Real-time GeoAI for high-resolution mapping and segmentation of arctic permafrost features: the case of ice-wedge polygons
Real-time GeoAI for high-resolution mapping and segmentation of arctic permafrost features: the case of ice-wedge polygons
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DOI：
10.1145/3557918.3565869
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Wenwen Li;Chia-Yu Hsu;Sizhe Wang;C. Witharana;A. Liljedahl
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作者：
Wenwen Li;Chia-Yu Hsu;Sizhe Wang;C. Witharana;A. Liljedahl
本文介绍了用于大规模图像分析和北极永久冻土特征细粒度分割的实时 GeoAI 工作流程。此分析中使用了非常高分辨率 (0.5m) 的商业图像。为了实现实时预测，我们的工作流程采用了基于深度学习的轻量级实例分割模型 SparseInst，该模型引入并使用实例激活图来准确定位图像场景中对象的位置。实验结果表明，该模型能够以比流行的 Mask-RCNN 模型更快的推理速度实现更好的预测精度。
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.