"Treating the Earth like a living organism. Monitoring, diagnosing, and healing it with AI."
A Neural-Symbolic Earth Intelligence Platform bridging satellite telemetry, multilingual LLMs, and climate science for actionable planetary health diagnostics.
📄 Research Paper (Coming Soon) · 🗺️ Architecture Docs · 🤝 Collaborate · 📊 Roadmap
GaiaAir is an open-science research initiative at the intersection of remote sensing, multimodal AI, and climate informatics. It operationalizes the concept of the Earth as a living patient — one that can be continuously monitored via satellite telemetry, diagnosed through large language model reasoning, and informed by a retrieval-augmented knowledge base of global climate science literature.
The core thesis: most climate intelligence is trapped in data silos inaccessible to the communities most affected by climate change. GaiaAir dismantles this barrier by unifying vision-based anomaly detection with multilingual, context-aware advisory generation — making planetary health legible to farmers, policymakers, and researchers alike.
GaiaAir operates on a three-layer Neural-Symbolic pipeline, each layer designed to mirror a human physician's diagnostic workflow:
┌─────────────────────────────────────────────────────────────── ─────┐
│ GAIAAIR INTELLIGENCE STACK │
├──────────────────────────────────────────────────────────────── ────┤
│ │
│ [SENSORY LAYER] [REASONING LAYER] [ADVISORY LAYER] │
│ │
│ Sentinel-2 (10m res) ──► Cohere Command R+ ──► SMS / WhatsApp │
│ Landsat-8/9 (30m res) (Multilingual LLM) Policy Reports │
│ Ground IoT Sensors ──► LangChain Agents ──► RAG Responses │
│ GEE Time-Series Vector Store Dashboards │
│ │
│ GeoPandas + GDAL ──► Cohere Rerank ──► Streamlit UI │
│ NDVI / EVI / NDWI UN Climate Corpus GeoJSON Maps │
│ │
└─────────────────────────────────────────────────────────────────────┘
Ingests raw geospatial data from:
- Sentinel-2 (ESA): 13 spectral bands at 10–60m resolution; used for NDVI, EVI, NDWI indices
- Landsat 8/9 (USGS/NASA): Thermal infrared bands for land surface temperature anomaly detection
- Ground IoT Sensors: Soil moisture, humidity, and particulate matter feeds for in-situ validation
- Google Earth Engine API: Cloud-based planetary-scale geospatial processing
- Cohere Command R+: Primary reasoning engine for structured environmental data interpretation and advisory generation
- LangChain Agentic Framework: Orchestrates multi-step diagnostic pipelines (detect → retrieve → reason → advise)
- Cohere Embed v3.0 + Rerank: Powers the semantic retrieval of climate literature, contextualizing anomalies against known patterns
- Multilingual report generation (supports 100+ languages via Cohere's multilingual stack)
- SMS/WhatsApp integration for last-mile delivery to low-connectivity rural communities
- Policy briefs synthesized from UN IPCC reports and FAO datasets
Micro-climate diagnostics for smallholder farmers, delivered in their native language.
| Component | Description |
|---|---|
| Crop Stress Detection | NDVI anomaly detection against seasonal baselines to flag early stress signals |
| Drought Forecasting | NDWI + soil moisture trend analysis with 14-day predictive window |
| Multilingual SMS Advisory | Cohere-powered crop health reports delivered via WhatsApp Business API |
| Pest/Blight Correlation | Cross-references crop stress patterns with historical pest outbreak records |
An always-on monitoring system that detects and classifies environmental events before they escalate.
| Component | Description |
|---|---|
| Wildfire Detection | SWIR band anomaly detection with confidence scoring |
| Flood Mapping | SAR coherence change detection integrated with Sentinel-1 |
| Air Quality Inference | Aerosol optical depth estimation via multi-spectral proxies |
| Urban Heat Islands | Landsat thermal band time-series to track heat exposure zones |
A retrieval-augmented knowledge base that makes the world's climate science queryable in plain language.
| Component | Description |
|---|---|
| UN IPCC Corpus | Full vectorization of AR6 Working Group reports |
| FAO Agricultural Datasets | Indexed for context-aware farm advisory generation |
| NDC Tracker Integration | National climate pledges cross-referenced with satellite-observed outcomes |
| Semantic Policy Search | Natural language queries returning sourced, ranked policy evidence |
This project contributes to several open research problems:
-
Grounded Multimodal Climate Reasoning: Connecting raw spectral indices to natural language explanations via LLMs — addressing the interpretability gap in Earth observation AI.
-
Low-Bandwidth Climate Intelligence: Designing RAG pipelines optimized for SMS delivery contexts where bandwidth, literacy, and device constraints are severe.
-
Neural-Symbolic Earth Diagnostics: Framing environmental monitoring as a medical diagnostic problem — enabling structured anomaly classification with uncertainty quantification.
-
Cross-Lingual Climate Communication: Evaluating multilingual LLM performance on domain-specific climate advisory tasks across low-resource languages (Swahili, Bengali, Hindi, etc.).
| Category | Technology | Purpose |
|---|---|---|
| LLM / Reasoning | Cohere Command R+ | Environmental advisory generation |
| Embeddings | Cohere Embed v3.0 | Semantic vectorization of climate literature |
| Retrieval | Cohere Rerank | Contextual ranking of retrieved climate evidence |
| Orchestration | LangChain | Agentic pipeline management |
| Geospatial Processing | Google Earth Engine API | Planetary-scale raster computation |
| Data Manipulation | GeoPandas, GDAL, NumPy | Geospatial data processing |
| Spectral Analysis | Rasterio, Shapely | Band arithmetic and vector operations |
| Dashboard | Streamlit | Interactive research and monitoring UI |
| Delivery | Twilio / WhatsApp Business API | Last-mile advisory distribution |
| Storage | PostGIS / GeoJSON | Spatial data persistence |
| Dataset | Provider | Resolution | Use Case |
|---|---|---|---|
| Sentinel-2 MSI | ESA Copernicus | 10m / 20m / 60m | NDVI, Crop Stress, Flood |
| Landsat 8 OLI/TIRS | USGS / NASA | 30m / 100m | LST, Urban Heat, Fire |
| Sentinel-1 SAR | ESA Copernicus | 10m | Flood Mapping |
| ERA5 Reanalysis | ECMWF | ~30km | Climate baseline reference |
| MODIS Active Fire | NASA FIRMS | 375m–1km | Wildfire validation |
| CHIRPS Precipitation | UCSB | 5km | Drought index computation |
| UN IPCC AR6 Reports | IPCC | — | RAG knowledge base |
| FAO GAEZ | FAO | 1km | Agricultural zoning context |
Phase 1 — Foundation (Q1–Q2 2025) ██████████░░░░░░ 60%
Architecture Design
GEE Integration Prototype
Cohere RAG Pipeline (In Progress)
FarmVital MVP
Phase 2 — Validation (Q3–Q4 2025) ░░░░░░░░░░░░░░░░ 0%
Ground-truth field validation (India pilot)
Multilingual evaluation benchmarks
GaiaSense v1 deployment
Phase 3 — Scale (2026) ░░░░░░░░░░░░░░░░ 0%
Policy Engine public API
Multi-country rollout
Peer-reviewed publication submission
GaiaAir is currently in active research phase. We specifically welcome collaborations from:
- Remote Sensing Researchers — ground-truth dataset contributions, spectral index validation
- NLP / LLM Researchers — multilingual evaluation, hallucination mitigation in climate advisory
- Climate Scientists — domain review of diagnostic models and advisory outputs
- Field Organizations — NGOs and agricultural agencies for pilot deployments
To collaborate, please open an issue describing your background and area of interest. For formal research partnerships or grant co-applications, reach out via the Discussions tab.
# Clone the repository
git clone https://github.com/AviJxn/GaiaAir-Platform.git
cd GaiaAir-Platform
# Create a virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Configure environment variables
cp .env.example .env
# Add your Cohere API key, GEE credentials, etc.
# Run the Streamlit dashboard
streamlit run app.py- Gorelick, N., et al. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment.
- Izaurralde, R.C., et al. (2003). Simulating soil C dynamics with EPIC. Ecological Modelling.
- Brown, T., et al. (2020). Language Models are Few-Shot Learners. NeurIPS.
- IPCC (2023). AR6 Synthesis Report: Climate Change 2023.
- FAO (2021). The State of Food and Agriculture 2021.
- Asner, G.P. (1998). Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment.
If you use GaiaAir in your research, please cite:
@software{gaiaair2025,
author = {Ranjan,Ravi and Contributors},
title = {GaiaAir: An AI Platform for Planetary Health Diagnostics},
year = {2025},
publisher = {GitHub},
url = {https://github.com/AviJxn/GaiaAir-Platform},
note = {Open-science initiative for satellite-informed climate intelligence}
}This project is licensed under the MIT License — see LICENSE for details.
All satellite data used complies with ESA Copernicus Data Policy and USGS Landsat Open Data Policy.
Built with ❤️ for the Planet.
GaiaAir is an independent open-science project. Contributions and citations are welcome.
