globalchange  > 气候变化事实与影响
Scopus记录号: 2-s2.0-85060580707
论文题名:
Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models
作者: Abolt C.J.; Young M.H.; Atchley A.L.; Wilson C.J.
刊名: Cryosphere
ISSN: 19940416
出版年: 2019
卷: 13, 期:1
起始页码: 237
结束页码: 245
语种: 英语
英文摘要: We present a workflow for the rapid delineation and microtopographic characterization of ice wedge polygons within high-resolution digital elevation models. At the core of the workflow is a convolutional neural network used to detect pixels representing polygon boundaries. A watershed transformation is subsequently used to segment imagery into discrete polygons. Fast training times (<5) permit an iterative approach to improving skill as the routine is applied across broad landscapes. Results from study sites near Utqiavik (formerly Barrow) and Prudhoe Bay, Alaska, demonstrate robust performance in diverse tundra settings, with manual validations demonstrating 70-96% accuracy by area at the kilometer scale. The methodology permits precise, spatially extensive measurements of polygonal microtopography and trough network geometry. © Author(s) 2019.
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/122686
Appears in Collections:气候变化事实与影响

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作者单位: Department of Geological Sciences, University of Texas at Austin, Austin, TX, United States; Bureau of Economic Geology, University of Texas at Austin, Austin, TX, United States; Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, United States

Recommended Citation:
Abolt C.J.,Young M.H.,Atchley A.L.,et al. Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models[J]. Cryosphere,2019-01-01,13(1)
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