Evaluation of Google Earth Engine Embedding Dataset for Remote Sensing Image Classification
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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.
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References
Adorno, B. V., Körting, T. S., & Amaral, S. (2023). Contribution of time-series data cubes to classify urban vegetation types by remote sensing. Urban Forestry and Urban Greening, 79. https://doi.org/10.1016/j.ufug.2022.127817
Adrian, J., Sagan, V., & Maimaitijiang, M. (2021). Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 175(December 2020), 215–235. https://doi.org/10.1016/j.isprsjprs.2021.02.018
Afininnas, F., Nawang Wulandari, Y., Fioren, A., Golo, J., & Kurniawan, R. (2024). Analisis Perbandingan Metode Klasifikasi Pada Pemetaan Tutupan Lahan di Provinsi DI Yogyakarta Tahun 2023. Seminar Nasional Sains Data, 2024.
Ahmad, S. K., Hossain, F., Eldardiry, H., & Pavelsky, T. M. (2020). A Fusion Approach for Water Area Classification Using Visible, near Infrared and Synthetic Aperture Radar for South Asian Conditions. IEEE Transactions on Geoscience and Remote Sensing, 58(4), 2471–2480. https://doi.org/10.1109/TGRS.2019.2950705
Askari, F., Fateh, A., & Mohammadi, M. R. (2025). Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms. ArXiv. https://doi.org/10.1016/j.neunet.2025.107339
Ba Alawi, A., & Bozkurt, F. (2025). Performance Analysis of Embedding Methods for Deep Learning-Based Turkish Sentiment Analysis Models. Arabian Journal for Science and Engineering, 50(10), 7299–7321. https://doi.org/10.1007/s13369-024-09360-4
Brown, C. F., Kazmierski, M. R., Pasquarella, V. J., Rucklidge, W. J., Samsikova, M., Zhang, C., Shelhamer, E., Lahera, E., Wiles, O., Ilyushchenko, S., Gorelick, N., Zhang, L. L., Alj, S., Schechter, E., Askay, S., Guinan, O., Moore, R., Boukouvalas, A., & Kohli, P. (2025). AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data. ArXiv. http://arxiv.org/abs/2507.22291
Chi, M., Plaza, A., Benediktsson, J. A., Sun, Z., Shen, J., & Zhu, Y. (2016). Big Data for Remote Sensing: Challenges and Opportunities. Proceedings of the IEEE, 104(11), 2207–2219. https://doi.org/10.1109/JPROC.2016.2598228
Dritsas, E., & Trigka, M. (2025). Remote Sensing and Geospatial Analysis in the Big Data Era: A Survey. Remote Sensing, 17(3). https://doi.org/10.3390/rs17030550
Ez-zahouani, B., Teodoro, A., El Kharki, A., El Kharki, O., & Rhachi, H. (2025). Evaluating the potential of Landsat 8/9 and Sentinel 2 data and different spectral and spatial indices for segment extraction in large watersheds for OBIA approach in remote sensing: A case study of the Sebou watershed. Remote Sensing Applications: Society and Environment, 38. https://doi.org/10.1016/j.rsase.2025.101575
Fattore, C., Abate, N., Faridani, F., Masini, N., & Lasaponara, R. (2021). Google earth engine as multi-sensor open-source tool for supporting the preservation of archaeological areas: The case study of flood and fire mapping in metaponto, italy. Sensors, 21(5), 1–27. https://doi.org/10.3390/s21051791
Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T., Masek, J., & Duke, N. (2011). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1), 154–159. https://doi.org/10.1111/j.1466-8238.2010.00584.x
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Harris, N., Butani, A., & Hashmy, S. (2024). Enhancing Embedding Performance through Large Language Model-based Text Enrichment and Rewriting. ArXiv. http://arxiv.org/abs/2404.12283
Hastoro, D. A., & Yudinugroho, M. (2023). Analisis Klasifikasi Tutupan Lahan dengan Citra Sentinel 1A Menggunakan Metode Dekomposisi Polarimetrik di Provinsi Daerah Istimewa Yogyakarta. Jurnal Ilmiah Geomatika, 3(2), 58. https://doi.org/10.31315/imagi.v3i2.10778
Hird, J. N., DeLancey, E. R., McDermid, G. J., & Kariyeva, J. (2017). Google earth engine, open-access satellite data, and machine learning in support of large-area probabilistic wetland mapping. Remote Sensing, 9(12). https://doi.org/10.3390/rs9121315
Kang, D., Kwon, H., Min, J., & Cho, M. (2021). Relational Embedding for Few-Shot Classification. ArXiv. http://cvlab.postech.ac.kr/research/RENet
Kumar, L., & Mutanga, O. (2018). Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10). https://doi.org/10.3390/rs10101509
Li, H., Cui, J., Zhang, X., Han, Y., & Cao, L. (2022). Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction. Remote Sensing, 14(18). https://doi.org/10.3390/rs14184579
Ma, Y., Wu, H., Wang, L., Huang, B., Ranjan, R., Zomaya, A., & Jie, W. (2015). Remote sensing big data computing: Challenges and opportunities. Future Generation Computer Systems, 51, 47–60. https://doi.org/10.1016/j.future.2014.10.029
Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Homayouni, S., & Gill, E. (2019). The first wetland inventory map of newfoundland at a spatial resolution of 10 m using sentinel-1 and sentinel-2 data on the Google Earth Engine cloud computing platform. Remote Sensing, 11(1). https://doi.org/10.3390/rs11010043
Padarian, J., Minasny, B., & McBratney, A. B. (2015). Using Google’s cloud-based platform for digital soil mapping. Computers and Geosciences, 83, 80–88. https://doi.org/10.1016/j.cageo.2015.06.023
Parente, L., Taquary, E., Silva, A. P., Souza, C., & Ferreira, L. (2019). Next generation mapping: Combining deep learning, cloud computing, and big remote sensing data. Remote Sensing, 11(23). https://doi.org/10.3390/rs11232881
Radhakrishna, A., & Susstrunk, S. (2011). Superpixels and Polygons using Simple Non-Iterative Clustering. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4651–4660. https://doi.org/10.1109/CVPR.2017.520
Salem, N., & Hussein, S. (2019). Data dimensional reduction and principal components analysis. Procedia Computer Science, 163, 292–299. https://doi.org/10.1016/j.procs.2019.12.111
Shlens, J. (2003). A Tutorial on Principal Component Analysis. ArXiv.
Tassi, A., & Vizzari, M. (2020). Object-Oriented LULC Classification in Google Earth Learning Algorithms. Remote Sensing, 12, 1–17. doi:10.3390/rs12223776
Velastegui-Montoya, A., Montalván-Burbano, N., Carrión-Mero, P., Rivera-Torres, H., Sadeck, L., & Adami, M. (2023). Google Earth Engine: A Global Analysis and Future Trends. Remote Sensing, 15(14). https://doi.org/10.3390/rs15143675
Yang, C., & Suh, C. P. C. (2023). Applying machine learning classifiers to Sentinel-2 imagery for early identification of cotton fields to advance boll weevil eradication. Computers and Electronics in Agriculture, 213. https://doi.org/10.1016/j.compag.2023.108268