14-make-prediction-chips.py

This script applies our model to generate predictions at the pixel level for each image chip generated from 12-prediction-inputs.py. These prediction results are intended for visualization purposes only. Our model was designed to make predictions based on aggregated reflectance information from multiple pixels, not individual pixel, so consumers of this information should not attempt to draw any inference from the predictions generated by this script. For this step, it’s only necessary to run the function for ITV and ANA sites, as we are not deploying predictions for USGS sites to the web app.

usage: 14-make-prediction-chips.py [-h] [--data-src {itv,ana,usgs,usgsi}]
                                   [--cloud-thr CLOUD_THR]
                                   [--buffer-distance BUFFER_DISTANCE]
                                   [--mask-method1 {lulc,scl}]
                                   [--mask-method2 {mndwi,ndvi,""}]
                                   [--n-folds N_FOLDS] [--seed SEED]

Named Arguments

--data-src

Possible choices: itv, ana, usgs, usgsi

For which data source should this script run?

--cloud-thr

Percent of cloud cover acceptable in the Sentinel tile corresponding to the sample. If this threshold is surpassed, no Sentinel image chip will be collected for the sample.

Default: 80

--buffer-distance

Square search radius (in meters) to use for reflectance data aggregation. This determines the size of the image chip that will be extracted and processed.

Default: 500

--mask-method1

Possible choices: lulc, scl

Which data to use for masking (removing) non-water in order to calculate aggreated reflectance values for only water pixels? Choose (“scl”) to water pixels as identified based on the Scene Classification Layer that accompanies the Snetinel tile, or (“lulc”) to use Impact Observatory’s Land-Use/Land-Cover layer to identify water, and the Scene Classification Layer to remove clouds. Using “lulc” is strongly recommended.

Default: “lulc”

--mask-method2

Possible choices: mndwi, ndvi, “”

Which additional normalized index to use, if any, to update the mask to remove errors of ommission (pixels classified as water that shouldn’t be) prior to calculated aggregated reflectance? If “ndvi”, then only pixels with an NDVI value less than 0.25 will be retained. If “mndwi” (recommended) then only pixels with an MNDWI value greater than 0 will be retained. Of “”, then no secondary mask is used.

Default: “mndwi”

--n-folds

The number of folds to create for the training / validation set when fitting models using k-fold cross-validation.

Default: 5

--seed

The seed (an integer) used to initialize the pseudorandom number generator for use in partitioning data.

Default: 123