Figure 1
Cross platform software stack.
Figure 2
Step 1: model training. GDAL is employed to read the input images (on-the-fly or in-memory). TensorFlow performs the training of the network, and export the trained weights in the SavedModel format.
Figure 3
Step 2: inference using a trained model, to generate a synthetic high-resolution geospatial image from one entire low-resolution remote sensing image. Weights of the trained model is stored in the SavedModel format.
Figure 4
Results obtained all around the earth using the available pre-trained model. Left: original Sentinel-2 images (in RGB colors), right: synthetic high-resolution images (2.5 meters spacing) generated with the software.