Room semantics inference using random forest and relational graph convolutional networks: A case study of research building-Reference-Cited by-同舟云学术

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Room semantics inference using random forest and relational graph convolutional networks: A case study of research building

Published:2020-08-19 Issue:1 Volume:25 Page:71-111
ISSN:1361-1682
Container-title:Transactions in GIS
language:en
Short-container-title:Transactions in GIS

Author:

Hu Xuke¹^ORCID,Fan Hongchao²^ORCID,Noskov Alexey¹,Wang Zhiyong³,Zipf Alexander¹,Gu Fuqiang⁴^ORCID,Shang Jianga⁵

Affiliation:

1. Institute of Geography Heidelberg University Heidelberg Germany

2. Department of Civil and Environmental Engineering Norges Technk‐Naturvitenskapelige Universitet (NTNU) Trondheim Norway

3. Human Geography and Spatial Planning Utrecht University Utrecht Netherlands

4. Department of Computer Science National University of Singapore Singapore Singapore

5. School of Geography and Information Engineering China University of Geosciences Wuhan China

Publisher

Wiley

Subject

General Earth and Planetary Sciences

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/tgis.12664

Reference47 articles.

1. Improved Automatic Analysis of Architectural Floor Plans

3. Automatic Room Segmentation From Unstructured 3-D Data of Indoor Environments

4. 3D semantic parsing of large‐scale indoor spaces;Armeni I.;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016

5. What can we learn from 38,000 rooms? Reasoning about unexplored space in indoor environments

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3. CB-SAGE: A novel centrality based graph neural network for floor plan classification;Engineering Applications of Artificial Intelligence;2023-11

4. Automated Generation of Room Usage Semantics from Point Cloud Data;ISPRS International Journal of Geo-Information;2023-10-17

5. Relational graph convolutional networks: a closer look;PeerJ Computer Science;2022-11-02

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