Vol. 37, issue 01, article # 6

Abstract:

Tundra lakes are an important indicator of climate change; therefore, the analysis of the dynamics of their size is of particular interest. This paper presents the results of using the U-Net convolutional neural network for tundra lakes segmentation in satellite optical images using Landsat data as an example. The comparative assessment of segmentation accuracy is performed for the original U-Net design and its modifications: U-Net++, Attention U-Net, and R2 U-Net, including with weights derived from a pre-trained VGG16 network. The segmentation accuracy is assessed based on the results of manual mapping of tundra lakes in northern Siberia. It is shown that more recent U-Net modifications do not provide a practically significant gain in segmentation accuracy, but increase the computational costs. A configuration based on the classic U-Net gives the best result in most cases (the average Soerens coefficient IoU = 0.88). The technique suggested and the resulting estimates can be used in analysis of modern climate trends.

Keywords:

tundra lakes, U-Net, Arctic, remote sensing, permafrost

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