Videos that are shot using commodity hardware such as phones and surveillance cameras record various metadata such as time and location. We encounter such geospatial videos on a daily basis and such videos have been growing in volume significantly. Yet, we do not have data management systems that allow users to interact with such data effectively. In this paper, we describe Spatialyze, a new framework for end-to-end querying of geospatial videos. Spatialyze comes with a domain-specific language where users can construct geospatial video analytic workflows using a 3-step, declarative, build-filter-observe paradigm. Internally, Spatialyze leverages the declarative nature of such workflows, the temporal-spatial metadata stored with videos, and physical behavior of real-world objects to optimize the execution of workflows. Our results using real-world videos and workflows show that Spatialyze can reduce execution time by up to 5.3x, while maintaining up to 97.1% accuracy compared to unoptimized execution.

Spatialyze是一个用于端到端查询地理空间视频的新框架，它提供了一种领域特定语言，用户可以使用三步、声明式、构建-过滤-观察的范式来构建地理空间视频分析工作流。实验结果表明，Spatialyze可以将执行时间缩短多达5.3倍，同时与未经优化的执行相比，准确度可达97.1%。

Spatialyze：一种具备空间感知优化的地理空间视频分析系统