Add badges, GCN architecture diagram, and GitHub Actions CI

- Add shield badges (Python, PyTorch, PyG, License, Sentinel-2, CI, Stars)
- Add GCN model architecture diagram with layer details table
- Add GitHub Actions CI workflow (flake8 linting + import checks)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Author: Osman-Geomatics93

.github/workflows/ci.yml

@@ -0,0 +1,38 @@
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  lint-and-check:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.9"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install flake8
+          pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
+          pip install torch_geometric
+          pip install numpy pandas scikit-learn matplotlib seaborn rasterio
+
+      - name: Lint with flake8
+        run: |
+          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+          flake8 . --count --exit-zero --max-line-length=120 --ignore=E501,W503,E203,E266,W504 --statistics
+
+      - name: Check imports
+        run: |
+          python -c "from gcn_crop_classification import GCN; print('GCN import OK')"
+          python -c "import apply_gcn_to_raster; print('Raster script import OK')"

README.md

@@ -1,5 +1,13 @@
 # Crop Classification with Graph Convolutional Networks (GCN)
 
+[![CI](https://github.com/Osman-Geomatics93/GCN-Crop-Classification/actions/workflows/ci.yml/badge.svg)](https://github.com/Osman-Geomatics93/GCN-Crop-Classification/actions/workflows/ci.yml)
+[![Python 3.9+](https://img.shields.io/badge/Python-3.9%2B-3776AB?logo=python&logoColor=white)](https://www.python.org/)
+[![PyTorch](https://img.shields.io/badge/PyTorch-2.0%2B-EE4C2C?logo=pytorch&logoColor=white)](https://pytorch.org/)
+[![PyG](https://img.shields.io/badge/PyTorch_Geometric-2.5%2B-3C2179?logo=pyg&logoColor=white)](https://pyg.org/)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
+[![Sentinel-2](https://img.shields.io/badge/Satellite-Sentinel--2-1B4F72)](https://sentinel.esa.int/web/sentinel/missions/sentinel-2)
+[![Stars](https://img.shields.io/github/stars/Osman-Geomatics93/GCN-Crop-Classification?style=social)](https://github.com/Osman-Geomatics93/GCN-Crop-Classification)
+
 Pixel-level crop classification from **Sentinel-2** satellite imagery using a **Graph Convolutional Network** built with PyTorch Geometric. The model classifies agricultural land into 5 crop/land-cover classes at 10 m spatial resolution.
 
 ---
@@ -8,6 +16,7 @@ Pixel-level crop classification from **Sentinel-2** satellite imagery using a **
 
 - [Overview](#overview)
 - [Method](#method)
+  - [Model Architecture](#model-architecture)
 - [Project Structure](#project-structure)
 - [Results](#results)
   - [Exploratory Data Analysis](#1-exploratory-data-analysis)
@@ -63,6 +72,26 @@ Classified Crop Map (GeoTIFF + PNG)
 3. **GCN training** -- 3-layer GCN with batch normalization, dropout (0.5), and inverse-frequency class weighting
 4. **Raster inference** -- Tiled KNN-graph prediction over the full 2262x1424 Sentinel-2 composite
 
+### Model Architecture
+
+The GCN consists of 3 graph convolutional layers with batch normalization and dropout. The KNN graph (k=8) provides the edge connectivity, enabling each pixel to aggregate spectral information from its 8 most similar neighbors in feature space.
+
+<p align="center">
+  <img src="figures/gcn_architecture.png" alt="GCN Model Architecture" width="900">
+</p>
+
+| Component | Details |
+|:----------|:--------|
+| **Input** | 23 features (10 spectral bands + 13 vegetation indices) |
+| **Layer 1** | GCNConv(23, 128) + BatchNorm + ReLU + Dropout(0.5) |
+| **Layer 2** | GCNConv(128, 128) + BatchNorm + ReLU + Dropout(0.5) |
+| **Layer 3** | GCNConv(128, 5) — output logits |
+| **Graph** | KNN (k=8), symmetric, undirected |
+| **Parameters** | 20,741 |
+| **Optimizer** | Adam (lr=0.01, weight_decay=5e-4) |
+| **Loss** | CrossEntropy with inverse-frequency class weights |
+| **Early stopping** | Patience = 30 epochs |
+
 ## Project Structure
 
 ```