). The model was run with a batch size of 32 for 28 epochs based on a 3 run early stopping procedure on the reported validation accuracy. The 2180 images in our dataset are split into a training dataset for learning and validation dataset to test using a 70:30 ratio, respectively. Each epoch is refined based on an Adam optimizer and loss measured by a sparse categorical cross entropy.
To apply the model, the algorithm requires a 512 × 512 image input and creates a 512 × 512 image prediction containing 3 layers of probability , one for each of three categories: single-threaded, multi-threaded and background category. To limit potential edge effects in the resulting prediction, we export the Landsat imagery for Tensorflow as a series of 512 × 512 tiles with a 128 pixel overlap to keep the central 384 × 384 pixels for the resulting output .
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