Create IBM Granite Geospatial Models MCP Server

## Overview
Create a comprehensive MCP Server for IBM Granite Geospatial Models supporting Earth observation data analysis, satellite imagery processing, environmental monitoring, and geospatial AI applications using NASA-IBM collaboration models.

## Server Specifications

### **Server Details**
- **Name**: `granite-geospatial-server`
- **Language**: Python 3.11+
- **Location**: `mcp-servers/python/granite_geospatial_server/`
- **Purpose**: Provide geospatial AI capabilities via IBM-NASA Granite models

### **Supported Models**
From [IBM Granite Geospatial on Hugging Face](https://huggingface.co/ibm-granite):
- **Foundation Model**: granite-geospatial-fm (NASA-IBM Earth Observations)
- **Satellite Analysis**: granite-satellite-imagery-v1, granite-landsat-analysis
- **Climate Models**: granite-climate-prediction, granite-weather-forecasting
- **Environmental**: granite-forest-monitoring, granite-water-quality-assessment
- **Urban Planning**: granite-urban-change-detection, granite-infrastructure-mapping

### **Data Sources**
- **Satellite Imagery**: Landsat, MODIS, Sentinel, GOES
- **Climate Data**: NOAA, NASA Earth Observing System
- **Environmental**: EPA, USGS, World Bank Open Data
- **Custom**: User-provided geospatial datasets

### **Provider Support**
- **Ollama**: Local inference with geospatial models
- **watsonx.ai**: IBM's enterprise geospatial AI platform
- **Hugging Face**: Direct access to NASA-IBM geospatial models
- **Custom Endpoints**: Integration with geospatial data platforms

### **Tools Provided**

#### 1. `analyze_satellite_imagery`
Comprehensive satellite image analysis and interpretation
```python
@dataclass
class SatelliteImageryRequest:
    image_data: str  # Satellite image or coordinates for data retrieval
    model: str = "granite-satellite-imagery-v1"
    provider: str = "ollama"
    analysis_type: str = "land_cover"  # land_cover, change_detection, vegetation, water, urban
    temporal_analysis: bool = False
    coordinate_system: str = "EPSG:4326"  # WGS84
    resolution: str = "30m"  # spatial resolution
    output_format: str = "geojson"  # geojson, shapefile, raster
```

#### 2. `monitor_environmental_changes`
Track and analyze environmental changes over time
```python
@dataclass
class EnvironmentalMonitoringRequest:
    location: Dict[str, float]  # lat, lon, or bounding box
    model: str = "granite-forest-monitoring"
    provider: str = "watsonx"
    monitoring_type: str = "deforestation"  # deforestation, urbanization, water_quality, air_quality
    time_range: Dict[str, str]  # start_date, end_date
    alert_thresholds: Optional[Dict[str, float]] = None
    change_detection: bool = True
```

#### 3. `predict_climate_patterns`
Climate and weather pattern prediction and analysis
```python
@dataclass
class ClimatePredictionRequest:
    location: Dict[str, float]
    model: str = "granite-climate-prediction"
    provider: str = "huggingface"
    prediction_type: str = "temperature"  # temperature, precipitation, drought, floods
    forecast_horizon: str = "30_days"  # 7_days, 30_days, seasonal, annual
    include_uncertainty: bool = True
    historical_context: bool = True
```

#### 4. `assess_water_quality`
Water body analysis and quality assessment
```python
@dataclass
class WaterQualityRequest:
    water_body_location: Dict[str, float]
    model: str = "granite-water-quality-assessment"
    provider: str = "ollama"
    quality_parameters: List[str] = ["turbidity", "chlorophyll", "temperature", "pollution"]
    satellite_source: str = "sentinel2"  # landsat8, sentinel2, modis
    temporal_aggregation: str = "monthly"  # daily, weekly, monthly
    contamination_detection: bool = True
```

#### 5. `detect_urban_changes`
Urban development and infrastructure change detection
```python
@dataclass
class UrbanChangeRequest:
    urban_area: Dict[str, Any]  # city name or coordinates
    model: str = "granite-urban-change-detection"
    provider: str = "watsonx"
    change_types: List[str] = ["construction", "demolition", "roads", "green_spaces"]
    time_comparison: Dict[str, str]  # before_date, after_date
    infrastructure_mapping: bool = True
    population_impact: bool = False
```

#### 6. `analyze_agricultural_areas`
Agricultural monitoring and crop analysis
```python
@dataclass
class AgriculturalAnalysisRequest:
    farm_location: Dict[str, float]
    model: str = "granite-geospatial-fm"
    provider: str = "huggingface"
    analysis_type: str = "crop_health"  # crop_health, yield_prediction, soil_moisture, disease_detection
    crop_type: Optional[str] = None
    growing_season: Optional[Dict[str, str]] = None
    weather_integration: bool = True
```

#### 7. `map_natural_disasters`
Natural disaster mapping and impact assessment
```python
@dataclass
class DisasterMappingRequest:
    disaster_location: Dict[str, float]
    model: str = "granite-geospatial-fm"
    provider: str = "ollama"
    disaster_type: str = "flood"  # flood, wildfire, earthquake, hurricane, drought
    impact_assessment: bool = True
    damage_estimation: bool = True
    recovery_monitoring: bool = False
    evacuation_planning: bool = False
```

#### 8. `generate_geospatial_insights`
Comprehensive geospatial analysis and reporting
```python
@dataclass
class GeospatialInsightsRequest:
    area_of_interest: Dict[str, Any]
    model: str = "granite-geospatial-fm"
    provider: str = "watsonx"
    insight_categories: List[str] = ["environmental", "economic", "social", "infrastructure"]
    temporal_scope: str = "current"  # current, historical, predictive
    include_recommendations: bool = True
    output_format: str = "report"  # report, dashboard, api_response
```

### **Implementation Requirements**

#### Directory Structure
```
mcp-servers/python/granite_geospatial_server/
├── src/
│   └── granite_geospatial_server/
│       ├── __init__.py
│       ├── server.py
│       ├── providers/
│       │   ├── __init__.py
│       │   ├── ollama_geospatial.py
│       │   ├── watsonx_geospatial.py
│       │   ├── huggingface_geospatial.py
│       │   └── nasa_data_provider.py
│       ├── models/
│       │   ├── __init__.py
│       │   ├── granite_geospatial_models.py
│       │   └── earth_observation_models.py
│       ├── processing/
│       │   ├── __init__.py
│       │   ├── satellite_processor.py
│       │   ├── climate_processor.py
│       │   ├── environmental_processor.py
│       │   └── change_detector.py
│       ├── data/
│       │   ├── __init__.py
│       │   ├── satellite_data_loader.py
│       │   ├── climate_data_loader.py
│       │   └── geospatial_utils.py
│       ├── tools/
│       │   ├── __init__.py
│       │   ├── imagery_tools.py
│       │   ├── climate_tools.py
│       │   ├── environmental_tools.py
│       │   └── analysis_tools.py
│       └── utils/
│           ├── __init__.py
│           ├── coordinate_systems.py
│           ├── data_validation.py
│           └── visualization.py
├── tests/
├── requirements.txt
├── README.md
└── examples/
    ├── satellite_analysis.py
    ├── climate_monitoring.py
    └── environmental_assessment.py
```

#### Dependencies
```python
# requirements.txt
mcp>=1.0.0
transformers>=4.35.0
torch>=2.1.0
rasterio>=1.3.0
geopandas>=0.14.0
xarray>=2023.12.0
dask>=2023.12.0
netcdf4>=1.6.0
h5py>=3.10.0
scikit-image>=0.22.0
folium>=0.15.0
plotly>=5.17.0
requests>=2.31.0
pydantic>=2.5.0
ollama>=0.1.7
ibm-watson-machine-learning>=1.0.325
numpy>=1.24.0
pandas>=2.1.0
matplotlib>=3.7.0
cartopy>=0.22.0
```

### **Configuration**
```yaml
# config.yaml
providers:
  ollama:
    base_url: "http://localhost:11434"
    geospatial_models_enabled: true
    timeout: 600  # Longer timeout for large satellite data
    
  watsonx:
    url: "https://us-south.ml.cloud.ibm.com"
    apikey: "${WATSONX_API_KEY}"
    project_id: "${WATSONX_PROJECT_ID}"
    geospatial_endpoint: "/ml/v1/geospatial"
    
  huggingface:
    api_key: "${HF_API_KEY}"
    cache_dir: "./hf_geospatial_cache"
    device: "auto"

models:
  default_foundation: "granite-geospatial-fm"
  default_satellite: "granite-satellite-imagery-v1"
  default_climate: "granite-climate-prediction"
  default_environmental: "granite-forest-monitoring"

data_sources:
  nasa:
    earthdata_token: "${NASA_EARTHDATA_TOKEN}"
    api_endpoints:
      landsat: "https://earthexplorer.usgs.gov/api"
      modis: "https://modis.gsfc.nasa.gov/data"
      
  noaa:
    api_key: "${NOAA_API_KEY}"
    climate_data_url: "https://www.ncei.noaa.gov/data"
    
  satellite_imagery:
    max_file_size: "1GB"
    supported_formats: ["tiff", "hdf", "netcdf", "geojson"]
    cache_duration: "24h"

processing:
  max_area_km2: 10000  # Maximum analysis area
  spatial_resolution: ["30m", "100m", "250m", "1km"]
  temporal_range_years: 50
  parallel_processing: true
  chunk_size: "512MB"

visualization:
  default_map_style: "satellite"
  color_schemes: ["viridis", "plasma", "terrain", "ocean"]
  export_formats: ["png", "svg", "html", "geojson"]
```

### **Usage Examples**

#### Satellite Imagery Analysis
```python
# Analyze deforestation in Amazon region
result = await mcp_client.call_tool("analyze_satellite_imagery", {
    "image_data": {"lat": -3.4653, "lon": -62.2159, "bbox_km": 50},
    "model": "granite-satellite-imagery-v1",
    "provider": "ollama",
    "analysis_type": "change_detection",
    "temporal_analysis": True,
    "resolution": "30m",
    "output_format": "geojson"
})
```

#### Environmental Monitoring
```python
# Monitor forest changes over time
result = await mcp_client.call_tool("monitor_environmental_changes", {
    "location": {"lat": 45.5231, "lon": -122.6765},  # Portland area
    "model": "granite-forest-monitoring",
    "provider": "watsonx",
    "monitoring_type": "urbanization",
    "time_range": {"start_date": "2020-01-01", "end_date": "2024-01-01"},
    "change_detection": True
})
```

#### Climate Prediction
```python
# Predict drought conditions
result = await mcp_client.call_tool("predict_climate_patterns", {
    "location": {"lat": 37.7749, "lon": -122.4194},  # San Francisco
    "model": "granite-climate-prediction",
    "provider": "huggingface",
    "prediction_type": "drought",
    "forecast_horizon": "seasonal",
    "include_uncertainty": True,
    "historical_context": True
})
```

#### Water Quality Assessment
```python
# Assess lake water quality
result = await mcp_client.call_tool("assess_water_quality", {
    "water_body_location": {"lat": 43.8554, "lon": -79.3373},  # Lake Ontario
    "model": "granite-water-quality-assessment",
    "provider": "ollama",
    "quality_parameters": ["turbidity", "chlorophyll", "pollution"],
    "satellite_source": "sentinel2",
    "contamination_detection": True
})
```

#### Urban Change Detection
```python
# Detect urban development changes
result = await mcp_client.call_tool("detect_urban_changes", {
    "urban_area": {"city": "Dubai", "country": "UAE"},
    "model": "granite-urban-change-detection",
    "provider": "watsonx",
    "change_types": ["construction", "roads", "green_spaces"],
    "time_comparison": {"before_date": "2015-01-01", "after_date": "2024-01-01"},
    "infrastructure_mapping": True
})
```

#### Agricultural Analysis
```python
# Monitor crop health and yield
result = await mcp_client.call_tool("analyze_agricultural_areas", {
    "farm_location": {"lat": 40.7128, "lon": -74.0060, "area_km2": 100},
    "model": "granite-geospatial-fm",
    "provider": "huggingface",
    "analysis_type": "yield_prediction",
    "crop_type": "corn",
    "weather_integration": True
})
```

#### Disaster Mapping
```python
# Map flood impact and damage
result = await mcp_client.call_tool("map_natural_disasters", {
    "disaster_location": {"lat": 29.7604, "lon": -95.3698},  # Houston
    "model": "granite-geospatial-fm",
    "provider": "ollama",
    "disaster_type": "flood",
    "impact_assessment": True,
    "damage_estimation": True,
    "recovery_monitoring": True
})
```

### **Advanced Features**
- **Multi-spectral Analysis**: Process data across multiple electromagnetic spectrum bands
- **Temporal Analysis**: Track changes over time with historical data comparison
- **Change Detection**: Identify and quantify environmental and urban changes
- **Predictive Modeling**: Forecast environmental conditions and climate patterns
- **Integration APIs**: Connect with major satellite data providers and climate databases
- **Real-time Processing**: Handle streaming satellite data for emergency response

### **NASA-IBM Collaboration Features**
- **Earth System Models**: Leverage NASA's Earth observation expertise
- **Satellite Data Integration**: Direct integration with NASA satellite missions
- **Climate Research**: Advanced climate modeling and prediction capabilities
- **Environmental Science**: Cutting-edge environmental monitoring algorithms
- **Open Science**: Support for open data and reproducible research

### **Enterprise Capabilities**
- **Scalable Processing**: Handle petabyte-scale satellite datasets
- **High-Performance Computing**: GPU acceleration for large-scale analysis
- **API Integration**: RESTful APIs for enterprise geospatial applications
- **Custom Models**: Fine-tuning for specific geographic regions or applications
- **Compliance**: Environmental reporting and regulatory compliance support

## Acceptance Criteria
- [ ] Python MCP server with 8+ Granite geospatial model tools
- [ ] Support for NASA-IBM Granite geospatial foundation model
- [ ] Multi-provider integration with geospatial focus
- [ ] Satellite imagery analysis and processing
- [ ] Environmental monitoring and change detection
- [ ] Climate prediction and weather forecasting
- [ ] Water quality and agricultural assessment
- [ ] Urban change detection and disaster mapping
- [ ] Integration with major satellite data sources
- [ ] Geospatial data format support (GeoJSON, Shapefile, NetCDF)
- [ ] Comprehensive test suite with sample geospatial data (>90% coverage)
- [ ] Complete documentation with Earth observation examples

## Priority
High - Demonstrates cutting-edge NASA-IBM geospatial AI collaboration

## Use Cases
- Environmental monitoring and conservation
- Climate change research and adaptation
- Disaster response and emergency management
- Urban planning and smart city development
- Agricultural optimization and food security
- Water resource management
- Natural resource exploration
- Infrastructure monitoring and maintenance
- Regulatory compliance and environmental reporting
- Scientific research and Earth system science

Create IBM Granite Geospatial Models MCP Server #910

Description

Overview

Server Specifications

Server Details

Supported Models

Data Sources

Provider Support

Tools Provided

1. analyze_satellite_imagery

2. monitor_environmental_changes

3. predict_climate_patterns

4. assess_water_quality

5. detect_urban_changes

6. analyze_agricultural_areas

7. map_natural_disasters

8. generate_geospatial_insights

Implementation Requirements

Directory Structure

Dependencies

Configuration

Usage Examples

Satellite Imagery Analysis

Environmental Monitoring

Climate Prediction

Water Quality Assessment

Urban Change Detection

Agricultural Analysis

Disaster Mapping

Advanced Features

NASA-IBM Collaboration Features

Enterprise Capabilities

Acceptance Criteria

Priority

Use Cases

Metadata

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

Issue actions

1. `analyze_satellite_imagery`

2. `monitor_environmental_changes`

3. `predict_climate_patterns`

4. `assess_water_quality`

5. `detect_urban_changes`

6. `analyze_agricultural_areas`

7. `map_natural_disasters`

8. `generate_geospatial_insights`