Despite the widespread use of machine learning(ML)models for geospatial applications,adaptations to imbalanced multitemporal land cover(LC)datasets remain underexplored.For over two dec-ades,studies have predominantly...Despite the widespread use of machine learning(ML)models for geospatial applications,adaptations to imbalanced multitemporal land cover(LC)datasets remain underexplored.For over two dec-ades,studies have predominantly trained ML models on a single interval of LC data to model changes,with detriments of imbal-anced training datasets managed through manual manipulations.Therefore,this study proposes and implements an ML-spatial sam-ple weighting(ML-SSW)approach to leverage available multitem-poral LC data while adjusting sample influence to reflect recency of change occurrence and class-level spatial pattern measures to enable data-driven LC change modeling.Random Forest(RF),Neural Network(NN),and Extreme Gradient Boosting Machine(XGB)models are trained under the ML-SSW strategy on three study areas located in British Columbia,Canada.The RF-SSW,NN-SSW,and XGB-SSW models forecasted more realistic changes across multiple timesteps with fewer errors than baseline configurations.The presented methodology provides a step toward establishing spatialized cost-sensitive learning strategies and extending classical ML models to multitemporal LC datasets.展开更多
With the advancement of Artificial Intelligence(Al)technologies and accumulation of big Earth data,Deep Learning(DL)has become an important method to discover patterns and understand Earth science processes in the pas...With the advancement of Artificial Intelligence(Al)technologies and accumulation of big Earth data,Deep Learning(DL)has become an important method to discover patterns and understand Earth science processes in the past several years.While successful in many Earth science areas,Al/DL applications are often challenging for computing devices.In recent years,Graphics Processing Unit(GPU)devices have been leveraged to speed up Al/DL applications,yet computational performance still poses a major barrier for DL-based Earth science applications.To address these computational challenges,we selected five existing sample Earth science Al applications,revised the DL-based models/algorithms,and tested the performance of multiple GPU computing platforms to support the applications.Application softwarepackages,performance comparisonsacross different platforms,along with other results,are summarized.This article can help understand how various Al/ML Earth science applications can be supported by GPU computing and help researchers in the Earth science domain better adopt GPU computing(such as supermicro,GPU clusters,and cloud computing-based)for their Al/ML applications,and to optimize their science applications to better leverage the computing device.展开更多
基金supported by the Natural Sciences and Engineering Research Council of Canada[RGPIN-2023-04052].
文摘Despite the widespread use of machine learning(ML)models for geospatial applications,adaptations to imbalanced multitemporal land cover(LC)datasets remain underexplored.For over two dec-ades,studies have predominantly trained ML models on a single interval of LC data to model changes,with detriments of imbal-anced training datasets managed through manual manipulations.Therefore,this study proposes and implements an ML-spatial sam-ple weighting(ML-SSW)approach to leverage available multitem-poral LC data while adjusting sample influence to reflect recency of change occurrence and class-level spatial pattern measures to enable data-driven LC change modeling.Random Forest(RF),Neural Network(NN),and Extreme Gradient Boosting Machine(XGB)models are trained under the ML-SSW strategy on three study areas located in British Columbia,Canada.The RF-SSW,NN-SSW,and XGB-SSW models forecasted more realistic changes across multiple timesteps with fewer errors than baseline configurations.The presented methodology provides a step toward establishing spatialized cost-sensitive learning strategies and extending classical ML models to multitemporal LC datasets.
基金supported by NSF F I/UCRC(1841520),NASA Goddard CISTO,and NASA AIST programs.
文摘With the advancement of Artificial Intelligence(Al)technologies and accumulation of big Earth data,Deep Learning(DL)has become an important method to discover patterns and understand Earth science processes in the past several years.While successful in many Earth science areas,Al/DL applications are often challenging for computing devices.In recent years,Graphics Processing Unit(GPU)devices have been leveraged to speed up Al/DL applications,yet computational performance still poses a major barrier for DL-based Earth science applications.To address these computational challenges,we selected five existing sample Earth science Al applications,revised the DL-based models/algorithms,and tested the performance of multiple GPU computing platforms to support the applications.Application softwarepackages,performance comparisonsacross different platforms,along with other results,are summarized.This article can help understand how various Al/ML Earth science applications can be supported by GPU computing and help researchers in the Earth science domain better adopt GPU computing(such as supermicro,GPU clusters,and cloud computing-based)for their Al/ML applications,and to optimize their science applications to better leverage the computing device.
