Multi-Dimension Geospatial Feature Learning for Urban Region Function Recognition

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publicationPeoceedings
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages5832-5835
ISBN (electronic)978-1-6654-2792-0
Publication statusPublished - 2022

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2022-July

Conference

Title2022 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022)
LocationKuala Lumpur Convention Centre & Virtual
PlaceMalaysia
CityKuala Lumpur
Period17 - 22 July 2022

Abstract

Urban region function recognition plays a vital character in monitoring and managing the limited urban areas. Since urban functions are complex and full of social-economic properties, simply using remote sensing (RS) images equipped with physical and optical information cannot completely solve the classification task. On the other hand, with the development of mobile communication and the internet, the acquisition of geospatial big data (GBD) becomes possible. In this paper, we propose a Multi-dimension Feature Learning Model (MDFL) using high-dimensional GBD data in conjunction with RS images for urban region function recognition. When extracting multi-dimension features, our model considers the user-related information modeled by their activity, as well as the region-based information abstracted from the region graph. Furthermore, we propose a decision fusion network that integrates the decisions from several neural networks and machine learning classifiers, and the final decision is made considering both the visual cue from the RS images and the social information from the GBD data. Through quantitative evaluation, we demonstrate that our model achieves overall accuracy at 92.75%, outperforming the state-of-the-art by 10% percent.

Research Area(s)

  • decision fusion network, geospatial big data, multi-dimension feature extraction, Urban region function recognition

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Multi-Dimension Geospatial Feature Learning for Urban Region Function Recognition. / Xu, Wenjia; Wang, Jiuniu; Wu, Yirong.
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium: Peoceedings. Institute of Electrical and Electronics Engineers, Inc., 2022. p. 5832-5835 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review