Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features ...

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...

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Bibliographic Details
Main Authors: Li, Wenwen, Hsu, Chia-Yu, Wang, Sizhe, Witharana, Chandi, Liljedahl, Anna
Format: Report
Language:unknown
Published: arXiv 2023
Subjects:
Computer Vision and Pattern Recognition cs.CV
FOS Computer and information sciences
Arctic
permafrost
Online Access:https://dx.doi.org/10.48550/arxiv.2306.05341
https://arxiv.org/abs/2306.05341
Description
Summary: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. ...

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