Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic
  Permafrost Features
Wenwen Li Chia-Yu Hsu Sizhe Wang Chandi Witharana Anna Liljedahl
This paper introduces a real-time GeoAI workflow for large-scale image
analysis and the segmentation of Arctic permafrost features at a
fine-granularity. Very high-resolution (0.5m) commercial imagery is used in
this analysis. To achieve real-time prediction, our workflow employs a
lightweight, deep learning-based instance segmentation model, SparseInst, which
introduces and uses Instance Activation Maps to accurately locate the position
of objects within the image scene. Experimental results show that the model can
achieve better accuracy of prediction at a much faster inference speed than the
popular Mask-RCNN model.