GitHub - garimasikka/SwachhZero: SwachhZero is building an ecosystem that makes the "good" action (segregation and reuse) the easiest, most convenient, and most profitable choice for the consumer.

AI Platform for Planetary Well-Being

SwachhZero is an open-source AI platform that unifies multi-modal Earth observation data, physics-informed machine learning, and policy optimization to enable real-time monitoring, prediction, and decision-making for planetary health. Our mission is to democratize access to state-of-the-art environmental intelligence — empowering researchers, governments, NGOs, and citizens to understand and protect the natural systems that sustain life on Earth.

Important

This is a vision and proposal system — a comprehensive architectural blueprint and codebase skeleton for what SwachhZero aims to become. The code is untested in production and actively evolving. We are building this dream into reality, one commit at a time. Open to contribution, collaboration, and building together. Join us.

🌏 Vision

In the making for a decade, built with love. 🤍

Earth's interconnected environmental systems — climate, biodiversity, water, air quality, and oceans — are under unprecedented stress. Traditional monitoring approaches are fragmented: satellite data lives in one silo, ground sensors in another, socioeconomic indicators in yet another. Decision-makers lack the integrated, real-time intelligence needed to respond effectively to accelerating ecological crises.

SwachhZero exists to close that gap — born from a deep love for Earth.

Our north star is the United Nations 17 Sustainable Development Goals, with direct focus on SDG 6 (Clean Water & Sanitation), SDG 7 (Affordable & Clean Energy), SDG 11 (Sustainable Cities & Communities), SDG 13 (Climate Action), SDG 14 (Life Below Water), and SDG 15 (Life on Land).

Our core mission: The healthier longevity of our planet and all life upon it. 🌱

We are building a unified AI platform that ingests heterogeneous environmental data streams — from satellite imagery and ocean buoys to acoustic sensors and economic indicators — and synthesizes them through a suite of foundation models, physics-informed neural networks, and graph-based ecosystem models. The result is a continuously updated, scientifically rigorous understanding of planetary state that can power everything from local air-quality alerts to national climate policy optimization.

This is monitoring not for exploitation but for care. Prediction not for profit but for protection. Intelligence not for control but for stewardship.

🌌 A Pale Blue Dot

"Look again at that dot. That's here. That's home. That's us. On it everyone you love, everyone you know, everyone you ever heard of, every human being who ever was, lived out their lives… on a mote of dust suspended in a sunbeam." — Carl Sagan, Pale Blue Dot

Earth is a tiny, fragile speck in the vast cosmic arena — and yet it is everything we have. Every breath, every ocean, every forest, every child's laughter exists on this one irreplaceable world. There is no backup planet. There is no Plan B. The awareness of our cosmic smallness should not fill us with despair but with fierce determination: what we have is precious beyond measure, and it is worth protecting with everything we are.

SwachhZero is born from this awareness — that technology, when guided by love and purpose, can become the greatest ally our planet has ever known.

Plant a tree today, so future generations may sit in its shade. 🌳

Why hate, when we can love. Why destroy, when we can build. Why discard, when we can fix. 💫

We are the solution. Let us be the healers and guardians of Planet Earth — mother to all. 🌏

The long-term sustainability of our planet and all its life forms — from the smallest microorganism to the great whales, from ancient forests to the species Homo sapiens — depends on the choices we make today. SwachhZero is one such choice: to build intelligence that serves life, not profit; to create tools that heal, not harm; to leave behind a world better than the one we inherited.

This project is designed for researchers seeking reproducible, state-of-the-art baselines for Earth system modeling; government agencies needing operational environmental monitoring dashboards; NGOs working on conservation, water security, or climate adaptation; and citizens who deserve transparent, accessible information about the health of their environment. SwachhZero is built in the open because planetary well-being is a shared responsibility — and the tools to protect it should be shared too.

🌍 Aligned with UN Sustainable Development Goals

SwachhZero is built in service of the United Nations 17 Sustainable Development Goals — the world's shared blueprint for peace and prosperity for people and the planet.

SDG	Goal	SwachhZero Contribution	Status
1	No Poverty	Indirect — climate resilience supports poverty reduction	🔄
2	Zero Hunger	Agricultural monitoring, crop health via satellite imagery	✅
3	Good Health & Well-Being	Air quality monitoring, pollution tracking, health correlation	✅
4	Quality Education	Indirect — open data & tools for environmental education	🔄
5	Gender Equality	Indirect — open access democratizes environmental intelligence	🔄
6	Clean Water & Sanitation	Water quality monitoring, watershed pollution tracking	✅
7	Affordable & Clean Energy	Renewable energy optimization, energy policy simulation	✅
8	Decent Work & Economic Growth	Indirect — green economy analytics	🔄
9	Industry, Innovation & Infrastructure	AI infrastructure for environmental monitoring at scale	✅
10	Reduced Inequalities	Indirect — equitable access to environmental data	🔄
11	Sustainable Cities & Communities	Urban heat mapping, waste management, air quality dashboards	✅
12	Responsible Consumption & Production	Indirect — waste tracking and circular economy metrics	🔄
13	Climate Action	Climate modeling, carbon tracking, policy optimization	✅
14	Life Below Water	Ocean monitoring, marine ecosystem health, coral tracking	✅
15	Life on Land	Biodiversity monitoring, deforestation detection, ecosystem GNNs	✅
16	Peace, Justice & Strong Institutions	Indirect — transparent environmental governance data	🔄
17	Partnerships for the Goals	Indirect — open-source collaboration, federated learning	🔄

✅ = Direct contribution | 🔄 = Indirect contribution

✨ Key Features

🛰️ Multi-Modal Earth Observation Satellite, sensor, ocean, atmosphere, acoustic, and socioeconomic data ingestion with unified catalog	🧠 Earth Foundation Model Multi-modal transformer for planetary understanding with cross-modal fusion and pre-training	⚛️ Physics-Informed ML PINNs and neural operators constrained by physical conservation laws
🌿 Ecosystem Intelligence Graph neural networks for biodiversity modeling, species interaction, and ecosystem health	🎯 Policy Optimization Reinforcement learning for climate adaptation, water management, and urban sustainability	🔒 Privacy-Preserving Federated learning with differential privacy via Flower and Opacus
🌍 Interactive Dashboard Real-time monitoring with maps, time-series charts, and geospatial visualizations	🔌 Extensible API REST + GraphQL + WebSocket for seamless integration with external systems	📊 Production-Ready Docker, Kubernetes, Terraform, CI/CD, Prometheus, and Grafana
📖 Comprehensive Documentation — 16 documentation files covering architecture, data, models, deployment, and API

🏛️ Architecture Overview

graph TD
    subgraph Data Sources
        SAT[Satellite - Sentinel-2, Landsat, MODIS]
        SEN[Sensors - Air Quality, Weather]
        OCE[Ocean - Argo Floats, Buoys]
        ATM[Atmosphere - ERA5, CAMS]
        ACO[Acoustic - Biodiversity Audio]
        SOC[Socioeconomic - World Bank, UN SDG]
    end

    subgraph Processing
        ING[Data Ingestion]
        VAL[Validation and QA]
        TRANS[Transform Pipeline]
        STORE[(Multi-Database Storage)]
    end

    subgraph AI Models
        FM[Earth Foundation Model]
        PINN[Physics-Informed NNs]
        GNN[Ecosystem GNNs]
        RL[Policy RL]
        FL[Federated Learning]
    end

    subgraph Services
        API[REST and GraphQL API]
        INF[Inference Engine]
        MON[Monitoring]
        DASH[Dashboard]
    end

    SAT --> ING
    SEN --> ING
    OCE --> ING
    ATM --> ING
    ACO --> ING
    SOC --> ING

    ING --> VAL --> TRANS --> STORE

    STORE --> FM
    STORE --> PINN
    STORE --> GNN
    STORE --> RL
    FL -.-> FM

    FM --> INF
    PINN --> INF
    GNN --> INF
    RL --> INF

    INF --> API
    API --> DASH
    MON --> DASH

Technology Stack

Layer	Technologies
Languages	Python 3.12+, TypeScript 5
ML Frameworks	PyTorch 2.5, JAX 0.4, Flax, Transformers, PyTorch Lightning
Data Processing	Dask, Polars, xarray, GeoPandas, rasterio, Zarr
API	FastAPI, Strawberry GraphQL, WebSocket
Frontend	Next.js 15, React 19, Tailwind CSS, Recharts, Leaflet
Databases	PostgreSQL 16 + PostGIS, TimescaleDB, Neo4j 5, Redis 7
Storage	MinIO (S3-compatible), Apache Kafka
ML Ops	MLflow, Weights & Biases, Celery
Observability	Prometheus, Grafana, OpenTelemetry, structlog
Infrastructure	Docker, Kubernetes, Helm, Terraform

🚀 Quick Start

Prerequisites

Python 3.12 or higher
Docker 24+ and Docker Compose v2
Git 2.40+
8 GB RAM minimum (16 GB recommended for model training)

Option 1: Docker Compose (Recommended)

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

# Copy environment configuration
cp .env.example .env

# Launch the full stack (API, frontend, databases, monitoring)
docker compose up -d

# Verify all services are healthy
docker compose ps

# Check API health
curl http://localhost:8000/api/health

The dashboard will be available at http://localhost:3000 and the API at http://localhost:8000/docs.

Option 2: pip Install

# Install the core package
pip install swachhzero

# Install with ML dependencies
pip install "swachhzero[ml]"

# Install everything
pip install "swachhzero[all]"

Option 3: From Source

# Clone and enter the project
git clone https://github.com/swachhzero/swachhzero.git
cd swachhzero

# Create a virtual environment
python -m venv .venv
source .venv/bin/activate  # Linux/macOS
# .venv\Scripts\activate   # Windows

# Install in development mode with all extras
pip install -e ".[all]"

# Run database migrations
swachhzero db upgrade

# Start the development server
swachhzero serve --reload

Verify Installation

# CLI health check
swachhzero health

# Expected output:
# ✅ API Server     — running on http://localhost:8000
# ✅ PostgreSQL     — connected
# ✅ Redis          — connected
# ✅ TimescaleDB    — connected
# ✅ MinIO          — connected

📝 Usage Examples

Ingesting Satellite Data

from swachhzero.data.ingestion.satellite import SatelliteIngestion
from swachhzero.core.config import Settings

settings = Settings()
ingestion = SatelliteIngestion(settings)

# Ingest Sentinel-2 imagery for a region of interest
result = await ingestion.ingest(
    source="sentinel-2",
    bbox=[77.5, 12.9, 77.7, 13.1],  # Bangalore
    date_range=("2026-01-01", "2026-01-31"),
    bands=["B02", "B03", "B04", "B08"],
)
print(f"Ingested {result.tile_count} tiles, {result.size_gb:.2f} GB")

Running a Prediction

from swachhzero.inference.engine import InferenceEngine

engine = InferenceEngine.from_pretrained("swachhzero/earth-foundation-v1")

prediction = await engine.predict(
    task="air_quality_forecast",
    location={"lat": 28.6139, "lon": 77.2090},  # New Delhi
    horizon_hours=48,
)
print(prediction.aqi_forecast)  # [156, 148, 142, ...]
print(prediction.confidence)     # 0.87

Simulating a Climate Scenario

from swachhzero.models.rl.environment import ClimateEnvironment
from swachhzero.models.rl.policy import PolicyAgent

env = ClimateEnvironment(region="south-asia", scenario="ssp245")
agent = PolicyAgent.load("swachhzero/policy-climate-v1")

# Run a 10-year simulation with policy interventions
trajectory = agent.simulate(env, steps=120)  # monthly steps
print(f"Projected temp anomaly: {trajectory.temperature_delta:.2f}°C")
print(f"Optimal interventions: {trajectory.top_actions[:5]}")

CLI Usage

# Ingest data from a configured source
swachhzero data ingest --source sentinel-2 --region bangalore --days 30

# Run inference on a saved dataset
swachhzero predict --model earth-foundation-v1 --input data/bangalore_jan.zarr

# Launch interactive dashboard
swachhzero dashboard --port 3000

# Export predictions to GeoJSON
swachhzero export --format geojson --output results/predictions.geojson

🔌 API Overview

The SwachhZero API exposes REST, GraphQL, and WebSocket endpoints. Full documentation is generated at /docs (Swagger) and /graphql (GraphiQL).

REST Endpoints

Method	Endpoint	Description
`GET`	`/api/health`	Service health and dependency status
`POST`	`/api/v1/data/ingest`	Trigger data ingestion pipeline
`GET`	`/api/v1/data/catalog`	Browse available datasets
`POST`	`/api/v1/predictions/create`	Run a prediction task
`GET`	`/api/v1/predictions/{id}`	Retrieve prediction results
`POST`	`/api/v1/scenarios/simulate`	Run a policy scenario simulation
`GET`	`/api/v1/scenarios/{id}`	Get simulation results
`POST`	`/api/v1/citizen/reports`	Submit a citizen science report
`WS`	`/api/v1/ws/stream`	Real-time prediction stream

Python Client

import httpx

client = httpx.AsyncClient(base_url="http://localhost:8000")

# Create a prediction
response = await client.post("/api/v1/predictions/create", json={
    "model": "earth-foundation-v1",
    "task": "land_cover_classification",
    "region": {"bbox": [77.5, 12.9, 77.7, 13.1]},
    "params": {"resolution_m": 10},
})
prediction = response.json()
print(prediction["id"], prediction["status"])

curl Example

# Health check
curl -s http://localhost:8000/api/health | python -m json.tool

# Submit a prediction request
curl -X POST http://localhost:8000/api/v1/predictions/create \
  -H "Content-Type: application/json" \
  -d '{
    "model": "earth-foundation-v1",
    "task": "air_quality_forecast",
    "region": {"lat": 28.6139, "lon": 77.2090},
    "params": {"horizon_hours": 48}
  }'

📁 Project Structure

swachhzero/
├── src/
│   ├── swachhzero/              # Python package root
│   │   ├── api/                 # FastAPI application, REST routes, GraphQL, WebSocket
│   │   │   ├── routes/          # REST endpoint handlers
│   │   │   ├── graphql/         # GraphQL schema, resolvers, types
│   │   │   └── websocket/       # Real-time data streams
│   │   ├── core/                # Configuration, logging, types, constants, exceptions
│   │   ├── data/                # Data layer
│   │   │   ├── ingestion/       # Source-specific ingestors (satellite, sensor, ocean, etc.)
│   │   │   ├── processing/      # Geospatial transforms, validation, normalization
│   │   │   ├── storage/         # Database adapters (PostGIS, TimescaleDB, Neo4j, MinIO)
│   │   │   └── catalog/         # Dataset registry and metadata
│   │   ├── models/              # ML model families
│   │   │   ├── foundation/      # Earth Foundation Model (transformer, encoders, fusion)
│   │   │   ├── physics/         # PINNs, neural operators, physical constraints
│   │   │   ├── graphs/          # Ecosystem GNNs, knowledge graphs
│   │   │   ├── rl/              # Policy optimization (environment, policy, rewards)
│   │   │   └── federated/       # Federated learning (client, server, privacy)
│   │   ├── inference/           # Inference engine, pipeline orchestration, caching
│   │   ├── monitoring/          # Metrics, alerts, distributed tracing
│   │   ├── tasks/               # Celery async tasks (ingestion, training)
│   │   └── cli.py               # Command-line interface
│   └── frontend/                # Next.js dashboard application
│       └── src/
│           ├── components/      # React components (map, charts, dashboard, layout)
│           └── lib/             # API client, hooks, state, types, utilities
├── tests/                       # Test suite
│   ├── unit/                    # Unit tests (API, core, models, data)
│   ├── integration/             # Integration tests (pipelines)
│   ├── e2e/                     # End-to-end tests
│   └── fixtures/                # Shared test fixtures
├── configs/                     # YAML configuration (default, production)
├── deploy/                      # Deployment manifests
│   ├── helm/                    # Kubernetes Helm chart
│   ├── terraform/               # Infrastructure-as-Code (AWS/GCP/Azure)
│   └── monitoring/              # Prometheus & Grafana configs
├── docker/                      # Dockerfiles (API, frontend, worker)
├── docs/                        # MkDocs documentation (16 files)
├── models/                      # Trained model artifacts and checkpoints
├── notebooks/                   # Research and exploration notebooks
├── scripts/                     # Utility and automation scripts
├── docker-compose.yml           # Full-stack local orchestration
├── pyproject.toml               # Python project metadata and tool config
├── Makefile                     # Developer convenience commands
└── mkdocs.yml                   # Documentation site configuration

🧠 Models

SwachhZero includes five families of models designed for complementary aspects of Earth system understanding:

Model Family	Architecture	Use Case	Status
Earth Foundation Model	Multi-modal Vision Transformer with cross-attention fusion	Unified representation learning across satellite, weather, and sensor modalities	✅ Architecture complete
Physics-Informed NNs	PINNs + Fourier Neural Operators	Fluid dynamics, atmospheric dispersion, and heat transfer governed by PDEs	✅ Architecture complete
Ecosystem GNNs	Heterogeneous Graph Neural Networks	Biodiversity modeling, species interaction networks, ecosystem health scoring	✅ Architecture complete
Policy RL Agents	PPO / SAC with multi-objective rewards	Climate adaptation strategy, water resource allocation, urban planning optimization	✅ Architecture complete
Federated Learning	FedAvg / FedProx with differential privacy	Privacy-preserving model training across distributed regional data sources	✅ Architecture complete

📄 Detailed model documentation: docs/models/

📡 Data Sources

SwachhZero ingests six modalities of environmental data through a unified ingestion framework:

Modality	Sources	Provider	Description
🛰️ Satellite Imagery	Sentinel-2, Landsat-9, MODIS	ESA Copernicus, NASA, Microsoft Planetary Computer	Multi-spectral optical imagery for land cover, vegetation, and urban analysis
📡 Ground Sensors	IoT air quality, weather stations	Government networks, OpenAQ, CPCB	Real-time PM2.5, NO₂, O₃, temperature, humidity, wind measurements
🌊 Ocean Data	Argo floats, sea-surface temperature, altimetry	NOAA, Copernicus Marine, ARGO	Ocean temperature profiles, salinity, currents, and sea level
🌤️ Atmosphere	ERA5 reanalysis, radiosonde, lidar	ECMWF, national weather services	3D atmospheric state — pressure, wind, moisture, radiation
🔊 Acoustic	Bioacoustic recorders, urban noise sensors	Research stations, municipal networks	Soundscape ecology for biodiversity and noise pollution assessment
📊 Socioeconomic	Census, GDP, health, land use	World Bank, UN, national statistics	Population density, economic activity, health outcomes, land-use change

📄 Data pipeline documentation: docs/data/

🛠️ Development

Environment Setup

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

# Create virtual environment
python -m venv .venv
source .venv/bin/activate

# Install all development dependencies
pip install -e ".[all]"

# Install pre-commit hooks
pre-commit install

# Start infrastructure services
docker compose up -d postgres redis timescaledb neo4j minio

Running Tests

# Run all tests
pytest

# Run with coverage report
pytest --cov=src/swachhzero --cov-report=html

# Run only unit tests
pytest tests/unit/ -v

# Run integration tests
pytest tests/integration/ -v

# Run tests in parallel
pytest -n auto

Code Style

SwachhZero enforces strict code quality via pyproject.toml:

# Lint with ruff
ruff check src/ tests/

# Auto-fix lint issues
ruff check --fix src/ tests/

# Format code
ruff format src/ tests/

# Type check with mypy
mypy src/swachhzero/

# Security scan
bandit -r src/swachhzero/

Pre-commit Hooks

All commits are validated by pre-commit hooks defined in .pre-commit-config.yaml:

ruff — linting and formatting
mypy — static type checking
bandit — security vulnerability scanning
trailing whitespace and end-of-file fixes

🚢 Deployment

Docker Compose (Local / Staging)

# Full stack with all services
docker compose up -d

# Production configuration
docker compose -f docker-compose.yml -f docker/docker-compose.dev.yml up -d

# View logs
docker compose logs -f api worker

# Scale workers
docker compose up -d --scale worker=4

Kubernetes with Helm

# Add the SwachhZero Helm repository
helm repo add swachhzero https://charts.swachhzero.earth

# Install to your cluster
helm install swachhzero swachhzero/swachhzero \
  --namespace swachhzero \
  --create-namespace \
  --values deploy/helm/swachhzero/values.yaml

# Upgrade with new values
helm upgrade swachhzero swachhzero/swachhzero \
  --namespace swachhzero \
  --values deploy/helm/swachhzero/values.yaml

📄 Helm chart: deploy/helm/swachhzero/

Cloud Deployment (Terraform)

cd deploy/terraform

# Initialize Terraform
terraform init

# Preview infrastructure changes
terraform plan -var-file="production.tfvars"

# Apply infrastructure
terraform apply -var-file="production.tfvars"

Terraform modules provision:

Managed Kubernetes cluster (EKS / GKE / AKS)
Managed PostgreSQL with PostGIS
Object storage (S3 / GCS / Azure Blob)
Kafka / event streaming
Monitoring stack (Prometheus + Grafana)
CDN and load balancer

📄 Terraform configuration: deploy/terraform/

🗺️ Roadmap

Quarter	Milestone	Key Deliverables	Status
Q1 2026	Core Infrastructure & Foundation Model	Monorepo scaffold, data ingestion pipeline, Earth Foundation Model architecture, API framework, Docker Compose stack, CI/CD, 16 documentation files	✅ Complete
Q2 2026	Model Training & Evaluation	Foundation model pre-training on Sentinel-2 + ERA5, physics-informed model benchmarks, ecosystem GNN on GBIF data, evaluation benchmark suite	🔄 In Progress
Q3 2026	Production Deployment & Partnerships	Kubernetes production deployment, partner API onboarding (3 pilot regions), federated learning cross-region training, real-time dashboard launch	⬚ Planned
Q4 2026	Global Rollout & Citizen Science	Global coverage expansion, citizen science mobile app, multi-language support, open model weights release, community governance	⬚ Planned

🚀 Moonshot Goals

"Shoot for the moon. Even if you miss, you'll land among the stars."

These are our deliberately audacious goals. Some may take years. Some may take decades. All are worth pursuing.

Note

SwachhZero is currently a comprehensive proposal and architectural blueprint — a vision brought to life as code. This system is untested and under active development. We are working towards building this into a fully functional platform.

This project is open to contribution, collaboration, and building together. If you share our mission for Earth, join us. Every line of code, every idea, every act of care matters.

🌱 In the making for a decade. Built with love.

🌡️ Climate & Atmosphere

Real-time global carbon tracking — Monitor every major emission source on Earth in real-time
72-hour extreme weather prediction at 1km resolution for every point on the planet
Methane leak detection from satellite imagery within 24 hours of occurrence
Urban heat island mapping for every city with >100,000 population
Air quality prediction for 1 billion people who currently have no monitoring

🌊 Oceans & Water

Complete ocean health dashboard — temperature, acidification, deoxygenation, plastic pollution
Coral reef monitoring for every reef system on Earth using satellite + acoustic data
Freshwater quality prediction for every major river basin
Flood prediction system giving 48-hour warning to vulnerable communities
Illegal fishing detection using satellite imagery and vessel tracking

🌿 Biodiversity & Ecosystems

Global biodiversity index updated daily from acoustic monitoring + satellite imagery
Species population tracking for 10,000 endangered species
Deforestation alert system detecting illegal logging within 24 hours
Pollinator health monitoring across agricultural regions
Ecosystem connectivity mapping identifying critical wildlife corridors

🏙️ Sustainable Communities

Environmental justice dashboard — mapping pollution exposure vs. income/race for every community
Renewable energy optimization for 1,000 cities
Sustainable urban planning AI helping cities reach net-zero
Agricultural yield prediction helping 500 million smallholder farmers
Clean water access mapping for every village on Earth

🤝 Global Collaboration

Federated learning network across 100+ institutions in 50+ countries
Citizen science platform with 1 million active contributors
Open data commons — the largest freely accessible environmental dataset
Policy simulation toolkit used by 100+ governments for climate decisions
Education platform training the next generation of planetary stewards

🌍 The Ultimate Goal

Contribute measurably to achieving all 17 UN Sustainable Development Goals by 2030
Help keep global warming below 1.5°C through better monitoring, prediction, and policy
Zero preventable environmental deaths — no one should die from polluted air or water
Net positive biodiversity — reverse the decline and restore ecosystem health
Every human on Earth has access to clean air, clean water, and a healthy environment

These goals are intentionally ambitious. We don't know how to achieve all of them yet. But we believe that open-source AI, global collaboration, and collective human determination can move mountains — and heal planets.

If even one of these goals becomes reality because someone was inspired by this list, then SwachhZero has succeeded.

🤝 Contributing

This is a living vision — every contribution, no matter how small, brings us closer to a healthier planet. 🌱

We welcome researchers, engineers, activists, policymakers, students — anyone who cares about Earth. Whether it's a bug fix, a new data source connector, a model improvement, documentation, or even just an idea — every contribution matters and is celebrated.

Quick start:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Make your changes with tests
Ensure all checks pass (ruff check . && mypy src/ && pytest)
Submit a Pull Request

Please read our full Contributing Guide for detailed guidelines on:

Development workflow and branching strategy
Code style and commit message conventions
Testing requirements
Pull request review process

We also adhere to a Code of Conduct — please review it before participating.

💚 Forever Free

SwachhZero is and will always be free. No subscriptions. No paywalls. No monetized data. No strings attached.

This project is funded purely by donations, grants, and the goodwill of those who care about the future of our planet. We don't take — we give. We don't extract — we protect. Every line of code, every model, every dataset connector is a gift to the commons.

We are open to investors and visionaries who share this dream — not those seeking returns on investment, but those seeking returns on impact. People who want to help leave behind a better planet for the generations that follow.

The philosophy is simple: the tools to understand and protect Earth should never be locked behind a paywall. Environmental intelligence is a human right, not a product.

"If you share this dream, support us — not because you must, but because you can." 💚

If you'd like to support SwachhZero through donations, grants, compute resources, or partnerships, please reach out through our GitHub Discussions or community channels.

📚 Citation

If you use SwachhZero in your research, please cite:

@software{swachhzero2026,
  title     = {SwachhZero: AI Platform for Planetary Well-Being},
  author    = {{SwachhZero Contributors}},
  year      = {2026},
  url       = {https://github.com/swachhzero/swachhzero},
  version   = {0.1.0},
  license   = {Apache-2.0},
  note      = {Multi-modal Earth observation, prediction, and policy optimization}
}

💬 Community & Support

Channel	Link
📖 Documentation	docs.swachhzero.earth
💬 GitHub Discussions	Discussions
🐛 Bug Reports	Issues
💡 Feature Requests	Issues
🔒 Security Issues	See SECURITY.md — please report responsibly
📧 Email	swachhzero@googlegroups.com

🙏 Acknowledgments

Research Inspirations

SwachhZero's architecture draws on foundational work in Earth system AI:

ClimaX (Nguyen et al., 2023) — Foundation model approach for weather and climate
Prithvi (Jakubik et al., 2023) — Geospatial foundation model by NASA & IBM
FourCastNet (Pathak et al., 2022) — Fourier neural operator for global weather
GraphCast (Lam et al., 2023) — Graph neural network for medium-range weather by DeepMind
Physics-Informed Neural Networks (Raissi et al., 2019) — PDE-constrained deep learning

Open-Source Foundations

We gratefully build upon these open-source projects:

PyTorch & JAX — ML frameworks
FastAPI — High-performance API framework
Next.js — React framework for the dashboard
Flower — Federated learning framework
xarray & GeoPandas — Scientific data structures
Microsoft Planetary Computer — Earth observation data catalog
MkDocs Material — Documentation framework

Organizations

ESA Copernicus Programme — Sentinel satellite data
NASA Earthdata — Earth science data systems
ECMWF — ERA5 reanalysis data
NOAA — Ocean and atmospheric observations
World Bank Open Data — Socioeconomic indicators

📄 License

SwachhZero is released under the Apache License 2.0 — see the LICENSE file for full details.

You are free to use, modify, and distribute this software for both commercial and non-commercial purposes, with attribution.

From the Whitehole of Love 🤍

In the making for a decade, built with love for Planet Earth 🌍

For the healthier longevity of our planet and all life upon it 🌱

"Look again at that dot. That's here. That's home. That's us." — Carl Sagan, Pale Blue Dot

Why hate, when we can love. Why destroy, when we can build. Why discard, when we can fix. 💫

We are the solution. Let us be the healers and guardians of Planet Earth — mother to all. 🌏

SwachhZero — Because Earth Deserves Intelligence That Cares

Forever Free. Forever Open. For Every Living Being. 🕊️

Apache 2.0 Licensed | Open Source | Open Hearts | Open Collaboration

Plant a tree today, so future generations may sit in its shade 🌳

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
.github		.github
assets		assets
configs		configs
data		data
deploy		deploy
docker		docker
docs		docs
models		models
notebooks		notebooks
scripts		scripts
src		src
tests		tests
.editorconfig		.editorconfig
.env.example		.env.example
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
CHANGELOG.md		CHANGELOG.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
SECURITY.md		SECURITY.md
docker-compose.yml		docker-compose.yml
mkdocs.yml		mkdocs.yml
pyproject.toml		pyproject.toml

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🌏 Vision

🌌 A Pale Blue Dot

🌍 Aligned with UN Sustainable Development Goals

✨ Key Features

🏛️ Architecture Overview

Technology Stack

🚀 Quick Start

Prerequisites

Option 1: Docker Compose (Recommended)

Option 2: pip Install

Option 3: From Source

Verify Installation

📝 Usage Examples

Ingesting Satellite Data

Running a Prediction

Simulating a Climate Scenario

CLI Usage

🔌 API Overview

REST Endpoints

Python Client

curl Example

📁 Project Structure

🧠 Models

📡 Data Sources

🛠️ Development

Environment Setup

Running Tests

Code Style

Pre-commit Hooks

🚢 Deployment

Docker Compose (Local / Staging)

Kubernetes with Helm

Cloud Deployment (Terraform)

🗺️ Roadmap

🚀 Moonshot Goals

🌡️ Climate & Atmosphere

🌊 Oceans & Water

🌿 Biodiversity & Ecosystems

🏙️ Sustainable Communities

🤝 Global Collaboration

🌍 The Ultimate Goal

🤝 Contributing

💚 Forever Free

📚 Citation

💬 Community & Support

🙏 Acknowledgments

Research Inspirations

Open-Source Foundations

Organizations

📄 License

About

Resources

License

Code of conduct

Contributing

Security policy

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