Google Earth 2026: Data Layers from Earth Engine and Natural Language Search via Gemini
As of March 2026, Google Earth is absorbing Google Earth Engine's raster data catalog through a no-code interface. New layers include global elevation contours, slope/aspect analysis, forest cover change, flood inundation history, and cycling infrastructure data. An 'Ask Google Earth' feature powered by Gemini enables natural language geospatial queries. The target audience has shifted from consumers to engineers, urban planners, and sustainability consultants.
In March 2026, Google Earth began integrating Google Earth Engine's petabyte-scale raster data catalog through a no-code visual interface, bridging two products that had been separate for approximately 15 years. ## What Changed Google Earth was historically a consumer globe viewer. Google Earth Engine was a research-grade geospatial analysis platform requiring Python or JavaScript coding. The 2026 update brings Earth Engine's data layers into the visual Google Earth interface without requiring any code. ## New Data Layers **Elevation and terrain:** Global contour lines, slope analysis, and aspect (which direction terrain faces). Previously required downloading DEM data and processing in GIS software. **Forest cover:** Hansen Global Forest Change dataset showing forest loss and gain over time. Enables year-by-year visualization of deforestation patterns. **Flood inundation history:** Historical flood extent data showing which areas have been underwater and how frequently. **Cycling infrastructure:** Dedicated cycling data layer (specific data source not detailed in announcement). ## Ask Google Earth A natural language search feature powered by Gemini that allows queries like "show me areas in the Netherlands below sea level" or "find deforested regions in Borneo since 2010." The system translates natural language into geospatial queries against the data layers. ## Strategic Context The integration positions Google Earth as a professional geospatial tool for engineers, urban planners, and sustainability consultants — users who previously needed GIS expertise and Earth Engine coding skills to access this data. The no-code interface dramatically lowers the barrier while the underlying data quality remains research-grade.