An interactive spatio-temporal task computing framework with incremental graph reconstruction for remote sensing cloud platforms

In cloud-based environments, remote sensing computing platforms typically rely on executable scripts to orchestrate the invocation of spatio-temporal operators, thereby supporting the solution of geoscientific problems. However, existing task orchestration paradigms are primarily oriented toward monolithic end-to-end modeling, making it difficult to meet the requirements of scientific Workflow Management Systems (WfMS) for staged workflow construction, modular management, and iterative optimization. Moreover, in interactive analysis scenarios, repeatedly executed workflows with similar structures often lack effective reuse mechanisms, resulting in substantial redundancy in cloud computing resource consumption. To address these challenges, this study proposes an interactive spatio-temporal task computing framework tailored with an incremental computation approach based on graph reconstruction. We formally represent and dynamically construct spatiotemporal tasks using graph structures, then track the evolution of workflows during the spatiotemporal task modeling, and analyze the topological differences and dependencies between successive task versions. This enables us to systematically identify change operations and potential reuse opportunities during interactive orchestration, ultimately supporting fine-grained incremental computation. The proposed framework and methodology have been implemented on the Open Geospatial Engine (OGE) platform. Quantitative evaluations demonstrate that the proposed approach consistently reduces computational redundancy and execution time across different remote sensing tasks and data scales compared with conventional cloud-based geoprocessing workflows.