Smartflow让大规模地理时空数据研究更高效，支持多源数据融合与模型实验管理。
Smartflow: Enabling Scalable Spatiotemporal Geospatial Research
- 基于STAC标准和Kubernetes实现多源地理数据的统一处理与弹性扩展
- 在IARPA SMART项目数据上验证，可识别大型建设项目的全阶段特征
- 适合需要处理海量时空影像的科研或工业用户
BlackSky推出Smartflow，一个基于开源技术的云原生框架，支持可扩展的时空地理空间研究。通过STAC兼容目录作为统一输入，异构地理空间数据可被转换为标准化数据立方体，用于分析与模型训练。模型实验通过ClearML、TensorBoard和Apache Superset等工具协同管理。底层由Kubernetes驱动，负责工作流的资源调度与执行，实现横向与纵向扩展能力。该框架适用于大范围地理区域、长时序及大规模影像档案上的模型开发与分析。本文还展示了一个基于Smartflow构建的新颖神经架构，用于监测大范围区域的重型建筑活动。基于IARPA Space-based Machine Automated Recognition Technique (SMART)项目的数据，定性结果显示该模型可有效识别建设项目在所有主要阶段的特征。
原文摘要 · Abstract (English)
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.
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