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Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics
By taking advantage of Apache Spark, Nvidia DGX1, and DGX2 computing platforms, we demonstrate unprecedented compute speed-ups for deep learning inference on pixel labeling workloads; processing 21, 028~Terrabytes of imagery data and delivering an output maps at area rate of 5. 245sq. km/sec, amounting to 453, 168 sq. km/day - reducing a 28 day workload to 21~hours.
Building Extraction at Scale using Convolutional Neural Network: Mapping of the United States
The quality of extracted buildings and processing time demonstrated the proposed CNN-based framework fits the need of building extraction at scale.
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