TLDR:
I built OASIS, an AI-assisted system that translates climate research questions into reproducible climate data analyses and geospatial outputs. It started as a tool to simplify my own thesis workflow, but I am exploring whether this approach could help other researchers and domain experts work with climate and spatial data more efficiently. I would appreciate honest feedback.
Hey everyone,
I recently finished my integrated master's thesis in Crop Science, where I mapped climate risk for olive cultivation across Greece.The results of this research have been submitted to the scientific journal Climate (MDPI), and a preprint version is also available for anyone interested.
Coming from an agricultural background rather than computer science, I had no previous experience writing code or working with large-scale climate datasets. To overcome this, I started using AI to help me build the scripts I needed, both for Google Earth Engine workflows and for processing downloaded Copernicus climate data. However, as the research questions became more complex, I found myself repeatedly adapting the code, changing data paths, modifying parameters and rebuilding similar workflows for each new analysis. This process became very time consuming and shifted my focus away from the scientific questions I wanted to explore.
To solve this frustration, I started building a prototype called OASIS. The goal was simple instead of repeatedly prompting an AI with what I wanted and having to manually validate every single time whether the generated code was actually correct, I wanted a more structured approach where I could describe the analysis in natural language and let the system handle the heavy lifting safely.
The way it works is straightforward. I have already downloaded the climate data needed and the analysis for generating the code is handled either locally through Ollama or via OpenRouter using free models. The system uses rules and validations to correctly handle temporal and spatial parameters for environmental and climate indicators so the AI doesn't have to guess formulas. Each generated analysis is accompanied by the underlying code, allowing users to inspect, validate, and reproduce the workflow if needed.
I also understand that the current implementation is mainly focused on climate data, which may not directly apply to everyone here.
I recently put the technical overview of OASIS on Zenodo (Technical Architecture and Workflow Record) and started realizing that this approach could be useful beyond my own research, especially for domain experts, scientists, and consultants who need climate insights but may not have the technical background to build these workflows themselves. Managing large climate datasets and setting up the required processing environment can be a significant barrier, especially for those who are not familiar with coding or data infrastructure.
My long-term vision is to evolve OASIS into a more scalable platform, potentially through a cloud-based infrastructure, making climate and geospatial analysis more accessible to a wider audience, while simultaneously ensuring it is faster and much easier to use for those who are already actively working with these workflows.
Before taking this further, I would really appreciate some honest and critical feedback from this community.
- Which groups do you think would benefit most from this approach? For example, researchers, consultants, decision-makers, or other domain experts who may not have a strong coding background or simply want to speed up their workflows?
- If you already work with large climate or environmental datasets, would you personally use a system like this? Would having cloud access to ready-to-use datasets and analysis workflows be valuable, instead of downloading and managing everything locally?
If anyone is interested in the research background behind the use case, I would also be happy to share the preprint of the climate risk assessment study.
Thank you for any feedback, positive or critical!
