From natural language to quantitative satellite imagery analysis: Copernicus Data Space Ecosystem and AI enable vibe coding of custom scripts

Earth Observation (EO) is an essential source of information for most geosciences. However, high costs, large data volumes, and difficult access constrained its use for decades. Open data programs like Copernicus have reduced costs, and cloud access via the Copernicus Data Space Ecosystem (CDSE) has made local processing largely obsolete. In fact, API (Application Programming Interface)-based cloud access, analysis-ready mosaics and calibrated Copernicus Land Monitoring Service data products have made Sentinel data AI-ready. But despite these advances, the requirement for complex programming skills remained a significant barrier until recently. Here, we demonstrate how cloud-native processing APIs and generative artificial intelligence (AI) are removing this obstacle by enabling the "vibe coding" paradigm shift. Vibe coding is an approach to software development where the researcher focuses on the high-level logic, the functional vision, and the end product, while the syntax and code are generated and refined by AI.
Copernicus Data Space Ecosystem facilitates this transition through three key features: (1) the abstraction of EO analysis pipelines via RESTful APIs, which reduces tasks to a series of mathematical operations on pixel values; (2) the availability of intuitive web browser visualization for rapid prototyping and debugging; and (3) an extensive body of open documentation and code examples that serve as a robust training foundation for generative AI.
On CDSE, the Sentinel Hub API family utilizes "custom scripts" (or "evalscripts") — modular JavaScript files defining data inputs, outputs, calculations, and visualizations. The openEO API uses "process graphs", JSON representations of the processing steps in a unified structure as a series of nodes. Because the backend manages big data optimization and the browser handles rendering, these scripts are concise enough for AI assistants to generate, adapt, and debug effectively. The Sentinel Hub Custom Script Repository, containing over 200 community-contributed scripts, and the openEO community examples repository and CDSE "Algorithm Plaza" have laid the foundation for this approach. Neither of these advances was intentionally created to support AI, but rather to simplify programming for humans; however, combined, they enable a breakthrough in code development. We demonstrate how AI tools can efficiently adapt scripts across different satellite sensors, combine spectral indices into decision trees, and produce scalable quantitative outputs. This allows researchers not specialized in remote sensing to utilize existing code modules and natural language prompts to create meaningful results for their specific fields. Beyond the capabilities of Sentinel Hub, OpenEO supports joint analysis of data from multiple back-ends and the application of user-defined external code, such as biophysical models or pre-trained ONNX deep learning networks. While this added complexity presents a higher technical threshold, it also creates a massive opportunity for AI-driven automation. Ultimately, in combination with the public data space approach, generative AI further democratizes Earth Observation, transforming it from a specialist-only domain into an integrated component of all geoscience research workflows.