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All Journal Jurnal Ecodemica : Jurnal Ekonomi Manajemen dan Bisnis Jurnal Inovasi Bisnis (Inovbiz) INTECOMS: Journal of Information Technology and Computer Science SEIKO : Journal of Management & Business Jurnal Bakti Masyarakat Indonesia Kiwari El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam Journal of Ekonomics, Finance, and Management Studies Geoid - Journal of Geodesy and Geomatics Manager : Jurnal Ilmu manajemen Jurnal Ecodemica : Jurnal Ekonomi Manajemen dan Bisnis
Wijaya, Calvin
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Evaluation of Google Earth Engine Embedding Dataset for Remote Sensing Image Classification Wijaya, Calvin; Harintaka
Geoid Vol. 21 No. 1 (2026)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v21i1.8151

Abstract

Google Earth Engine (GEE) has emerged as one of the most powerful cloud-based platforms for processing and analyzing remote sensing imagery. By integrating vast Earth observation archives with scalable computational resources, it provides an accessible environment for researchers, practitioners, and decision-makers. In 2025, Google’s AlphaEarth Foundation introduced a novel embedding model trained on diverse Earth observation datasets available on the GEE server. This model, generated from annual time-series imagery and offered in an analysis-ready format, enables general-purpose applications such as classification, clustering, regression and change detection. Despite its potential, the performance and capabilities of this embedding model remain largely underexplored. This study evaluates the effectiveness of the embedding datasets in GEE for supervised classification method. Comparative experiments were conducted against widely used remote sensing imagery, including Sentinel-2 and Landsat 9 imagery, using multiple algorithms such as K-Neural Network (KNN), Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Trees (CART), and Object-Based Image Analysis (OBIA). In addition, a case study was carried out to examine the use of embedding datasets for mangrove classification. Validation using overall accuracy demonstrates that embedding datasets achieve superior results compared to conventional imagery. Classification using the embedding dataset achieved an average overall accuracy of 94%, outperforming Landsat 9 (83.1%) and Sentinel-2 (82.5%). Moreover, the embedding dataset produced a classification pattern similar to OBIA, even without the need for image segmentation. The findings highlight the potential of embedding datasets to enhance classification accuracy and broaden the scope of remote sensing applications, suggesting new opportunities for leveraging advanced machine learning representations in geospatial analysis.
Co-Authors Candy, Candy Cuandra, Fendy Dyfan Dyfan, Dyfan Hans, Elbert Harintaka Josephine, Jane Lady, Lady Lily Purwianti Lina Lina Noviani, Metta Nurhidayati Nurhidayati Raymond Sugiyah Sugiyah, Sugiyah Suteja, Mekar Sari Wasino Wasino Winduwati, Septia Yendiarti Yendiarti, Yendiarti Yuliantari, Kartika