Abstract
The shear volumes of data generated from earth observation and remote sensing technologies continue to make major impact; leaping key geospatial applications into the dual data and compute-intensive era. As a consequence, this rapid advancement poses new computational and data processing challenges. We implement a novel remote sensing data flow (RESFlow) for advancing machine learning to compute with massive amounts of remotely sensed imagery. The core contribution is partitioning massive amounts of data into homogeneous distributions for fitting simple models. RESFlow takes advantage of Apache Spark and the availability of modern computing hardware to harness the acceleration of deep learning inference on expansive remote sensing imagery. The framework incorporates a strategy to optimize resource utilization across multiple executors assigned to a single worker. We showcase its deployment in both computationally and data-intensive workloads for pixel-level labeling tasks. The pipeline invokes deep learning inference at three stages; during deep feature extraction, deep metric mapping, and deep semantic segmentation. The tasks impose compute-intensive and GPU resource sharing challenges motivating for a parallelized pipeline for all execution steps. To address the problem of hardware resource contention, our containerized workflow further incorporates a novel GPU checkout routine and the ticketing system across multiple workers. The workflow is demonstrated with NVIDIA DGX accelerated platforms and offers appreciable compute speed-ups for deep learning inference on pixel labeling workloads; processing 21 028 TB of imagery data and delivering output maps at area rate of 5.245 sq.km/s, amounting to 453 168 sq.km/day—reducing a 28 day workload to 21 h.
