TY - JOUR
T1 - An Optimal GeoAI Workflow for Pan-Arctic Permafrost Feature Detection from High-Resolution Satellite Imagery
AU - Udawalpola, Mahendra R.
AU - Hasan, Amit
AU - Liljedahl, Anna
AU - Soliman, Aiman
AU - Terstriep, Jeffrey
AU - Witharana, Chandi
N1 - Publisher Copyright:
© 2022 American Society for Photogrammetry and Remote Sensing.
PY - 2022/3
Y1 - 2022/3
N2 - High-spatial-resolution satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. Knowledge discovery through artificial intelligence, big imagery, and high-performance computing (HPC) resources is just starting to be realized in Arctic permafrost science. We have developed a novel high-performance image-analysis frame-work—Mapping Application for Arctic Permafrost Land Environment (MAPLE)—that enables the integration of operational-scale GeoAI capabilities into Arctic permafrost modeling. Interoperability across heterogeneous HPC systems and optimal usage of computational resources are key design goals of MAPLE. We systematically compared the performances of four different MAPLE workflow designs on two HPC systems. Our experimental results on resource utilization, total time to completion, and overhead of the candidate designs suggest that the design of an optimal workflow largely depends on the HPC system architecture and underlying service-unit accounting model.
AB - High-spatial-resolution satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. Knowledge discovery through artificial intelligence, big imagery, and high-performance computing (HPC) resources is just starting to be realized in Arctic permafrost science. We have developed a novel high-performance image-analysis frame-work—Mapping Application for Arctic Permafrost Land Environment (MAPLE)—that enables the integration of operational-scale GeoAI capabilities into Arctic permafrost modeling. Interoperability across heterogeneous HPC systems and optimal usage of computational resources are key design goals of MAPLE. We systematically compared the performances of four different MAPLE workflow designs on two HPC systems. Our experimental results on resource utilization, total time to completion, and overhead of the candidate designs suggest that the design of an optimal workflow largely depends on the HPC system architecture and underlying service-unit accounting model.
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U2 - 10.14358/PERS.21-00059R2
DO - 10.14358/PERS.21-00059R2
M3 - Article
AN - SCOPUS:85134020571
SN - 0099-1112
VL - 88
SP - 181
EP - 188
JO - Photogrammetric Engineering and Remote Sensing
JF - Photogrammetric Engineering and Remote Sensing
IS - 3
ER -