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.

翻译：本文介绍了一种实时地理空间智能工作流，用于大规模图像分析及北极多年冻土特征的细粒度分割。分析中使用了极高分辨率（0.5米）商业卫星影像。为实现实时预测，本工作流采用了轻量级深度学习实例分割模型SparseInst，该模型引入并使用实例激活图来精确定位图像场景中的目标位置。实验结果表明，与流行的Mask-RCNN模型相比，该模型能在更快的推理速度下实现更高的预测精度。