Large-Scale Land Cover Mapping of Satellite Images Using Ensemble of Random Forests — IEEE Data Fusion Contest 2020 Track 1

This paper describes our approach in the land cover classification with low-resolution labels challenge of 2020 IEEE Data Fusion Contest. The challenge features the large-scale land cover mapping based on weakly annotated samples. We firstly refine the samples based on analyzing the class confusions and the confidence of the sample. Subsequently, an ensemble of random forests is trained for classification and finally the results are generated via soft voting of the classifiers. This approach achieves an average accuracy of 0.5676 on the test dataset and achieves the 4
^th
place in Track 1 of the Data Fusion Contest 2020.