add architecture

README.md

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-# fair-ds-oc1-docs
+# FAIR-DS Demonstrator: Copernicus Data SpaceMachine Learning - ECOMETRICS
+
+The ECOMETRICS app is a demonstrator for the FAIR-DS project.
+This document presents the software architecture.
+You can find more information about the app itself in the [FAIR-DS wiki](https://fair-ds4nfdi.github.io/wiki/).
+
+## Architecture
+
+There are four components to the ECOMETRICS app demonstrator: the Data Spaces, the Geo Engine instance, the machine learning in Jupyter and the ECOMETRICS dashboard.
+There are two kinds of users: a Data Scientist that trains and refines a model and a user that select area and time of interest and performs an analysis.
+The following diagram shows the architecture of the ECOMETRICS app. ![ECOMETRICS architecture](./assets/architecture.svg)
+
+```mermaid
+flowchart LR
+subgraph DataSpaces
+NFDI[NFDI 4 Biodiversity] --> Aruna
+Copernicus[Copernicus Data Space Ecosystem] --> Aruna
+end
+
+subgraph GeoEngine
+Aruna[Data Connectors - Aruna & STAC] --> ProcessingEngine[Processing Engine]
+ProcessingEngine --> API[API]
+end
+
+API --> Dashboards[ECOMETRICS Dashboards]
+API --> PythonLibrary[Python Library]
+
+User[User] -->|Select area/time and analyze| Dashboards
+DataScientist[Data Scientist] -->|Train and refine model| PythonLibrary
+
+PythonLibrary --> Jupyter[Jupyter Notebook]
+PythonLibrary --> ScikitLearn[Scikit-Learn]
+PythonLibrary --> ONNX[ONNX]
+
+Dashboards -->|Show insights| User
+```
+
+### Data Spaces
+
+The demonstrator connects to the Copernicus Data Space and the NFDI4Biodiversity Data Space.
+The NFDI4Biodiversity provides training labels for the machine learning model.
+The Copernicus Data Space provides the satellite data.
+
+### Geo Engine
+
+Geo Engine has a data connector for each of the Data Spaces.
+The connectors are able to browser metadata and access the raw data for analysis.
+They map the files to the Geo Engine data model that supports temporal and spatial queries.
+The processing engine harmonizes and enriches the data and makes it ready for machine learning.
+Standardized and custom API methods make the data available.
+
+### Machine Learning in Jupyter
+
+The machine learning notebook access the Geo Engine API to retrieve the data.
+It uses Sci-kit Learn to train and refine a model.
+The model is stored in the ONNX format and uploaded to the Geo Engine where it is registered and can be used as an operator.
+
+### ECOMETRICS Dashboard
+
+The ECOMETRICS Dashboard is an easy-to-use web app that build upon the Geo Engine UI toolkit.
+The dashboard has an interactive map and lets the user select an area and time of interest.
+It has an analysis functionality that triggers the Geo Engine to run the machine learning model.

assets/architecture.mmd

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+flowchart TB
+    %% Data Spaces
+    subgraph DataSpaces["Data Spaces"]
+        nfdi["NFDI 4 BIODIVERSITY"]
+        copernicus["Copernicus Data Space Ecosystem"]
+    end
+
+    %% Geo Engine Components
+    subgraph GeoEngine["Geo Engine"]
+        subgraph DataConnectors["Data Connectors"]
+            aruna["ARUNA"]
+            stac["STAC"]
+        end
+        processingEngine["Processing Engine"]
+        api["API"]
+
+        subgraph PythonLibrary["Python Library"]
+            pythonLib["Python Library"]
+            scikit["Scikit-learn"]
+            onnx["ONNX"]
+            jupyter["Jupyter"]
+        end
+    end
+
+    %% ECOMETRICS Dashboards
+    subgraph Dashboards["ECOMETRICS Dashboards"]
+        dashboard["Dashboard"]
+    end
+
+    %% Actors
+    user["User"]
+    dataScientist["Data Scientist"]
+
+    %% Connections within Geo Engine
+    nfdi --> aruna
+    copernicus --> stac
+    aruna --> processingEngine
+    stac --> processingEngine
+    processingEngine --> api
+    api --> dashboard
+    api --> pythonLib
+    pythonLib --> scikit
+    pythonLib --> onnx
+    pythonLib --> jupyter
+
+    %% User Interactions
+    user --> dashboard:::interaction["Select area/time and analyze"]
+    dataScientist --> pythonLib:::interaction["Train and refine model"]
+
+    %% Styling for better readability (optional)
+    classDef interaction fill:#f9f,stroke:#333,stroke-width:2px;