Google Earth Engine (GEE) is presently the most innovative international open-source platform for the advanced-level analysis of geospatial big data. In this study, we used three machine learning algorithms to apply this cloud platform for Land Use Land Cover (LULC) research in the Mardan, Pakistan. The machine learning algorithm in GEE is the most advanced technique to generate reliable and informative LULC maps from various satellite data to present reliable results. The primary goal of the present study is to compare the performance of various machine learning models (i.e., classification and regression trees [CART], support vector machine [SVM], and random forest [RF]) in GEE for the reliable four classes LULC maps using the Sentinel-2 imageries of 2022. In the current study, three satellite indices like the Normalized Difference Vegetation Index, Modified Normalized Difference Water Index, and Normalized Difference Built Index were applied to detect the features (i.e., vegetation, built, barren land, and water bodies in the study area). The performance of all three models was evaluated by validation and accuracy assessments. The Kappa coefficients of CART, SVM, and RF for Sentinel-2 images were 94%, 95%, and 97%, while the average overall accuracy is 96.25%, 97%, and 98.68%, respectively. The present study illustates that in this classification and comparison, RF performed better than SVM and CART. The current research study revealed that GEE has speedily processed the satellite imageries to develop the four classes of reliable LULC maps of the study area with the best accuracy results and deliver excellent support for further analysis.

Google Earth Engine（GEE）是目前最具创新性的国际开源平台，用于高级地理空间大数据分析。在本研究中，我们使用三种机器学习算法将该云平台应用于巴基斯坦马尔丹的土地利用土地覆盖（LULC）研究。GEE 中的机器学习算法是最先进的技术，可从各种卫星数据生成可靠且信息丰富的 LULC 地图，以呈现可靠的结果。本研究的主要目标是比较 GEE 中各种机器学习模型（即分类和回归树 [CART]、支持向量机 [SVM] 和随机森林 [RF]）对于可靠的四类 LULC 的性能使用 2022 年 Sentinel-2 图像绘制地图。在当前的研究中，应用归一化植被指数、修正归一化水分指数和归一化差异建成指数等三种卫星指数来检测特征（即植被、建成、贫瘠）研究区的土地、水体）。通过验证和准确性评估来评估所有三个模型的性能。Sentinel-2图像的CART、SVM和RF的Kappa系数分别为94%、95%和97%，而平均总体准确率分别为96.25%、97%和98.68%。本研究表明，在这种分类和比较中，RF 的表现优于 SVM 和 CART。目前的研究表明，GEE 已快速处理卫星图像，以开发出研究区域四类可靠的 LULC 地图，其结果具有最佳精度，并为进一步分析提供了良好的支持。