Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features
This addresses the need for efficient monitoring of Arctic permafrost changes, which is critical for climate research and environmental management, but it is incremental as it builds on existing segmentation methods.
The paper tackles the problem of real-time, high-resolution mapping and segmentation of Arctic permafrost features by introducing a GeoAI workflow using a lightweight deep learning model, achieving better accuracy and faster inference speed than Mask-RCNN.
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.