Abstract
Abstract. The microtopography associated with ice wedge polygons (IWPs) governs the Arctic ecosystem from local to regional scales due to the impacts on the flow and storage of water and therefore, vegetation and carbon. Increasing subsurface temperatures in Arctic permafrost landscapes cause differential ground settlements followed by a series of adverse microtopographic transitions at sub decadal scale. The entire Arctic has been imaged at 0.5 m or finer resolution by commercial satellite sensors. Dramatic microtopographic transformation of low-centered into high-centered IWPs can be identified using sub-meter resolution commercial satellite imagery. In this exploratory study, we have employed a Deep Learning (DL)-based object detection and semantic segmentation method named the Mask R-CNN to automatically map IWPs from commercial satellite imagery. Different tundra vegetation types have distinct spectral, spatial, textural characteristics, which in turn decide the semantics of overlying IWPs. Landscape complexity translates to the image complexity, affecting DL model performances. Scarcity of labelled training images, inadequate training samples for some types of tundra and class imbalance stand as other key challenges in this study. We implemented image augmentation methods to introduce variety in the training data and trained models separately for tundra types. Augmentation methods show promising results but the models with separate tundra types seem to suffer from the lack of annotated data.
Highlights
A network of polygonal pattern appears in the tundra due to the cracking and subsequent development of ice wedges
The main goal of this study is to explore the potential of augmentation methods on top of a state-of-the-art Deep Learning (DL) CNN method (Mask R-CNN) to characterize the tundra icewedge polygon landscape as well as to assess the change in the model performance when trained with separate tundra types
Mapping ice wedge polygons from large satellite imagery takes a lot of computational resources as well as a lot of annotated images
Summary
A network of polygonal pattern appears in the tundra due to the cracking and subsequent development of ice wedges. Previous studies show promising results found by implementing the implementation of deep learning convolutional neural networks with commercial satellite imagery (Zhang et al 2018) (Witharana et al 2020). We retrained the Mask R-CNN model with different augmentation methods using our dataset so that the model can be used for the detection and segmentation of the ice wedge polygons. We trained Mask R-CNN models for different tundra types. The main goal of this study is to explore the potential of augmentation methods on top of a state-of-the-art DL CNN method (Mask R-CNN) to characterize the tundra icewedge polygon landscape as well as to assess the change in the model performance when trained with separate tundra types
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