Building and managing local databases from Google Earth Engine with the geeLite R package

Overview

Google Earth Engine has substantially expanded geospatial analysis capabilities through provision of petabyte-scale satellite imagery and cloud-based computation infrastructure. Despite proliferation of R packages interfacing with GEE, construction and management of complex spatio-temporal databases for continuous monitoring of remotely sensed data remains technically demanding. The geeLite R package addresses this gap by facilitating local database construction and maintenance of GEE-derived outputs, enabling longitudinal tracking of environmental and socio-economic phenomena at large spatial and temporal scales.

Methods and approach

The geeLite package implements SQLite as the underlying database format, selecting a serverless, self-contained solution that eliminates infrastructural dependencies and installation requirements. The package provides functionality for database initialization, iterative updating of GEE-derived outputs, automated conversion to native R data structures, and temporal aggregation workflows. This architecture enables researchers to integrate cloud-based geospatial computation with local data management and downstream analytical pipelines.

Key Findings

The package facilitates construction of portable, offline-accessible databases storing GEE-processed outputs in standardized SQLite format. It provides integrated functions for database population through iterative GEE queries, conversion of stored results to R-compatible formats, and aggregation operations supporting time series analysis. The implementation reduces technical barriers associated with complex spatio-temporal data management while preserving the computational capacity of GEE infrastructure.

Implications

The geeLite package extends accessibility of GEE-derived geospatial databases to researchers with intermediate R programming competency by abstracting database construction and management complexity. Reduction of technical prerequisites enables broader adoption of continuous monitoring workflows for environmental and socio-economic research applications requiring systematic collection of remotely sensed observations. Local database storage in portable SQLite format facilitates reproducible analysis, collaborative research workflows, and offline analytical pipelines independent of cloud service availability.