Google Earth Engine Since 2022: A Structured Bibliometric Review of GeoAI-Driven Trends and Applications
4 affiliations
- 1Department of Civil, Environmental, Land, Building Engineering and Chemistry, Politecnico di Bari, 70125 Bari, Italy
- 2University of Benevento Giustino Fortunato, 82100 Benevento, Italy
- 3Department of Engineering, University of Naples Parthenope, 80143 Naples, Italy
- 4Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
Abstract
Google Earth Engine (GEE) has become a central platform for planetary-scale geospatial analysis, but its rapid evolution in the last few years is not yet reflected in the existing review literature. Earlier reviews mainly describe the platform’s architecture and its initial application domains, whereas a structured bibliometric and thematic overview of the post-2022 phase of GEE is still lacking. In this more recent phase, the platform has introduced foundation models, satellite embeddings, and native links to cloud databases. Drawing on a structured bibliometric analysis of 5591 Scopus and Web of Science indexed documents published between 2011 and 2025, the results reveal sustained long-term growth, with annual publications increasing from 3 records in 2011 to 1371 records in 2025, corresponding to a compound annual growth rate (CAGR) of 54.88%, indicating a shift from exploratory testing of the platform to more operational use. Logistic growth modelling (R2=0.991) suggests that GEE research is transitioning from rapid expansion towards a scientific maturity phase, where the platform increasingly functions as a normalized analytical infrastructure embedded within broader cloud-native geospatial ecosystems. The full 2011–2025 corpus is used to establish long-term bibliometric trajectories, whereas the thematic synthesis focuses on the post-2022 transition towards Geospatial Artificial Intelligence(GeoAI), satellite embeddings, and cloud-database interoperability. The review examines how new satellite embedding datasets and BigQuery integrations help close the gap between raster-centric Earth observation (EO) workflows and tabular data science. We summarise methodological changes from traditional pixel-based classifiers to multimodal fusion approaches that combine Synthetic Aperture Radar (SAR), Global Ecosystem Dynamics Investigation (GEDI), and optical sensors, and we discuss how GEE’s highly integrated ecosystem influences reproducibility and the risk of vendor lock-in. Finally, we propose a roadmap for the ongoing transition of GEE towards GeoAI, offering researchers and policymakers a transparent and reproducible framework for deploying the platform in high-impact environmental governance.
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BibTeX
@article{khachoo2026google,
title = {{Google Earth Engine Since 2022: A Structured Bibliometric Review of GeoAI-Driven Trends and Applications}},
author = {Yasir Hassan Khachoo and Matteo Cutugno and Umberto Robustelli and Giovanni Pugliano},
journal = {Sustainability},
year = {2026},
month = jun,
doi = {10.3390/su18126241},
url = {https://doi.org/10.3390/su18126241},
note = {CC BY},
}
RIS
TY - JOUR
TI - Google Earth Engine Since 2022: A Structured Bibliometric Review of GeoAI-Driven Trends and Applications
AU - Yasir Hassan Khachoo
AU - Matteo Cutugno
AU - Umberto Robustelli
AU - Giovanni Pugliano
PY - 2026
DA - 2026/06/17
JO - Sustainability
DO - 10.3390/su18126241
UR - https://doi.org/10.3390/su18126241
AB - Google Earth Engine (GEE) has become a central platform for planetary-scale geospatial analysis, but its rapid evolution in the last few years is not yet reflected in the existing review literature. Earlier reviews mainly describe the platform’s architecture and its initial application domains, whereas a structured bibliometric and thematic overview of the post-2022 phase of GEE is still lacking. In this more recent phase, the platform has introduced foundation models, satellite embeddings, and native links to cloud databases. Drawing on a structured bibliometric analysis of 5591 Scopus and Web of Science indexed documents published between 2011 and 2025, the results reveal sustained long-term growth, with annual publications increasing from 3 records in 2011 to 1371 records in 2025, corresponding to a compound annual growth rate (CAGR) of 54.88%, indicating a shift from exploratory testing of the platform to more operational use. Logistic growth modelling (R2=0.991) suggests that GEE research is transitioning from rapid expansion towards a scientific maturity phase, where the platform increasingly functions as a normalized analytical infrastructure embedded within broader cloud-native geospatial ecosystems. The full 2011–2025 corpus is used to establish long-term bibliometric trajectories, whereas the thematic synthesis focuses on the post-2022 transition towards Geospatial Artificial Intelligence(GeoAI), satellite embeddings, and cloud-database interoperability. The review examines how new satellite embedding datasets and BigQuery integrations help close the gap between raster-centric Earth observation (EO) workflows and tabular data science. We summarise methodological changes from traditional pixel-based classifiers to multimodal fusion approaches that combine Synthetic Aperture Radar (SAR), Global Ecosystem Dynamics Investigation (GEDI), and optical sensors, and we discuss how GEE’s highly integrated ecosystem influences reproducibility and the risk of vendor lock-in. Finally, we propose a roadmap for the ongoing transition of GEE towards GeoAI, offering researchers and policymakers a transparent and reproducible framework for deploying the platform in high-impact environmental governance.
SN - 2071-1050
ER -