A Python-based MCP server that collects, analyzes, and visualizes forest fire occurrence data on maps, allowing users to access regional fire information, risk analysis, and map visualizations.
Enables AI agents to query Earth observation data, satellite imagery, active fires, and weather via natural language, returning interactive maps and briefs.
Production-ready satellite imagery analysis server that enables natural language queries for Earth observation data, including land cover classification, vegetation monitoring, water detection, change detection, and automated environmental reporting.
Provides sovereign geospatial awareness by wrapping open, non-US-dependent geospatial APIs for AI-agent situational awareness, environmental compliance, and disaster response.
Enables satellite imagery analysis through Google Earth Engine, allowing users to search datasets, calculate vegetation indices like NDVI, filter collections by location and date, and export imagery to cloud storage. Supports major satellite datasets including Sentinel-2, Landsat, and MODIS for applications like agriculture monitoring and deforestation tracking.