## Overview
A geospatial dataset of point geometries with a land use / land cover label and several remote-sensing derived predictor variables that can be used to train and test a land use / land cover classifier.
This dataset was generated with support from a Climate Change AI Innovation Grant and the Australian Centre for International Agricultural Research.
Each of the point geometries was assigned one of the following class labels:
To evaluate land use and land cover (LULC) maps an independent and representative test dataset is required. Here, a test dataset was generated via stratified random sampling approach across all areas in Fiji not used to generate [training data](https://github.com/livelihoods-and-landscapes/ccai-data/tree/main/fiji-…) (i.e. all Tikinas which did not contain a training data point were valid for sampling to generate the test dataset). Following equation 13 in [Olofsson et al.