Efficient geospatial data partitioning is essential for large-scale satellite imagery processing and vector-raster alignment. We present QUARTER, a scalable framework for extracting satellite imagery using quadtree-based spatial subdivision. Given administrative boundaries as input, QUARTER adaptively partitions each region to minimize redundant tile requests and optimize coverage at varying resolutions. Our approach reduces the processing time compared to conventional rasterization methods while maintaining spatial accuracy, with execution times varying based on region size and grid resolution. Experiments demonstrate excellent scalability, processing regions like Hokkaido in about 46 seconds and largest territories like Russia in about 8.7 hours at 100-meter resolution. QUARTER’s advantages include eliminating redundant cell generation, preserving vector properties throughout processing, and seamless integration with QGIS for extended analysis. This framework addresses a critical bottleneck in the extraction of region footprints by efficiently extracting aligned satellite imagery for large-scale geospatial analysis.