A graph deep learning approach for urban building grouping-Reference-Cited by-同舟云学术

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A graph deep learning approach for urban building grouping

Published:2020-12-29 Issue: Volume: Page:1-24
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Yan Xiongfeng¹^ORCID,Ai Tinghua²^ORCID,Yang Min²^ORCID,Tong Xiaohua¹,Liu Qian³

Affiliation:

1. College of Surveying and Geo-Informatics, Tongji University, Shanghai, China

2. School of Resource and Environmental Sciences, Wuhan University, Wuhan, China

3. School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2020.1856195

Reference51 articles.

1. Amalgamation in cartographic generalization using Kohonen's feature nets

2. Performance of shape indices and classification schemes for characterising perceptual shape complexity of building footprints in GIS

3. Geometric Deep Learning: Going beyond Euclidean data

4. Proximity-based grouping of buildings in urban blocks: a comparison of four algorithms

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1. Reasoning cartographic knowledge in deep learning-based map generalization with explainable AI;International Journal of Geographical Information Science;2024-06-20

2. A graph-based neural network approach to integrate multi-source data for urban building function classification;Computers, Environment and Urban Systems;2024-06

3. Interactive Optimization for Cartographic Aggregation of Building Features;Computer Graphics Forum;2024-06

4. Artificial intelligence studies in cartography: a review and synthesis of methods, applications, and ethics;Cartography and Geographic Information Science;2024-01-16

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