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Joint Deep Learning: A novel framework for urban land cover and land use classification

Research output: Contribution to conference - Without ISBN/ISSN Conference paper

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Publication date22/07/2020
Number of pages5
Pages1-5
Original languageEnglish
EventGISRUK 2020 -
Duration: 21/07/202023/07/2020
http://london.gisruk.org/index.php

Conference

ConferenceGISRUK 2020
Period21/07/2023/07/20
Internet address

Abstract

The majority of existing research on urban land cover and land use (LULC) classification from remotely sensed imagery is to differentiate at a specific level (either land cover (LC) or land use (LU)) and at a specific scale. The spatial and hierarchical relationships between LC and LU were not considered in the previous paradigm. This paper proposed Joint Deep Learning as a novel framework for urban LULC classification. Three state-of-the-art methods were compared under the framework, including JDL, SS-JDL and a newly proposed Cross GAN. Experimental results demonstrate the superiority of Cross GAN in terms of classification accuracy and robustness.