RF 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 probabilityRandom forest predictionRandom forest probability [this dataset]XGBoost 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:<b>xxxxx_mmm_prediction.tif</b>: model prediction, encoded as 8-bit integers where 1 is predicted as wetland (&gt;50% probability), and NA (no data) is non-wetland.<b>xxxxx_mmm_probability.tif</b>: 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, <b>xxxxx</b> refers to the UUID of the grid area, which can be found in the file nzgrid_uuid.gpkg, and <b>mmm</b> is a code which refers to the model used:<b>hgb</b>: histogram gradient boost<b>mlpc</b>: multi-layer perceptron classification<b>rf</b>: random forest<b>xgb</b>: extreme gradient boostingIn addition to the tif images, two virtual raster tile files are included to enable mapping at the national scale:<b>_mmm_prediction.vrt</b><b>_mmm_probability.vrt</b>All 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