research-article

A Spatial and Adversarial Representation Learning Approach for Land Use Classification with POIs

Authors:

Liqiang NieAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 6

Article No.: 114, Pages 1 - 25

https://doi.org/10.1145/3627824

Published: 14 November 2023 Publication History

Abstract

Points-of-interests (POIs) have been proven to be indicative for sensing urban land use in numerous studies. However, recent progress mainly relies on spatial co-occurrence patterns among POI categories, which falls short in utilizing the rich semantic information embodied in POI hierarchical categories and in sensing the spatial distribution patterns of POIs at an individual zonal scale. In this context, we present a spatial and adversarial representation learning approach (SARL) for predicting land use of urban zones with POIs. SARL deeply mines the information from POIs from both spatial and categorical perspectives. Specifically, we first utilize a convolutional neural network to sense the spatial distribution patterns of POIs in each urban zone. We then leverage an autoencoder and an adversarial learning strategy to mine the POI categorical information in all hierarchical levels, which emphasizes the prominent and definitive POIs while preserves the overall POI hierarchical structures in each zone. Finally, we fuse these information from the two perspectives via a Wide & Deep network and carry out land use prediction with the fused embeddings. We conduct comprehensive experiments to validate the effectiveness of SARL in four European cities with real-world data. The results demonstrate that SARL substantially outperforms several competitive baselines.

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Cited By

Zhang DChen MHuang WGong YZhao KLarson K(2024)Exploring urban semanticsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/280(2533-2541)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/280
Jia HChen MHuang WZhao KGong YLarson K(2024)Learning hierarchy-enhanced POI category representations using disentangled mobility sequencesProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/231(2090-2098)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/231
Li ZHuang WZhao KYang MGong YChen MWooldridge MDy JNatarajan S(2024)Urban region embedding via multi-view contrastive predictionProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28718(8724-8732)Online publication date: 20-Feb-2024
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A Spatial and Adversarial Representation Learning Approach for Land Use Classification with POIs

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 6

December 2023

493 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3632517

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 14 November 2023

Online AM: 17 October 2023

Accepted: 17 September 2023

Revised: 19 August 2023

Received: 12 December 2022

Published in TIST Volume 14, Issue 6

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National Natural Science Foundation of China
Natural Science Foundation of Shandong Province of China
Young Scholars Program of Shandong University
Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources

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https://dl.acm.org/doi/10.24963/ijcai.2024/280
Jia HChen MHuang WZhao KGong YLarson K(2024)Learning hierarchy-enhanced POI category representations using disentangled mobility sequencesProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/231(2090-2098)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/231
Li ZHuang WZhao KYang MGong YChen MWooldridge MDy JNatarajan S(2024)Urban region embedding via multi-view contrastive predictionProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28718(8724-8732)Online publication date: 20-Feb-2024
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