Kurbucz, Marcell T;
Andrée, Bo Pieter Johannes;
(2025)
Building and Managing Local Databases from Google Earth Engine with the geeLite R Package.
(Policy Research Working Papers
11115).
World Bank: Washington, D.C., USA.
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Abstract
Google Earth Engine has transformed geospatial analysis by providing access to petabytes of satellite imagery and geospatial data, coupled with the substantial computational power required for in-depth analysis. This accessibility empowers scientists, researchers, and non-experts alike to address critical global challenges on an unprecedented scale. In recent years, numerous R packages have emerged to leverage Google Earth Engine’s functionalities. However, constructing and managing complex spatio-temporal databases for monitoring changes in remotely sensed data remains a challenging task that often necessitates advanced coding skills. To bridge this gap, geeLite, a novel R package, is introduced to facilitate the construction, management, and updating of local databases for Google Earth Engine-computed geospatial features, which enables users to monitor their evolution over time. By storing geospatial features in SQLite format—a serverless and self-contained database solution requiring no additional setup or administration—geeLite simplifies the data collection process. Furthermore, it streamlines the conversion of stored data into native R formats and provides functions for aggregating and processing created databases to meet specific user needs.
| Type: | Working / discussion paper |
|---|---|
| Title: | Building and Managing Local Databases from Google Earth Engine with the geeLite R Package |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1596/1813-9450-11115 |
| Publisher version: | https://doi.org/10.1596/1813-9450-11115 |
| Language: | English |
| Additional information: | Copyright © World Bank. This is an Open Access paper published under a Creative Commons Attribution 3.0 IGO (CC BY 3.0 IGO) Licence (https://creativecommons.org/licenses/by/3.0/igo). |
| Keywords: | Google Earth Engine, Geographic Information System, Remote Sensing, Raster Data, Spatio-Temporal Data, Software |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > UCL Institute for Global Prosperity |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10216352 |
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