Using Sentinel-2 image time series to map the State of Victoria, Australia

Charlotte Pelletier, Zehui Ji, Olivier Hagolle, Elizabeth Morse-McNabb, Kathryn Sheffield, Geoffrey I. Webb, Francois Petitjean

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp 2019)
EditorsFrancesca Bovolo, Sicong Liu
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781728146157
ISBN (Print)9781728146164
Publication statusPublished - 2019
EventInternational Workshop on the Analysis of Multitemporal Remote Sensing Images 2019 - Shanghai, China
Duration: 5 Aug 20197 Aug 2019
Conference number: 10th


ConferenceInternational Workshop on the Analysis of Multitemporal Remote Sensing Images 2019
Abbreviated titleMultiTemp 2019
Internet address


  • land cover map
  • Random Forests
  • Sentinel-2 images
  • Temporal Convolutional Neural Networks
  • time series

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