![]() This function has a number of arguments for customizing function: We’ll use the _to_poly_geojson() function. Example 3 Follow-Up: Masking and Multimodal Datasetsįirst, we’ll work with just the first channel for the simplest case of going from footprints to polygons.Example 1 Follow-Up: Building a Reusable Class.Example 2 Follow-Up: Parallel Processing.Solaris Multimodal Preprocessing Library.Initialize a few more functions for scoring our results. ![]() Post-processing- binarize our masks and convert them to polygons.Specify our directories for post-processing.Create a csv file that lists our images and our masks for training and testing.Calculate some basic statistics for z-scoring (normalizing) our imagery.Dialate our masks to increase the size of our labels.Tile our masks and convert them to GeoTiffs.Specify our directories for pre processing.Mapping vehicles with solaris and the cowc dataset.Creating reference CSVs for model training and inference.Ground truth and prediction data formats.Using the solaris CLI to score model performance.Running a deep learning pipeline with the solaris CLI.Batch mask creation using the solaris CLI.Using the solaris CLI to make training masks.Training included SpaceNet models with the solaris Python API.Training your own custom model using solaris.Tiling imagery and labels using the solaris Python API.Creating training masks with the solaris python API.Converting model outputs to vector format using the Python API. ![]()
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