Smartflow: Enabling Scalable Spatiotemporal Geospatial Research
This addresses the need for scalable geospatial model development for researchers and analysts working with large geographic areas and time scales, but it is incremental as it builds on existing open-source tools and technologies.
The paper tackles the problem of scalable spatiotemporal geospatial research by introducing Smartflow, a cloud-based framework that processes heterogeneous data into standardized datacubes for analysis and model training, and demonstrates its use in a neural architecture for detecting heavy construction across major development phases with qualitative results from the IARPA SMART program.
BlackSky introduces Smartflow, a cloud-based framework enabling scalable spatiotemporal geospatial research built on open-source tools and technologies. Using STAC-compliant catalogs as a common input, heterogeneous geospatial data can be processed into standardized datacubes for analysis and model training. Model experimentation is managed using a combination of tools, including ClearML, Tensorboard, and Apache Superset. Underpinning Smartflow is Kubernetes, which orchestrates the provisioning and execution of workflows to support both horizontal and vertical scalability. This combination of features makes Smartflow well-suited for geospatial model development and analysis over large geographic areas, time scales, and expansive image archives. We also present a novel neural architecture, built using Smartflow, to monitor large geographic areas for heavy construction. Qualitative results based on data from the IARPA Space-based Machine Automated Recognition Technique (SMART) program are presented that show the model is capable of detecting heavy construction throughout all major phases of development.