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http://dx.doi.org/10.7780/kjrs.2019.35.6.2.3

Automatic Classification by Land Use Category of National Level LULUCF Sector using Deep Learning Model  

Park, Jeong Mook (Human Resources Development Center for Convergence of Advanced Technologies in Forest Industry, Kangwon National University)
Sim, Woo Dam (Department of Forest Management, Kangwon National University)
Lee, Jung Soo (Department of Forest Management, Kangwon National University)
Publication Information
Korean Journal of Remote Sensing / v.35, no.6_2, 2019 , pp. 1053-1065 More about this Journal
Abstract
Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.
Keywords
LULUCF; National forest inventory; Forest Aerial Photography; Deep learning model; Sampling method;
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Times Cited By KSCI : 3  (Citation Analysis)
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