Alleviating Resource Requirements for Spatial Deep Learning Workloads

Spatial data volumes have increased exponentially over the past couple of decades. This growth has been fueled by networked observational devices, remote sensing sources such as satellites, and simulations that characterize spatiotemporal dynamics of phenomena (e.g., climate). Manual inspection of these data becomes unfeasible at such scales. Fitting models to the data offer an avenue to extract patterns from the data, make predictions, and leverage them to understand phenomena and decision-making. Innovations in deep learning and their ability to capture non-linear interactions between features make them particularly relevant for spatial datasets. However, deep learning workloads tend to be resource-intensive. In this study, we design and contrast transfer learning schemes to substantively alleviate resource requirements for training deep learning models over spatial data at scale. We profile the suitability of our methodology using deep networks built over satellite datasets and gridded data. Empirical benchmarks demonstrate that our spatiotemporally aligned transfer learning scheme ensures ~2.87-5.3 fold reduction in completion times for each model without sacrificing on the accuracy of the models.