Getting Started with ClimateVision Development

Welcome to the ClimateVision team! This guide will help you get up and running quickly.

🚀 Quick Setup

1. Clone and Install

# Clone the repository
git clone https://github.com/yourusername/ClimateVision.git
cd ClimateVision

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

# Install dependencies
pip install --upgrade pip
pip install -r requirements.txt
pip install -r requirements-dev.txt

# Install in editable mode
pip install -e .

2. Verify Installation

# Test imports
python -c "from climatevision.models import UNet; print('✓ Models module working')"
python -c "from climatevision.utils import calculate_iou; print('✓ Utils module working')"

# Run quick test
python -c "
import torch
from climatevision.models import UNet

model = UNet(n_channels=13, n_classes=2)
x = torch.randn(1, 13, 256, 256)
y = model(x)
print(f'✓ Model forward pass: {y.shape}')
"

3. Try the Quickstart Notebook

jupyter notebook notebooks/01_quickstart.ipynb

👥 Team Roles & First Tasks

Technical Lead (You)

Focus: Architecture, code review, integration

Week 1 Tasks:

Code to Review: All modules

Data Science Engineer 1 - ML Model Development

Focus: Model architectures, training, optimization

Week 1-2 Tasks:

Create training loop (training/trainer.py)
Add model checkpointing (training/checkpointing.py)
Implement evaluation metrics logging
Test U-Net training on dummy data
Document hyperparameters

Files to Create:

training/
├── trainer.py           # Main training loop
├── evaluator.py         # Model evaluation  
├── callbacks.py         # Training callbacks
└── checkpointing.py     # Save/load models

Example Task - Training Loop:

# training/trainer.py
import torch
from torch.utils.data import DataLoader
from climatevision.models import UNet
from climatevision.utils.metrics import calculate_segmentation_metrics

class Trainer:
    def __init__(self, model, optimizer, criterion, device='cuda'):
        self.model = model
        self.optimizer = optimizer
        self.criterion = criterion
        self.device = device
    
    def train_epoch(self, dataloader: DataLoader):
        self.model.train()
        total_loss = 0
        
        for batch in dataloader:
            images, masks = batch
            images = images.to(self.device)
            masks = masks.to(self.device)
            
            # Forward pass
            self.optimizer.zero_grad()
            outputs = self.model(images)
            loss = self.criterion(outputs, masks)
            
            # Backward pass
            loss.backward()
            self.optimizer.step()
            
            total_loss += loss.item()
        
        return total_loss / len(dataloader)

Data Science Engineer 2 - Data Pipeline & MLOps

Focus: Data loading, preprocessing, pipeline optimization

Week 1-2 Tasks:

Implement Sentinel-2 data loader (data/sentinel2.py)
Create PyTorch Dataset class (data/dataset.py)
Add preprocessing pipeline (data/preprocess.py)
Implement data augmentation (data/augmentation.py)
Document data formats

Files to Create:

data/
├── sentinel2.py         # Sentinel-2 API wrapper
├── landsat.py          # Landsat API wrapper
├── dataset.py          # PyTorch Dataset
├── preprocess.py       # Image preprocessing
├── augmentation.py     # Data augmentation
└── utils.py            # Data utilities

Example Task - Dataset Class:

# data/dataset.py
import torch
from torch.utils.data import Dataset
import numpy as np
from pathlib import Path

class ForestSegmentationDataset(Dataset):
    """Dataset for forest segmentation"""
    
    def __init__(self, data_dir: str, transform=None):
        self.data_dir = Path(data_dir)
        self.transform = transform
        self.image_files = list(self.data_dir.glob('images/*.tif'))
        self.mask_files = list(self.data_dir.glob('masks/*.tif'))
    
    def __len__(self):
        return len(self.image_files)
    
    def __getitem__(self, idx):
        # Load image and mask
        image = self._load_image(self.image_files[idx])
        mask = self._load_mask(self.mask_files[idx])
        
        if self.transform:
            image, mask = self.transform(image, mask)
        
        return torch.from_numpy(image), torch.from_numpy(mask)
    
    def _load_image(self, path):
        # TODO: Implement with rasterio
        pass
    
    def _load_mask(self, path):
        # TODO: Implement with rasterio
        pass

Data Science Engineer 3 - Carbon Analytics & Validation

Focus: Regression models, statistical analysis, validation

Week 1-2 Tasks:

Implement carbon estimation model (models/carbon_estimator.py)
Add Random Forest regressor
Create validation framework (validation/validator.py)
Implement uncertainty quantification
Document carbon calculation methodology

Files to Create:

models/
└── carbon_estimator.py  # Carbon estimation models

validation/
├── validator.py         # Cross-validation
├── uncertainty.py       # Uncertainty quantification
└── metrics.py          # Regression metrics

Example Task - Carbon Estimator:

# models/carbon_estimator.py
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from climatevision.utils.geospatial import calculate_carbon_loss

class CarbonEstimator:
    """Estimate carbon stock and loss from deforestation"""
    
    def __init__(self):
        self.model = RandomForestRegressor(
            n_estimators=100,
            max_depth=10,
            random_state=42
        )
    
    def train(self, features: np.ndarray, targets: np.ndarray):
        """
        Train carbon estimation model
        
        Args:
            features: Forest features (NDVI, height, etc.)
            targets: Carbon density (tons/ha)
        """
        self.model.fit(features, targets)
    
    def predict(self, features: np.ndarray) -> np.ndarray:
        """Predict carbon density"""
        return self.model.predict(features)
    
    def predict_with_uncertainty(self, features: np.ndarray):
        """Predict with uncertainty estimates"""
        # Implement bootstrap or ensemble uncertainty
        pass

Data Science Engineer 4 - API & Deployment

Focus: Model serving, API development, deployment

Week 1-2 Tasks:

Create inference pipeline (inference/predictor.py)
Implement batch processing (inference/batch_processor.py)
Set up FastAPI application (api/main.py)
Add prediction endpoint
Write deployment documentation

Files to Create:

inference/
├── predictor.py         # Single image prediction
├── batch_processor.py   # Batch processing
├── postprocess.py       # Post-processing
└── onnx_optimizer.py    # ONNX optimization

api/
├── main.py             # FastAPI app
├── routes.py           # API endpoints
├── models.py           # Pydantic models
└── serving.py          # Model serving

Example Task - API Endpoint:

# api/main.py
from fastapi import FastAPI, UploadFile, File
from fastapi.responses import JSONResponse
import numpy as np
from climatevision.inference import Predictor

app = FastAPI(title="ClimateVision API")
predictor = Predictor()

@app.post("/predict/segmentation")
async def predict_segmentation(file: UploadFile = File(...)):
    """
    Predict forest segmentation for uploaded satellite image
    """
    # Load image
    contents = await file.read()
    image = np.frombuffer(contents, dtype=np.uint8)
    
    # Run prediction
    result = predictor.predict(image)
    
    return JSONResponse({
        "forest_area_ha": result["forest_area"],
        "deforested_area_ha": result["deforested_area"],
        "confidence": result["confidence"]
    })

@app.get("/health")
async def health_check():
    return {"status": "healthy"}

🔧 Development Workflow

Daily Workflow

# 1. Start your day
git checkout main
git pull origin main

# 2. Create feature branch
git checkout -b feat/your-feature-name

# 3. Make changes, commit often
git add .
git commit -m "feat: implement data loader for Sentinel-2"

# 4. Run tests before pushing
pytest tests/
black src/
flake8 src/

# 5. Push and create PR
git push origin feat/your-feature-name
# Create Pull Request on GitHub

Code Review Process

Self-review your code first
Write tests for new functionality
Update documentation (docstrings, README)
Request review from team lead
Address feedback promptly
Merge once approved

Testing Your Code

# Run all tests
pytest tests/ -v

# Run with coverage
pytest --cov=climatevision --cov-report=html tests/

# Run specific test
pytest tests/test_models.py::test_unet_forward -v

# Test your module only
pytest tests/test_data/ -v

📊 Communication

Daily Standups (15 min)

What did you do yesterday?
What will you do today?
Any blockers?

Weekly Sprint Planning (Monday)

Review last week's progress
Plan this week's tasks
Assign responsibilities

Code Reviews (Within 24 hours)

Review each other's PRs
Provide constructive feedback
Ask questions if unclear

Demo (Friday)

Show what you built this week
Get feedback from team
Celebrate wins!

🎯 Week 1 Goals

Team Goal

✅ Have a working end-to-end pipeline (even with dummy data)

Individual Goals

Engineer 1: Train U-Net on synthetic data
Engineer 2: Load and preprocess one Sentinel-2 tile
Engineer 3: Implement basic carbon estimator
Engineer 4: Set up API with one prediction endpoint

📚 Learning Resources

Must-Read

README.md - Project overview
PROJECT_STRUCTURE.md - Codebase organization
CONTRIBUTING.md - Development guidelines

PyTorch Tutorials

Satellite Imagery

MLOps

❓ FAQ

Q: Which Python version should I use?
A: Python 3.8 or higher. Python 3.10 recommended.

Q: Can I use a different IDE?
A: Yes! VSCode, PyCharm, or any editor works fine.

Q: How do I get satellite data?
A: We'll use Sentinel Hub API (free tier available) or Google Earth Engine.

Q: What if I'm stuck?
A: 1) Check documentation, 2) Ask in team chat, 3) Open GitHub Discussion

Q: Can I work on multiple tasks?
A: Focus on one task at a time. Finish before starting another.

Q: How often should I commit?
A: Commit early and often! At least once per day with working code.

🎉 You're Ready!

Pick your first task, create a branch, and start coding!

Remember:

Ask questions early
Commit often
Test your code
Document as you go
Have fun building something impactful! 🌍

Need help? Tag @technical-lead in GitHub or Slack

Provide feedback

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Getting Started with ClimateVision Development

🚀 Quick Setup

1. Clone and Install

2. Verify Installation

3. Try the Quickstart Notebook

👥 Team Roles & First Tasks

Technical Lead (You)

Data Science Engineer 1 - ML Model Development

Data Science Engineer 2 - Data Pipeline & MLOps

Data Science Engineer 3 - Carbon Analytics & Validation

Data Science Engineer 4 - API & Deployment

🔧 Development Workflow

Daily Workflow

Code Review Process

Testing Your Code

📊 Communication

Daily Standups (15 min)

Weekly Sprint Planning (Monday)

Code Reviews (Within 24 hours)

Demo (Friday)

🎯 Week 1 Goals

Team Goal

Individual Goals

📚 Learning Resources

Must-Read

PyTorch Tutorials

Satellite Imagery

MLOps

❓ FAQ

🎉 You're Ready!

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GETTING_STARTED.md

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Getting Started with ClimateVision Development

🚀 Quick Setup

1. Clone and Install

2. Verify Installation

3. Try the Quickstart Notebook

👥 Team Roles & First Tasks

Technical Lead (You)

Data Science Engineer 1 - ML Model Development

Data Science Engineer 2 - Data Pipeline & MLOps

Data Science Engineer 3 - Carbon Analytics & Validation

Data Science Engineer 4 - API & Deployment

🔧 Development Workflow

Daily Workflow

Code Review Process

Testing Your Code

📊 Communication

Daily Standups (15 min)

Weekly Sprint Planning (Monday)

Code Reviews (Within 24 hours)

Demo (Friday)

🎯 Week 1 Goals

Team Goal

Individual Goals

📚 Learning Resources

Must-Read

PyTorch Tutorials

Satellite Imagery

MLOps

❓ FAQ

🎉 You're Ready!