🛣️ Rural Road Planner

Bridging the gap: Optimizing rural infrastructure through intelligent road network prediction.

💡 Inspiration

Rural connectivity is a major challenge in developing regions like Jhapa. Poor road planning leads to inefficient transportation and wasted resources. We built Rural Road Planner to solve this by identifying the most impactful missing links in a road network.

By inputting a District and Place name, planners can visualize exactly where new roads should be constructed to maximize connectivity.

🚀 Key Features

• Interactive Geospatial Mapping: Built with Leaflet.js, allowing users to explore road networks dynamically.
• Granular Search: Users can filter by District and Place Name (e.g., Gauradaha) to isolate specific communities.
• Intelligent Prediction: The system analyzes the existing road network and generates Proposed Roads (highlighted in Red/Blue) using Graph Neural Networks.
• Map Modes: Supports Standard Streets, Satellite, Dark, and Light modes.

Visualizing the Data

Below is a visualization of the road network for the Jhapa district used in our analysis:

🛠️ Tech Stack

Frontend:

• React.js
• Leaflet.js (Map rendering)
• Tailwind CSS (Styling)

Backend:

• FastAPI (High-performance API)
• Python

AI & Data:

• PyTorch Geometric (GNN for Link Prediction)
• OpenStreetMap (OSM) (Raw geospatial data)

⚙️ How It Works

1. Data Ingestion: The system loads road network data for the specific region.
2. User Input: The user selects a target District and Municipality/Place.
3. Processing: The FastAPI backend calculates the most efficient new road segments required to connect disconnected nodes.
4. Visualization: The proposed roads are rendered on the frontend as color-coded segments overlaying the real-world map.

📦 Installation & Setup

1. Clone the repository

git clone [https://github.com/SaimonNeupane/rural-road-planner.git](https://github.com/SaimonNeupane/rural-road-planner.git)
cd rural-road-planner