An AI-powered geospatial application that allows you to explore and visualize vast Earth science data using natural language queries.
Built with Azure AI Foundry, Semantic Kernel agents, Azure AI Agent Service, and containerized microservices, Earth Copilot automatically finds the right planetary data collection, renders it on a map, and analyzes it for you. Whether you're a scientist, analyst, or decision-maker, Earth Copilot helps you spend less time finding data and more time unlocking insights.
Watch Satya Nadella introduce NASA Earth Copilot 1.0 at Microsoft Ignite 2024: View Here
Auto-Deploy Ready: This repository includes fully automated deployment via Bicep and GitHub Actions. Follow the Quick Start Guide to deploy the complete architecture: infrastructure, backend, and frontend within one hour. Its modular architecture is designed for extensibility to any use case.
This is a proof-of-concept, not a production-ready product.
Earth Copilot demonstrates a reusable geospatial AI pattern that can be adapted across different use cases. This open source repository is not supported by Microsoft Copilot and will continue to evolve.
- AI Agent Architecture — Semantic Kernel + Azure AI Agent Service with extendable modules for vision, terrain, mobility, comparison, building damage, and extreme weather analysis
- 130+ Satellite Collections — Microsoft Planetary Computer & NASA VEDA data catalog integration
- Bring Your Own Data — Connect your own private STAC catalogs via Planetary Computer Pro or extend with custom MCP tools
- Modern Container Architecture — Azure Container Apps with VNet integration and Entra ID authentication for enterprise security
- MCP Server Integration — Model Context Protocol server for integration with VS Code GitHub Copilot and other AI assistants
- Copilot Studio & M365 — Integrate with Microsoft Copilot Studio for Teams and M365 deployment via custom connectors
- ArcGIS Integration — Connect with Esri ArcGIS for advanced geospatial workflows, map services, and enterprise GIS capabilities
| Science & Environment | Agriculture & Natural Resources | Energy & Infrastructure | Public Safety & Emergency Management | Defense / National Security |
| Accelerate climate, air quality, land-surface, extreme weather scenarios, and environmental research | Assess drought conditions, soil moisture, and water quality for agriculture planning | Monitor energy grids, transmission corridors, and dam infrastructure, supporting site selection and permitting | Support response to wildfires, floods, hurricanes, and other natural disasters | Monitor geospatial intelligence and support situational awareness for national security operations |
🛰️ Satellite Imagery & Visualization
| Query |
|---|
| Show me high resolution satellite imagery of Dubai urban expansion in 2020 |
| Show me radar imagery of Houston Texas during Hurricane Harvey August 2017 |
| Show me HLS Landsat imagery for Ukraine farmland from 2024 |
| Show me burned area mapping for Montana wildfire regions 2023 |
| Show me NDVI vegetation health for Iowa cropland summer 2024 |
| Show me sea surface temperature anomalies in the Gulf of Mexico |
🌎 Contextual Earth Science Questions
| Query |
|---|
| How was NYC impacted by Hurricane Sandy |
| What was the impact of Hurricane Florence 2018 in North Carolina |
| How did vegetation recover after flooding in Missouri River valley 2023 |
| What are the long-term climate trends affecting Pacific Northwest forests |
| Explain the correlation between El Niño events and wildfire patterns |
🔍 Geointelligence & Raster Analysis
| Module | Query |
|---|---|
| Vision | Analyze this satellite image — what land cover types are visible and what is the surface reflectance? |
| Terrain | Analyze terrain elevation, slope, and line-of-sight at 38.9N, 77.0W |
| Comparison | Show wildfire activity in Southern California in January 2025 and analyze how it evolved over 48 hours |
| Mobility | Classify terrain traversability at these coordinates across 5 elevation layers |
| Building Damage | Assess building damage using before/after satellite imagery at these coordinates |
| Extreme Weather | What are the projected temperature and precipitation trends for Miami through 2050? |
🔐 Private Data Search with RAG
| Query |
|---|
| Analyze our proprietary STAC collection for mineral exploration sites |
| Compare our private agricultural data with public MODIS vegetation indices |
| Search our internal disaster response catalog for similar flood patterns |
| Query our custom satellite constellation for urban heat island analysis |
ALOS World (Berlanga) |
Burn Severity (California) |
Cropland (Florida) |
Elevation (Grand Canyon) |
HLS Greece Elevation |
LIDAR Height (New Orleans) |
MODIS Snow Cover (Quebec) |
Nadir BDRF (Mexico) |
Net Production (San Jose) |
Sea Surface Temp (Madagascar) |
Sentinel (NYC) |
Sentinel RTC (Philippines) |
Surface Water (Bangladesh) |
Vegetation Indices (Ukraine) |
Vision Agent |
Vision Agent |
Terrain Agent |
Terrain Agent |
Terrain Agent |
Mobility Agent |
Mobility Agent |
Extreme Weather Agent |
Extreme Weather Agent |
Thermal Anomalies (Australia) |
| Step | Technology |
|---|---|
| Unified Router — Classifies intent and routes to the right agent | Semantic Kernel |
| Location Resolver — Resolves place names to coordinates | Azure Maps, Google Maps, Mapbox |
| Collection Mapping Agent — Matches query to satellite data collections | Azure AI Foundry (model of choice) |
| STAC Query Builder Agent — Builds spatial-temporal search queries | Azure AI Foundry (model of choice) |
| STAC Search Executor — Searches Planetary Computer & VEDA catalogs | STAC API |
| Tile Selector — Picks the best imagery tiles from results | Function / LLM |
| TiTiler Renderer — Renders satellite tiles for map display | TiTiler |
GEOINT Modules:
| Module | Agent Class | Type | Status |
|---|---|---|---|
| Vision | EnhancedVisionAgent |
Azure AI Agent + 5 Tools | Active |
| Terrain | TerrainAgent |
Azure AI Agent + Tools | Active |
| Mobility | GeointMobilityAgent |
Azure AI Agent + Vision | Active |
| Comparison | ComparisonAgent |
Azure AI Agent (Query Mode) | Active |
| Building Damage | BuildingDamageAgent |
Azure AI Agent + 2 Tools | Active |
| Extreme Weather | ExtremeWeatherAgent |
Azure AI Agent + 7 Tools | Active |
Detailed Architecture Documentation: Agent System Overview
React UI (earth-copilot/web-ui/) - Azure Web Apps
- Main Search Interface: Unified natural language query input
- Chat Sidebar: Conversation history with context awareness
- Azure Maps Integration: Interactive map with satellite overlay and geointelligence results
- Data Catalog Selector: Switch between MPC, NASA VEDA, and custom data sources
- Technology: React 18, TypeScript, Vite, Azure Maps SDK v2
Container App Backend (earth-copilot/container-app/) - Azure Container Apps
- Semantic Kernel Framework: Multi-agent orchestration with Azure AI Foundry (model of choice)
- AI Agents: Query processing and geointelligence analysis pipeline
- STAC Integration: Microsoft Planetary Computer and NASA VEDA API connectivity
- Geointelligence Processing: Terrain analysis, mobility classification, line-of-sight (GDAL/Rasterio)
- Multi-Strategy Geocoding: Google Maps, Azure Maps, Mapbox, OpenAI fallback
- Hybrid Rendering System: TiTiler integration for 113+ satellite collection types
- VNet Integration: Enterprise-grade security with private networking
- Technology: Python 3.12, FastAPI, Semantic Kernel, Azure Container Apps
Azure Infrastructure
- Azure AI Foundry: Model deployments for agent intelligence (GPT-5 or model of choice)
- Azure AI Agent Service: Multi-turn tool orchestration for GEOINT agents (Hub + Project)
- Azure Maps: Geocoding, reverse geocoding, and map tile services
- Azure AI Search: Vector search for private data catalogs (RAG)
- Azure Storage: Blob storage for geointelligence raster processing results
- Virtual Network: Private networking with private endpoints and DNS resolution
MCP Server (earth-copilot/mcp-server/) - Model Context Protocol (Optional)
- GitHub Copilot Integration: Expose Earth Copilot as tool for VS Code
- HTTP Bridge: MCP protocol bridge for external tool access
- Technology: Python, FastAPI, Docker, Azure Container Apps
Copilot Studio - M365 Integration (Optional)
- Teams Bot: Chat with Earth Copilot directly inside Microsoft Teams
- M365 Copilot Plugin: Extend Microsoft 365 Copilot with geospatial capabilities
- Custom Connector: Points to the deployed backend API — no additional infrastructure required
Technical Background:
- Azure Subscription Management - Resource groups, RBAC, cost management, service quotas
- Azure Cloud Services - Azure AI Foundry, Azure Maps, Container Apps, AI Search
- Python Development - Python 3.12, FastAPI, async programming, package management
- React/TypeScript - React 18, TypeScript, Vite, modern JavaScript
- AI/ML Concepts - LLMs, Semantic Kernel, multi-agent systems, RAG
- Geospatial Data - STAC standards, satellite imagery, raster processing (GDAL/Rasterio)
- Docker & Containers - Docker builds, Azure Container Apps, VNet integration
- Infrastructure as Code - Bicep templates, Azure CLI, resource deployment
You can deploy this application using Agent mode in Visual Studio Code or GitHub Codespaces:
For step-by-step deployment instructions, see QUICK_DEPLOY.md
Services Deployed Automatically:
- Azure AI Foundry - Model deployment for AI agents (GPT-5 or model of choice)
- Azure AI Agent Service - Multi-turn tool orchestration for GEOINT agents
- Azure Container Apps - Backend API hosting (VNet-integrated when private endpoints enabled)
- Azure Web Apps - Frontend hosting
- Azure Maps - Geocoding and map visualization
- Azure Container Registry - Docker image storage (with VNet-integrated build agent pool when private endpoints are enabled)
Data Sources (External - No Setup Required):
- Microsoft Planetary Computer STAC API - 113+ global satellite collections
- NASA VEDA STAC API - Earth science datasets from NASA missions
Deploy Earth Copilot to Azure using fully automated GitHub Actions.
Complete Step-by-Step Guide: QUICK_DEPLOY.md ← Start here!
# 1. Fork the repo on GitHub, then clone your fork
git clone https://github.com/YOUR-USERNAME/Earth-Copilot.git
cd Earth-CopilotAfter deploying the core application, you can extend Earth Copilot with these optional integrations:
| Integration | What It Does | Guide |
|---|---|---|
| Planetary Computer Pro | Upload and query your own private satellite data alongside 130+ public collections. Connect your private STAC catalog so Earth Copilot searches both public and private datasets in a single query. | Planetary Computer Pro |
| Copilot Studio | Chat with Earth Copilot in Microsoft Teams (as a bot) or inside M365 Copilot (as a plugin). Create a custom connector pointing to your deployed backend API — no additional infrastructure required. | Microsoft Copilot Studio |
| MCP Server | Expose Earth Copilot as a Model Context Protocol (MCP) server so VS Code GitHub Copilot, Claude Desktop, and other MCP-compatible AI assistants can search satellite imagery and run GEOINT analyses directly from the chat. | Setup Guide |
Earth-Copilot/
├── earth-copilot/ # Main application directory
│ ├── container-app/ # FastAPI backend (Container Apps)
│ │ ├── fastapi_app.py # Main FastAPI application
│ │ ├── semantic_translator.py # STAC query orchestrator
│ │ ├── location_resolver.py # Multi-strategy geocoding
│ │ ├── collection_profiles.py # Collection mappings
│ │ ├── collection_name_mapper.py # Collection name resolution
│ │ ├── tile_selector.py # Tile selection logic
│ │ ├── hybrid_rendering_system.py # TiTiler rendering configs
│ │ ├── titiler_config.py # TiTiler configuration
│ │ ├── veda_collection_profiles.py # NASA VEDA collection profiles
│ │ ├── pc_tasks_config_loader.py # Planetary Computer config loader
│ │ ├── pc_rendering_config.json # Rendering configuration
│ │ ├── quickstart_cache.py # Quick-start query cache
│ │ ├── requirements.txt # Python dependencies
│ │ ├── Dockerfile # Container build
│ │ ├── agents/ # Semantic Kernel agents
│ │ │ └── enhanced_vision_agent.py # Vision Agent (SK)
│ │ └── geoint/ # Azure AI Agent Service modules
│ │ ├── agents.py # Agent factory & initialization
│ │ ├── router_agent.py # Router Agent (Semantic Kernel)
│ │ ├── terrain_agent.py # Terrain Analysis Agent
│ │ ├── terrain_tools.py # Terrain tool definitions
│ │ ├── mobility_agent.py # Mobility Classification Agent
│ │ ├── mobility_tools.py # Mobility tool definitions
│ │ ├── comparison_agent.py # Temporal Comparison Agent
│ │ ├── comparison_tools.py # Comparison tool definitions
│ │ ├── building_damage_agent.py # Building Damage Agent
│ │ ├── building_damage_tools.py # Building Damage tool definitions
│ │ ├── extreme_weather_agent.py # Extreme Weather Agent
│ │ ├── extreme_weather_tools.py # Extreme Weather tool definitions
│ │ ├── vision_analyzer.py # Vision analysis utilities
│ │ ├── chat_vision_analyzer.py # Chat-based vision analysis
│ │ ├── raster_data_fetcher.py # Raster data extraction
│ │ └── tools.py # Shared GEOINT tools
│ │
│ ├── web-ui/ # React frontend (Static Web App)
│ │ ├── src/
│ │ │ ├── components/ # React components
│ │ │ │ ├── Chat.tsx # Chat interface
│ │ │ │ ├── MapView.tsx # Azure Maps + satellite overlays
│ │ │ │ ├── DatasetDropdown.tsx # Data source selection
│ │ │ │ ├── GeointOverlay.tsx # GEOINT module overlay
│ │ │ │ ├── LandingPage.tsx # Landing page
│ │ │ │ ├── PCSearchPanel.tsx # Planetary Computer search
│ │ │ │ └── ...
│ │ │ ├── services/ # API integration
│ │ │ │ ├── api.ts # Backend API client
│ │ │ │ └── vedaSearchService.ts # NASA VEDA integration
│ │ │ ├── ui/ # UI layout components
│ │ │ └── utils/ # Rendering & tile utilities
│ │ ├── public/ # Static assets & config
│ │ ├── package.json # Node.js dependencies
│ │ ├── vite.config.ts # Vite build config
│ │ ├── vitest.config.ts # Test config
│ │ └── staticwebapp.config.json # Azure SWA config
│ │
│ ├── mcp-server/ # MCP server (Optional)
│ │ ├── server.py # MCP server with tool definitions
│ │ ├── mcp_bridge.py # MCP HTTP bridge for external access
│ │ ├── requirements.txt # MCP dependencies
│ │ ├── Dockerfile # MCP container build
│ │ ├── deploy-mcp-server.ps1 # Deployment script
│ │ ├── test_deployed_mcp.py # Production tests
│ │ ├── test_mcp_server.py # Unit tests
│ │ ├── CLIENT_CONNECTION_GUIDE.md # Client connection guide
│ │ ├── QUICK_START.md # Quick start guide
│ │ └── apim/ # API Management
│ │ ├── apim-template.json # APIM template
│ │ └── deploy-apim.ps1 # APIM deployment
│ │
│ ├── copilot-studio/ # Copilot Studio integration (Optional)
│ │
│ ├── ai-search/ # Azure AI Search setup
│ │ ├── README.md
│ │ ├── setup.sh
│ │ └── scripts/ # Index creation scripts
│ │ ├── create_search_index_with_vectors.py
│ │ └── requirements.txt
│ │
│ ├── infra/ # Infrastructure as Code
│ │ ├── main.bicep # Main Bicep template
│ │ ├── main.parameters.json # Parameters
│ │ ├── README.md
│ │ ├── app/ # App-specific resources
│ │ │ └── web.bicep
│ │ └── shared/ # Shared infrastructure
│ │ ├── ai-foundry.bicep # AI Foundry Hub + Project
│ │ ├── ai-search.bicep # AI Search service
│ │ ├── apps-env.bicep # Container Apps Environment
│ │ ├── keyvault.bicep # Key Vault
│ │ ├── maps.bicep # Azure Maps
│ │ ├── monitoring.bicep # Log Analytics
│ │ ├── openai-role-assignment.bicep # OpenAI role assignments
│ │ ├── registry.bicep # Container Registry
│ │ └── storage.bicep # Storage Account
│ │
│ ├── scripts/ # App-level scripts
│ │ └── health-check.sh
│ │
│ ├── azure.yaml # Azure Developer CLI config
│ └── deploy-all.ps1 # Deploy all services
│
├── documentation/ # Project documentation
│ ├── architecture/
│ │ ├── agent_system_overview.md # Agent architecture
│ │ ├── geoint_agent_tools.md # GEOINT tools reference
│ │ └── semantic_translator_logic.md # Translator logic
│ ├── data_collections/
│ │ ├── stac_collections.md # 113+ collections reference
│ │ └── tiles.md # Tile rendering guide
│ └── images/ # Screenshots and diagrams
│
├── scripts/ # Utility & setup scripts
│ ├── bootstrap-github-environment.ps1 # GitHub environment setup
│ ├── bootstrap-github-environment.sh
│ ├── enable-agent-service.ps1 # Enable Azure AI Agent Service
│ ├── enable-backend-auth.ps1 # Enable backend auth
│ ├── enable-webapp-auth.ps1 # Enable web app auth
│ ├── restrict-access.ps1 # Restrict resource access
│ ├── verify-requirements.py # Verify dependencies
│ ├── stac_availability/ # STAC data exploration
│ │ └── generate_dataset_table.py
│ └── veda_availability/ # VEDA data exploration
│ └── comprehensive_veda_analyzer.py
│
├── .github/ # GitHub configuration
│ ├── copilot/
│ │ └── mcp-servers.json # MCP server config for Copilot
│ ├── environment-config-template.yml # Environment config template
│ └── workflows/
│ └── deploy.yml # CI/CD deployment workflow
│
├── deploy-infrastructure.ps1 # Deploy all Azure resources
├── requirements.txt # Root Python dependencies (dev)
├── README.md # This file
├── QUICK_DEPLOY.md # Automated deployment guide
├── LICENSE.txt # MIT License
├── SECURITY.md # Security policy
├── SUPPORT.md # Support information
├── CONTRIBUTING.md # Contribution guidelines
└── CODE_OF_CONDUCT.md # Code of conduct
MIT License - see LICENSE.txt for details.
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
Earth Copilot was developed by Melisa Bardhi and advised by Juan Carlos Lopez.
A big thank you to our collaborators:
- Microsoft Planetary Computer
- NASA
- Microsoft Team: Juan Carlos Lopez, Jocelynn Hartwig, Minh Nguyen & Matt Morrell.
Built for the Earth science community with ❤️ and AI