ppstxd/False_Positive_Analysis

Contains the Approach for False Positive Analysis and Cleaning of Crop Field Prediction Raster of Angola # Notebook Overview This Repo contains the approach for false positive analysis and cleaning of my crop field prediction with a UNET model in Angola. The notebook guides users through a workflow for spatial data analysis and classification using a Random Forest model in Google Earth Engine (GEE) using GEEmap in Python. The goal is to create a mask for the prediction, to exclude mostly false-positive pixels. The workflow is organized into three main sections: #### Data Source Acknowledgment This project uses satellite imagery from the PLANET constellation, provided through the NICFI (Norway’s International Climate and Forest Initiative) program. I gratefully acknowledge the PLANET-NICFI initiative for making this high-resolution data available, which is essential for the spatial analysis and crop field prediction in this notebook. --- ## 1. Data Preparation - Loads and preprocesses predicted fractional map and land cover raster - Creates 500 stratified proportional random sampling points in total - The points were manually labelled with 1 = crop and 0 = non-crop --- ## 2. Sampling of Predictor Variables in Google Earth Engine - Loads several relevant predictor variables from Google Earth Engine to discriminate crop fields and non-crop fields - Stacks predictor variables and resamples them to 10 meter spatial resolution - Extracts values based on sampling points - Currently used predictor variables used to train the RF model: - **Elevation** derived from ALOS - **Precipitation**, **Evapotranspiration (ET)** and **Aridity Index'** derived from CHIRPS and MODIS - **Sentinel-1 Radar** Imagery Median composite (01-2021 - 12-2024) - **Enhanced Vegetation Index (EVI)** based on Sentinel-2 - **Class** variable (derived from Section 1 in notebook) --- ## 3. Training of Prediction of RF Model - Parameters for RF model in GEE: - `numberOfTrees=500` - `variablesPerSplit=3` - `minLeafPopulation=3` - `bagFraction=0.85` - `seed=42` - Trains the model on the prepared dat …