Skip to content

QUICKSTART.md

Latest commit

 

History

History
288 lines (225 loc) · 6.94 KB

QUICKSTART.md

File metadata and controls

288 lines (225 loc) · 6.94 KB
Raw

ClimateVision - Quick Start Guide

Project Setup (First Time)

1. Clone the Repository

git clone https://github.com/yourusername/ClimateVision.git
cd ClimateVision

2. Create Virtual Environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies

# Install core dependencies
pip install -r requirements.txt

# Install in development mode
pip install -e .

4. Verify Installation

python -c "import climatevision; print(climatevision.__version__)"

Development Workflow

Project Structure

ClimateVision/
├── src/climatevision/       # Main package
│   ├── data/               # Data loading and preprocessing
│   ├── models/             # Model architectures
│   ├── inference/          # Inference pipelines
│   ├── api/                # REST API
│   └── visualization/      # Plotting and visualization
├── scripts/                # Training and utility scripts
├── notebooks/              # Jupyter notebooks for experiments
├── tests/                  # Unit tests
├── docs/                   # Documentation
├── models/                 # Saved model weights
├── data/                   # Datasets (not tracked in git)
└── config.yaml             # Configuration file

Running Tests

# Test data loading
python src/climatevision/data/loader.py

# Test model
python src/climatevision/models/unet.py

# Test detector
python src/climatevision/models/detector.py

Team Roles & Tasks

Data Science Engineer 1 - ML Model Development

Focus: Deep learning models and experimentation

Initial Tasks:

  1. Review and enhance U-Net architecture in src/climatevision/models/unet.py
  2. Implement Siamese network for change detection
  3. Set up experiment tracking with MLflow/Wandb
  4. Create model benchmarking framework
  5. Research and implement latest segmentation architectures

Key Files:

  • src/climatevision/models/unet.py
  • src/climatevision/models/change_detector.py (to create)
  • scripts/train.py

Data Science Engineer 2 - Data Pipeline & MLOps

Focus: Data engineering and infrastructure

Initial Tasks:

  1. Implement Sentinel Hub API integration in src/climatevision/data/loader.py
  2. Build distributed preprocessing pipeline with Dask
  3. Set up DVC for data versioning
  4. Create automated data validation pipeline
  5. Implement feature engineering (NDVI, EVI, etc.)

Key Files:

  • src/climatevision/data/loader.py
  • src/climatevision/data/preprocessing.py (to create)
  • src/climatevision/data/sentinel_api.py (to create)

Data Science Engineer 3 - Statistical Modeling & Analytics

Focus: Carbon estimation and validation

Initial Tasks:

  1. Develop carbon stock estimation models
  2. Implement biomass regression using Random Forest/XGBoost
  3. Create uncertainty quantification framework
  4. Build validation pipeline against ground truth data
  5. Develop impact reporting system

Key Files:

  • src/climatevision/models/carbon_estimator.py (to create)
  • src/climatevision/analytics/ (to create)
  • notebooks/carbon_modeling.ipynb (to create)

Data Science Engineer 4 - API & Deployment

Focus: Production infrastructure

Initial Tasks:

  1. Build FastAPI backend in src/climatevision/api/
  2. Implement model serving with ONNX
  3. Create batch prediction pipeline
  4. Set up monitoring and logging
  5. Deploy with Docker and write deployment docs

Key Files:

  • src/climatevision/api/main.py (to create)
  • src/climatevision/api/models.py (to create)
  • Dockerfile (to create)
  • docker-compose.yml (to create)

Week 1-2 Sprint Plan

All Team Members

  • Set up development environment
  • Review existing code and architecture
  • Read relevant research papers
  • Create initial Jupyter notebooks

Monday: Sprint Kickoff

  • Architecture review meeting
  • Task assignment and sprint planning
  • Git workflow discussion

Wednesday: Technical Deep Dive

  • Code walkthrough
  • Discuss data sources and APIs
  • Model architecture decisions

Friday: Week 1 Demo

  • Show progress on assigned tasks
  • Code review
  • Adjust plan for Week 2

Git Workflow

Branching Strategy

main                    # Production-ready code
├── develop            # Integration branch
    ├── feature/data-pipeline
    ├── feature/unet-model
    ├── feature/api
    └── feature/carbon-estimation

Making Changes

# Create feature branch
git checkout develop
git pull origin develop
git checkout -b feature/your-feature-name

# Make changes and commit
git add .
git commit -m "Add: brief description of changes"

# Push and create PR
git push origin feature/your-feature-name
# Then create Pull Request on GitHub

Commit Message Convention

Add: New feature or file
Fix: Bug fix
Update: Modify existing feature
Refactor: Code restructuring
Docs: Documentation changes
Test: Add or modify tests

Useful Commands

Data Download (When API is ready)

from climatevision.data.loader import load_sentinel2_image

image = load_sentinel2_image(
    coordinates=(-3.4653, -62.2159, -3.0653, -61.8159),
    date_range=("2024-01-01", "2024-01-31"),
    cloud_coverage_max=20
)

Training Model

# When dataset is ready
python scripts/train.py \
    --data-dir data/forest_dataset \
    --epochs 50 \
    --batch-size 8 \
    --lr 0.0001

Running Inference

from climatevision import ForestDetector

detector = ForestDetector(model_path="models/best_model.pth")
result = detector.predict(image)
stats = result.get_statistics()
result.plot(save_path="output.png")

Resources

Documentation

Research Papers

  • U-Net: Convolutional Networks for Biomedical Image Segmentation
  • Focal Loss for Dense Object Detection
  • Deep Learning for Forest Monitoring

Datasets

Troubleshooting

GDAL Installation Issues

# Ubuntu/Debian
sudo apt-get install gdal-bin libgdal-dev

# macOS
brew install gdal

CUDA Not Available

# Check PyTorch CUDA installation
python -c "import torch; print(torch.cuda.is_available())"

# Reinstall PyTorch with CUDA support
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

Import Errors

# Reinstall in development mode
pip install -e .

Getting Help

  • Technical Questions: Post in GitHub Discussions
  • Bugs: Create GitHub Issue
  • Team Chat: Use Slack/Discord channel
  • Weekly Sync: Monday 10 AM

Let's build something amazing! 🌍🛰️