Google Earth Engine (GEE) is a cloud-based platform that allows researchers access to an unprescedented archive of remotely sensed data, as well as the computing power and infrastructure to perform large scale geospatial analysis. As such, GEE has become indispensible for ecological research. However, GEE is not optmized to work with vector data and has limited (albeit growing) options for training and validating machine learning models.
In contrast, R has been used by ecological researchers for decades and has a robust ecosystem of packages and resources for training, tuning and applying machine learning models, including the 'tidymodels' and 'caret' frameworks.
Thus, researchers often find themselves needing to move between R and GEE over the course of modeling workflow. For example, a researcher might capitalize on GEE's raster processing power to extract predictor variables across a set of training data, then bring the extracted data into R to train and validate a series of models. Ideally, these models could then be applied in GEE across the full raster domain. However, a common sticking point is formatting R-based models in a way that is readable by GEE's javascript based interface.
This repository includes scripts and sample data to take random forest models fitted via the ranger or randomForest package in R and convert them into the 'tree' based string format required for import into GEE. The code presented here is modified from the 'reprtree' package available here: https://github.com/araastat/reprtree/tree/master
The script 0_format_ranger_for_gee.R performs the random forest conversion. To run this script, users will need a random forest model fitted using either the 'ranger' or 'randomForest' model in R.
The script provides functionality to fit a test model, or users can load an existing model saved as an .RDS file.
In the script, set parameters to your specification:
- model_generation_method: Choose whether to fit an example model ('fit') or load an existing model ('load')
- model_type: Choose which package was used or will be used to fit the model 'ranger' or 'randomForest'
- response_type: Choose 'classification', 'regression' or 'probability' -- note that 'probability' forests are only available via ranger
- caret: Choose TRUE or FALSE to indicate whether the model was fit using caret. If TRUE, additional processing steps will be automatically taken to format the forest properly.
- tidymodels: Choose TRUE or FALSE to indicate whether the model was fit using tidymodels. If TRUE, additional processing steps will be automatically taken to format the forest properly.
- out_mod_prefix: Specify a prefix to use when saving the formatted model. This name will be combined with information on model and response type to create a final model name.
The output will be a .txt file with the random forest model formatted for input into GEE.
The script 1_apply_model_in_gee.R contains javascript pseduo-code for importing the model into GEE and applying it across a stack of predictor variables.