Python scripts and Dataset for a PU-learning framework for predicting agriculture terrace suitability: an application for the Troodos Mountains, Cyprus

This repository contains the rasterized dataset and Python scripts used to develop a machine learning framework for predicting agricultural terrace suitability in the Troodos Mountains, Cyprus. The dataset includes environmental, regulatory, and socio-economic variables, while the scripts implement Positive and Unlabeled (PU) learning using XGBoost models. All datasets can also be visualized via the Google Earth Engine at the following link: https://ee-ameena.projects.earthengine.app/view/land-suitability-analysis Note that original crop plot data, which have data-sharing restrictions, are not shared. The rasterized dataset only contains binary data on presence (1) or absence of a crop plot (0).本代码仓库包含用于构建机器学习框架的栅格化数据集与Python脚本，该框架旨在预测塞浦路斯特罗多斯山脉的农业梯田适宜性。本数据集涵盖环境、监管及社会经济变量；配套脚本基于XGBoost模型实现正样本与未标注样本（Positive and Unlabeled, PU）学习。所有数据集还可通过谷歌地球引擎（Google Earth Engine）进行可视化，访问链接如下：https://ee-ameena.projects.earthengine.app/view/land-suitability-analysis。请注意，受数据共享限制的原始作物地块数据未随本仓库一同发布。本次发布的栅格化数据集仅包含作物地块存在（1）与不存在（0）的二分类数据。