Leveraging HPC Resources to Accelerate LiDAR and Remote Sensing Workflows
Dr. Burch Fisher, University of Maryland, Center for Environmental Science.
This project will leverage high-performance computing (HPC) resources to design and scale advanced workflows for processing large LiDAR and remote sensing datasets. The allocation will support experiments in both multi-core parallelization and GPU acceleration to improve the efficiency of computationally intensive tasks such as point-cloud classification, canopy-height model generation, raster mosaicking, and large-area geospatial analysis. By integrating open-source tools within an HPC environment at UMBC, the project will develop reproducible, high-throughput pipelines optimized for hybrid CPU–GPU architectures. These workflows will provide a foundation for scalable environmental research applications, demonstrating how HPC resources can substantially expand the speed, scope, and analytical capability of modern remote sensing workflows.