Current estimates of stand volume for South Korean forests are mostly derived from expensive field data. Techniques that allow reducing the amount of ground data with reliable accuracy would decrease the cost and time. The fifth National Forest Inventory (NFI) has been conducted annually for all forest areas in South Korea from 2006 to 2010 and using these data we can make a model for estimating the stand volume of forests. The purpose of this study is to test deep learning whether it is available for measurement of stand volume with satellite imageries and geospatial information. The spatial distribution of the stand volume of South Korean forests was predicted with the convolutional neural networks (CNNs) algorithm. NFI data were randomly sampled for training from 90% to 10%, with 10% decrement, and the rest of the area was estimated using satellite imagery and geospatial information. Consequently, we found that the error rate of total stand volume was <5  %   when using over 17% of NFI data for training (R²  =  0.96). We identified that using CNNs model based on satellite imageries and geospatial information is considered to be suitable for estimating the national level of stand volume. This study is meaningful in that we (1) estimated the stand volume using a deep learning algorithm with high accuracy compare with previous studies, (2) identified the minimum training rate of the CNNs model to estimate the stand volume of South Korean forest, and (3) identified the effect of diameter class on error hotspots in stand volume estimates through clustering analysis.