Abstract
High-quality and high-productivity land cover data as a critical proxy are urgently needed for various communities. Although efforts in mapping projects had been made, various architectures to answer the challenges of Big Data of remote sensing are still needed. Therefore, this paper developed an improved U-Net model with weighted cross entropy (WCE) to map land covers. The accuracy was assessed by confusion matrix, and compared with the other two alternative cross-entropy loss functions. The comparisons highlighted an issue that unbalanced sample space of training dataset is a major cause of lower mapping accuracy. Also, the higher accuracy of U-Net with WCE implied its ability to handle the issue. This paper suggests an alternative solution for mapping land cover to address the challenges.
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