DEM Super-Resolution with EfficientNetV2
This addresses the scarcity of high-resolution DEMs for climate change monitoring and environmental modeling, but appears incremental as it applies an existing neural network architecture to a specific domain task.
The paper tackles the problem of generating high-resolution Digital Elevation Models (DEMs) from low-resolution versions, proposing an EfficientNetV2-based model that increases spatial resolution up to 16 times without additional data.
Efficient climate change monitoring and modeling rely on high-quality geospatial and environmental datasets. Due to limitations in technical capabilities or resources, the acquisition of high-quality data for many environmental disciplines is costly. Digital Elevation Model (DEM) datasets are such examples whereas their low-resolution versions are widely available, high-resolution ones are scarce. In an effort to rectify this problem, we propose and assess an EfficientNetV2 based model. The proposed model increases the spatial resolution of DEMs up to 16times without additional information.