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Using Sentinel-2 Image Time Series to map the State of Victoria, Australia

Abstract : Sentinel-2 satellites are now acquiring images of the entire Earth every five days from 10 to 60 m spatial resolution. The supervised classification of this new optical image time series allows the operational production of accurate land cover maps over large areas. In this paper, we investigate the use of one year of Sentinel-2 data to map the state of Victoria in Australia. In particular, we produce two land cover maps using the most established and advanced algorithms in time series classification: Random Forest (RF) and Temporal Convolutional Neural Network (TempCNN). To our knowledge, these are the first land cover maps at 10 m spatial resolution for an Australian state.
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https://hal.inrae.fr/hal-03187983
Contributor : Christopher Lallemant <>
Submitted on : Thursday, April 1, 2021 - 3:32:55 PM
Last modification on : Tuesday, September 7, 2021 - 3:44:42 PM

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Charlotte Pelletier, Ji Zehui, Olivier Hagolle, Morse-Mcnabb Elizabeth, Sheffield Kathryn, et al.. Using Sentinel-2 Image Time Series to map the State of Victoria, Australia. 2019 10TH INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTITEMPORAL REMOTE SENSING IMAGES (MULTITEMP), Aug 2019, Shanghai, China. ⟨hal-03187983⟩

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