An ensemble learning framework for generating high-resolution regional DEMs considering geographical zoning

Abstract

The current digital elevation model super-resolution (DEM SR) methods are unstable in regions with significant spatial heterogeneity. To address this issue, this study proposes a regional DEM SR method based on an ensemble learning strategy (ELSR). Specifically, we first classified geographical regions into 10 zones based on their terrestrial geomorphologic conditions to reduce spatial heterogeneity; we then integrated the global terrain features with local geographical zoning for terrain modeling; finally, based on ensemble learning theory, we integrated the advantages of different networks to improve the stability of the generated results. The approach was tested for 46,242 km² in Sichuan, China. The total accuracy of the regional DEM (stage 3) improved by 2.791 % compared with that of the super-resolution convolutional neural network (SRCNN); the accuracy of the geographical zoning strategy results (stage 2) increased by 1.966 %, and that of the baseline network results (stage 1) increased by 0.950 %. Specifically, the improvement in each stage compared with the previous stage was 110.105 % (in stage 2) and 41.963 % (in stage 3). Additionally, the accuracy of the 10 terrestrial geomorphologic classes improved by at least 2.000 %. In summary, the strategy proposed herein is effective for improving regional DEM resolution, with an improvement in relative accuracy related to terrain relief. This study creatively integrated geographical zoning and ensemble learning ideas to generate a stable, high-resolution regional DEM.