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As satellite imagery collections continue to grow at an astonishing rate, so is the demand for automated and scalable object detection and segmentation. Scaling computational activities demand models that generalize well across various challenges that can hamper progress, including diverse imaging and geographic conditions, sampling bias in training data, manual ground truth collection, tooling for model reuse and accountability assessment, and poor model training strategies. A great deal of progress has been made on these challenges. We contribute to the improvement through further development of ReSFlow, a workflow that breaks the problem of model generalization into a collection of specialized exploitations. ReSFlow partitions imagery collections into homogeneous buckets equipped with exploitation models trained to perform well under each bucket.s specific context. Essentially, ReSFlow aims for generalization through stratification. Therefore, within a bucket, exploitation is a homogeneous process that mitigates heterogeneity challenges, including the number of training data and data biases that can occur over varied conditions. Furthermore, custom model architectures and rich training strategies effective for within-bucket conditions can be developed. Meanwhile, across buckets, performance metrics support systematic views of the workflow leading to optimal data labeling allocations and indications that further specialization is warranted. Herein, we discuss the formation of models during the framework's “Offline Initialization” stage. Lastly, we exploit the inherent parallelism due to bucketing to introduce model reuse and demonstrate efficacy by reducing an 89-day manual data labeling cost to zero-days in a new area of interest.

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.