🛰️ Multimodal Geospatial Deep Learning for Fine-Grain Urban House Price Prediction
A production-grade AI system that predicts house prices using Multimodal Deep Learning + Geospatial Intelligence + Market Tier Modeling.
This platform combines:
🧠 Deep Learning (CNN + Tabular Fusion) 🌍 Geospatial Reachability 🇮🇳 Indian Real Estate Market Intelligence to deliver hyper-local, explainable property valuations.
🚀 🔥 Core Features 🧠 Deep Learning Inference Engine Multimodal Architecture: CNN (image) + Tabular features Feature Fusion using Dense Neural Layers Handles both structural + visual signals
🌍 Geospatial Intelligence 📍 Live Map Rendering (PyDeck + Mapbox) 🛰️ Isochrone Reachability Simulation 10-min walk radius 15-min drive radius Location-aware valuation 🇮🇳 10-Tier City Intelligence System Dynamic classification of Indian cities:
| Tier | Multiplier | Description |
|---|---|---|
| Tier 1 | 4.0x | Premium metros |
| Tier 2 | 3.2x | Emerging metros |
| Tier 3–5 | 2.6x – 1.9x | Growth cities |
| Tier 6–8 | 1.7x – 1.3x | Developing |
| Tier 9–10 | 1.2x – 1.1x | Low liquidity |
✔ Automatically applied based on location input ✔ Real-world pricing simulation
🧠 AI Explainability (Stable XAI)
Instead of unstable SHAP for multimodal models:
✔ Feature Contribution Visualization ✔ BHK vs Area vs Structure importance ✔ Interpretable surrogate explanation 📊 Advanced Analytics Dashboard
Below-map intelligence layer includes:
💰 Price Breakdown (Base + Premium + Growth) 📈 5-Year ROI Projection 🏙️ Area Intelligence Radar (Safety, Greenery, Transit) 📊 Market Trends Simulation 🧠 AI Investment Recommendations 🧠 Smart AI Recommendation Engine
Dynamic decision support based on:
City Tier Safety score Transit accessibility
Example outputs:
🚀 “Premium investment zone”
📈 “Emerging growth market”
🏗️ System Architecture: Tabular Data (BHK, SqFt, Location) │ ▼ Feature Processing │ ▼ Tabular Neural Network │
Image Data (House Images) │ ▼ CNN (ResNet18) │
Feature Fusion
(Concatenation Layer) │ ▼ Fully Connected Layers │ ▼ Final Price Prediction
📊 Model Architecture CNN Backbone: ResNet18 Tabular Network: Fully Connected Layers Fusion: Concatenation + Dense Layers Loss: Mean Squared Error (MSE)
📈 Model Evaluation Metrics
| Metric | Description |
|---|---|
| RMSE | Error magnitude |
| MAE | Absolute deviation |
| R² Score | Model accuracy |
🧪 Model Comparison
| Model | RMSE | R² |
|---|---|---|
| ML (RandomForest) | High | Medium |
| Deep Learning | Medium | High |
| Geospatial DL | Lowest | Highest 🚀 |
🛠️ Tech Stack 🔹 Frontend Streamlit (Interactive Dashboard) 🔹 Deep Learning PyTorch Torchvision (ResNet18) 🔹 Geospatial PyDeck (Mapbox) Geopy 🔹 Visualization Plotly Radar, Bar, ROI graphs 🔹 Backend (Optional) FastAPI 🔹 CI/CD GitHub Actions flake8 (lint) pytest (testing)
Multimodal_Geospatial_House_Price_Project/ ├── .github/workflows/ # CI/CD pipelines ├── data/ │ ├── dataset.py # PyTorch Dataset class │ ├── data.csv # Primary tabular dataset │ ├── images/ # Satellite/Property imagery │ └── generate_images.py # Script for image preprocessing ├── models/ │ ├── multimodal_model.py # Fusion architecture (CNN + Tabular) │ ├── cnn_model.py # Visual feature extractor │ └── tabular_model.py # Numerical feature processor ├── evaluation/ # Metrics and model comparison scripts ├── explainability/ # SHAP/LIME implementation for XAI ├── mlruns/ # MLflow experiment tracking logs ├── api.py # FastAPI backend ├── app.py # Streamlit frontend dashboard ├── graph_features.py # Geospatial relationship logic ├── train.py # Main training entry point ├── requirements.txt # Python dependencies └── README.md # Project documentation
🚀 Installation & Setup
git clone https://github.com/YeswanthVelpuru/Multimodal_Geospatial_House_Price_Project.git
cd Multimodal_Geospatial_House_Price_Project
pip install -r requirements.txt
streamlit run app.py python train.py
🌐 Deployment Streamlit Cloud ready GitHub Actions CI integrated Mapbox API enabled
🧠 Key Innovation : “Fusion of Multimodal Deep Learning with Geospatial Reachability and Tier-Based Market Intelligence for fine-grain urban price prediction.”