recent years, RS data processing has shifted from traditional workstations with cutting-edge, often costly hardware and RS software, to cloud-based platforms. The latter enable users to instantly access and analyze pre-processed geospatial data via user-friendly, web-based interfaces and powerful scripting languages [8]. Among these systems, Google Earth Engine (GEE) is a cloud-based geospatial analytic platform that allows users to tackle the fundamental difficulties of managing enormous volumes of data, and their storage, processing, and analysis in a highly effective manner [9]. The recent partnership between Google and Norway's International Climate and Forest Initiative (NICFI) has led to the addition of NICFI PlanetScope Basemaps (700+ geospatial high resolution, 4.77 m, datasets) to GEE's public data catalog [10]. These datasets provide broad time series (December 2015-August 2020, biannual; September 2020 onward, monthly), four-band (RGB+NIR), and coverage of 94 countries across the global tropics [11]. The availability of PlanetScope (PL) data within GEE is an important step forward for detecting and monitoring LULC dynamics at high resolution, including small-scale disturbances, in tropical landscapes. Ordinary LULC automated classification algorithms for RS data use training data to build spectral signatures of selected LULC classes and pixel-based (PB) discrimination between selected classes using more or less advanced classification algorithms [12; 13]. Object-based (OB) approaches (so-called GEographic Object-Based Image Analysis--GEOBIA) have become more popular due to their capability to delineate and classify landscape objects or patches at different scales [14; 15; 16]. An essential stage in object delineation is pixel clustering and segmentation, aiming to combine comparable pixels into image clusters and transform them into vectors [17; 18]. Despite the increased processing cost of segmentation and numerous features for classification, OB approaches often yield better results for higher resolution data, whereas the PB approach is generally suggested for lower image resolutions [19]. OB classification processes, usually performed within specific, often proprietary, image processing software [20], can also be successfully implemented in GEE [16; 21]. In this environment, the Simple Non-Iterative Clustering (SNIC) method has been proven to be particularly effective in similar grouping pixels and identifying probable unique objects or landscape patches. SNIC was successfully applied using high-resolution images [18; 19] or medium-resolution satellite data, such as Landsat [16; 22]. Following image clustering, GEOBIA performs object categorization by combining spectral and geographical information with the texture and context information of the image [21; 23]. The Gray-Level Co-occurrence Matrix (GLCM) can be used in GEE to extract the objects' textural indices that represent additional information used in the classification [21; 24]. The OB approach has been effectively implemented in GEE for various applications: crop mapping in the Sanjiang plain, China, using S2 data and combined growth-period attributes [25]; LULC classification and analysis of the Munneru River Basin, India [26]; and crop classification in the Heilongjiang province, China, using Sentinel-1 time series [27]. In the classification of LULC, non-parametric machine learning (ML) techniques generally yield better results than standard classifiers since they do not need hypotheses about the input data distribution. Among the various ML classifiers, the Classification and Regression Tree (CART) is a binary decision tree classifier that employs a predetermined threshold [13], while Random Forest (RF) is an ensemble classifier that uses multiple CART [28]. The latter is one of the most effective, accurate, and broadly used classifiers used for LULC classification [29].