Farmsar: fixing agricultural mislabels using sentinel-1 time series and autoencoders

HIGHLIGHTS

• who: Thomas Di Martino et al. from the Universitu00e9 Paris-Saclay, Rue Joliot Curie, Gif-sur-Yvette, France have published the research: FARMSAR: Fixing AgRicultural Mislabels Using Sentinel-1 Time Series and AutoencodeRs, in the Journal: (JOURNAL) of 15/Dec/2022
• what: To process and correct these errors the authors design a two-step methodology. The authors show a drastic decrease in the performance of supervised algorithms under critical conditions (smaller and larger amounts of introduced label errors) with Random Forest falling to 56% of correct relabels against 95% for the approach . The authors focus . . .
  

   

  

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