Hi,
I am following the below example for random forest (it is working quite well for a my case).
{
"cells": [
{
"cell_type": "markdown",
"id": "5563cc52",
"metadata": {},
"source": [
"# Land Cover Mapping Feature Extraction"
]
},
{
"cell_type": "markdown",
"id": "f4e65275",
"metadata": {},
"source": [
"This notebook serves as an example of how to use GFMap and openEO to extract point features for training a machine learning model to do Land Cover Mapping. \n",
"\n",
"The example uses the following steps:\n",
"- Load the labelled points and ditribute them into spatial hexagons.\n",
"- Define the pre-processing steps for extracting the features from Sentinel-1 and Sentinel-2 data.\n",
show original
What I need additionally is to get the probability of each pixel classification. The classification scenario of machine learning models (random forest in this case) are usually estimating the probability of a being classified as a class.
Thanks,
Hi Mohammad,
Interesting suggestion. It is something we discussed internally before, but have no implementation for yet.
It is probably possible to implement in an UDF.
I logged a ticket, but it is not planned in yet: Implement predict_probabilities · Issue #775 · Open-EO/openeo-geopyspark-driver · GitHub
With a NoR support package it could get a higher priority.
Greetings,
Hi Emile,
Thanks for your consideration.
Thanks,
Hi,
We also have an experimental catboost feature that should support probabilities. Would that be useful for you?
Emile
Hi Emile,
Can you please share with me the documentation of catboost?
Hi,
We don’t have documentation on it. But we have an advanced example where ONNX is used to run machine learning models. It is quite a standard library that supports probabilities
Training the model here (ONNX should support catboost and random_forest):https://github.com/clausmichele/openEO_photovoltaic/blob/main/notebooks/rf_training.ipynb
ONNX conversion example:https://github.com/HansVRP/openEO_photovoltaic/blob/hv_joboptionfix/udf_inference/openeo_pv_farms_onnxconversion.ipynb
Inference notebook:https://github.com/HansVRP/openEO_photovoltaic/blob/hv_joboptionfix/udf_inference/openeo_pv_farms_inference_udf.ipynb
The actual UDF where you would want to adapt the apply_datacube:https://github.com/HansVRP/openEO_photovoltaic/blob/hv_joboptionfix/udf_inference/udf_rf_onnx.py
We usually upload the model to our artifactory. But you can probably upload on your own filehost too.
Hi Emile,
Thanks for your help. Sorry for the late replies.
Thanks,