- 1CS GROUP, Space Processing and Intelligence, France (nicolas.choplain@cs-soprasteria.com)
- 2CS GROUP, Space Processing and Intelligence, France (vincent.gaudissart@cs-soprasteria.com)
Antflow is a next-generation orchestration and publication framework designed to streamline the operational deployment of Earth Observation (EO) processing workflows, particularly within Digital Twin environments. By automating the transformation of scientific code into interoperable, shareable, and scalable services, Antflow removes the traditional barriers between algorithm development and production-grade execution.
At its core, Antflow enables scientists and developers to publish complex workflows directly from their Git repositories, using OGC Earth Observation Application Packages (EOAP) as the workflow definition mechanism. These EOAP descriptions allow Antflow to instantly expose workflows as OGC API Processes services, enriched with dynamic user interfaces and STAC-compliant cataloguing of outputs. This ensures that every workflow - no matter how experimental or mature - can be discovered, reused, and integrated across Digital Twin platforms.
Antflow’s hybrid orchestration engine distributes tasks across heterogeneous computing environments, from HPC clusters to cloud-native nodes. Git-based lineage guarantees traceability and scientific integrity, while integrated multi-provider retrieval mechanisms (EODAG) simplify access to EO data sources.
A key strength of Antflow is its ability to generate interactive user interfaces automatically. These interfaces allow domain experts, integrators, and end-users to parameterize, run, and monitor workflows through clean, intuitive views.
Antflow is currently used across several projects (CNES Digital Twin Factory, OGC Open Science Persistent Demonstrator). It acts as a middleware layer that bridges algorithm design, operational integration, and stakeholder consumption. By standardizing workflow publication, ensuring reproducibility, and supporting scalable execution, it accelerates the deployment of modelling chains such as 3D environmental reconstruction, forecasting, and multi-sensor analysis workflows.
How to cite: Choplain, N. and Vincent, G.: Antflow: Simplifying Workflow Sharing and Execution for Digital Twins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11128, https://doi.org/10.5194/egusphere-egu26-11128, 2026.