “Door_opening_quantity”, “Window_quantity”, “Max_door_width”,” Encloses_ws”, “Is_contained_in_ws”, ”bounding_box”, and “Label” (28 identified lables are shown in bold type in Table 1). Edges are identified by a unique ID and characterized by three features, including “Z_angle”, “Delta_z”, and “Length”. In total, the dataset comprises 4871 nodes and 4566 edges, including disconnected nodes representing shafts. It is suitable for developing GDL models for space element and space function classification in Building information modeling (BIM) authoring systems. "

Table 1: Space and space element class hierarchies and instance count in the SAGC-A668 dataset.