https://github.com/yashpotdar-py/flood-vision
Flood Vision - A deep learning–based computer vision system for flood mapping and damage assessment using aerial imagery.
https://github.com/yashpotdar-py/flood-vision
cuda deep-learning flood-detection iot python
Last synced: 2 months ago
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Flood Vision - A deep learning–based computer vision system for flood mapping and damage assessment using aerial imagery.
- Host: GitHub
- URL: https://github.com/yashpotdar-py/flood-vision
- Owner: yashpotdar-py
- Created: 2025-10-05T16:16:19.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-10-05T18:53:32.000Z (9 months ago)
- Last Synced: 2025-10-13T10:55:09.409Z (8 months ago)
- Topics: cuda, deep-learning, flood-detection, iot, python
- Language: Python
- Homepage:
- Size: 38.1 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
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README
# 🌊 Flood Vision: Flood Mapping and Damage Segmentation System
> **Flood Vision** - A deep learning–based computer vision system for flood mapping and damage assessment using aerial imagery.
---
## 🧭 Overview
**Flood Vision** is a computer vision system designed to automatically analyze aerial or drone footage from flood-affected regions.
It uses **semantic segmentation** to identify and label key terrain types such as **water**, **flooded buildings**, **vegetation**, and **roads**, helping researchers and responders estimate affected areas efficiently.
The project focuses on **robust image segmentation** and **high-speed inference** on CUDA-enabled devices.
---
## ⚙️ Features Implemented
- ✅ 4-class **semantic segmentation model**
- ✅ **Weighted loss** for handling class imbalance
- ✅ **CUDA acceleration** for efficient GPU training
- ✅ **Video and image inference**
- ✅ **Color-coded visual outputs**
- ✅ **FloodNet dataset** integration
- ✅ Modular and extendable PyTorch architecture
---
## 🧪 Tech Stack
| Component | Technology |
|------------|-------------|
| **Language** | Python 3.13 |
| **Framework** | PyTorch |
| **GPU Acceleration** | NVIDIA CUDA |
| **Data Augmentation** | Albumentations |
| **Video/Image Processing** | OpenCV |
| **Visualization** | Matplotlib |
| **Loss Function** | Weighted Cross-Entropy |
---
## 🗂️ Dataset
This project uses the **FloodNet** dataset for supervised training.
📚 **Dataset Link:**
[https://github.com/BinaLab/FloodNet-Supervised_v1.0](https://github.com/BinaLab/FloodNet-Supervised_v1.0)
### 📖 Citation
```bibtex
@ARTICLE{9460988,
author={Rahnemoonfar, Maryam and Chowdhury, Tashnim and Sarkar, Argho and Varshney, Debvrat and Yari, Masoud and Murphy, Robin Roberson},
journal={IEEE Access},
title={FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding},
year={2021},
volume={9},
pages={89644-89654},
doi={10.1109/ACCESS.2021.3090981}
}
````
---
## 🧰 Installation
### 1️⃣ Clone the repository
```bash
git clone https://github.com/yashpotdar-py/flood-vision.git
cd flood-vision
```
### 2️⃣ Create and activate a virtual environment
```bash
python -m venv .venv
.\.venv\Scripts\activate
```
### 3️⃣ Install dependencies
```bash
pip install -r requirements.txt
```
### 4️⃣ (Optional) Verify CUDA availability
```bash
python -c "import torch; print(torch.cuda.is_available())"
```
---
## 🏋️♂️ Training
Train the segmentation model with:
```bash
python -m train.train_segmentation_4class
```
The training script:
- Loads images and masks from the FloodNet dataset
- Applies Albumentations-based augmentations
- Uses a **weighted loss** for balanced learning
- Logs metrics like IoU and validation loss
---
## 🔍 Inference
Run segmentation on an image or video:
```bash
python -m inference.run_inference --input data/videos/test1.mp4 --output results/output.mp4
```
### Color Legend
| Class | Color | Description |
| ----------------- | --------- | --------------------|
| Water | 🟦 Blue | Flooded water areas|
| Flooded Buildings | 🟥 Red | Buildings under water|
| Vegetation & Roads| 🟩 Green | Trees, fields, grass, Streets and infrastructure|
The processed video will be saved in `results/output_videos/`.
---
## 📁 Project Structure
```plaintext
flood-vision/
│
├── data/
│ ├── images/
│ ├── masks/
│ └── videos/
│
├── train/
│ ├── train_segmentation_4class.py
│ └── utils/
│
├── inference/
│ ├── run_inference.py
│
├── models/
│ ├── unet.py
│ └── loss_utils.py
│
├── results/
│ └── output_videos/
│
└── README.md
```
---
## 📊 Sample Metrics (Training Snapshot)
| Metric | Value |
| --------------- | ----- |
| Mean IoU | 0.74 |
| Pixel Accuracy | 0.90 |
| Validation Loss | 0.27 |
---
## 👨💻 Authors
**Developed by:**
- **Yash Potdar** - Model architecture & deployment, CUDA optimizations
- **Sahil Pawar** - Data preprocessing, model training, testing& evaluation
- **Akanksha Singh** - Web interface design, documentation, and system integration
**Affiliation:**
🎓 Final-Year B.E. Project,
Department of Artiticial Intelligence & Data Science
AISSMS Institute of Information Technology (AISSMS)
---
## 📜 License
This project is intended for **academic and research purposes**.
The **FloodNet dataset** belongs to its original authors.
Please cite the dataset and this repository when used in derivative works.
---
## 🙏 Acknowledgments
- [FloodNet Dataset](https://github.com/BinaLab/FloodNet-Supervised_v1.0)
- [PyTorch](https://pytorch.org/)
- [Albumentations](https://albumentations.ai/)
- [OpenCV](https://opencv.org/)
---
> *"Mapping floods through vision - towards smarter, data-driven disaster response."*
---