A comprehensive pipeline to analyze and predict urban growth in Cairo, Egypt between 2018 and 2025, leveraging geospatial data processing and machine learning.
# Geospatial Intelligence System for Monitoring Urban & Geographic Changes
A comprehensive pipeline to analyze and predict urban growth in Cairo, Egypt between 2018 and 2025, leveraging geospatial data processing and machine learning.
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## 🚀 Project Overview
Cities are ever-evolving systems, and understanding patterns of urban expansion is crucial for sustainable planning. This project:
* **Processes** high-resolution (10 m) and projected (100 m) raster datasets from the European Commission’s Global Human Settlement Layer (GHSL).
* **Detects** changes in built-up areas between 2018 and 2025.
* **Visualizes** spatial patterns of urban growth, stability, and decline.
* **Applies** a Random Forest classifier to predict change pixels using spectral features and local texture statistics.
## 📂 Repository Structure
```
├── data/ # Raw and processed raster datasets
│ ├── GHS_BUILT_S_E2018.tif # 2018 built-up data (10 m)
│ └── GHS_BUILT_S_E2025.tif # 2025 projected data (100 m)
│
├── notebooks/ # Jupyter notebooks
│ ├── gis-project-both.ipynb # Full geospatial workflow (preprocessing, change maps)
│ └── gis-project-ml.ipynb # ML pipeline (feature extraction, model training & evaluation)
│
├── requirements.txt # Python dependencies
├── README.md # Project overview and instructions
└── LICENSE # Project license (e.g., MIT)
```
## 🛠️ Prerequisites & Installation
1. **Clone the repo**
```bash
git clone
github.com /geospatial-intelligence-system.git
cd geospatial-intelligence-system
```
2. **Create a virtual environment** (recommended)
```bash
python3 -m venv venv
source venv/bin/activate # Linux/macOS
venv\\Scripts\\activate # Windows
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
> **Key libraries:** `geopandas`, `rasterio`, `numpy`, `matplotlib`, `scikit-learn`, `jupyter`
## 💻 Usage
### 1. Geospatial Analysis Notebo …