MLPC Probability (version 0.1): Mapping Wetlands with High Resolution Planet SuperDove Satellite Imagery

These datasets represent predictions and associated probabilities using four machine learning methods, associated with collection: Mapping Wetlands with High Resolution Planet SuperDove Satellite Imagery: An Assessment of Machine Learning Models Across the Diverse Waterscapes of New Zealand (10.26021/canterburynz.c.7848596).
The following datasets are available:
HGB predictionHGB probabilityMLPC predictionMLPC probability [this dataset]Random forest predictionRandom forest probabilityXGBoost predictionXGBoost probabilityFor details of the models developed, please see the collection and associated paper. The following files are available in each dataset, each representing an area within New Zealand:
xxxxx_mmm_prediction.tif: model prediction, encoded as 8-bit integers where 1 is predicted as wetland (>50% probability), and NA (no data) is non-wetland.xxxxx_mmm_probability.tif: model wetland probability, encoded as 16-bit integers, with probability values from 0 to 1 rescaled from 0 to 10,000. Divide the values by 10,000 to obtain probabilities to four decimal places.In the tile filenames, xxxxx refers to the UUID of the grid area, which can be found in the file nzgrid_uuid.gpkg, and mmm is a code which refers to the model used:
hgb: histogram gradient boostmlpc: multi-layer perceptron classificationrf: random forestxgb: extreme gradient boostingIn addition to the tif images, two virtual raster tile files are included to enable mapping at the national scale:
_mmm_prediction.vrt_mmm_probability.vrtAll tif images are saved using cloud optimised geotiff (COG), which makes them fast to display even at a national level, although increases the data size. Total size is around 700 MB for the prediction datasets, and ~75 GB for the probability datasets.
Metadata for the Planet SuperDove imagery used for each pixel of the predictions is available here: https://doi.org/10.26021/canterburynz.29231837.v