#geospatial-data

#geospatial-data

Africa's official maps are stuck in the past, often either outdated, incomplete-or both. But governments don't have the budgets to fix them, making it difficult to complete projects as complex as deciding where to put new solar plants to as simple as delivering a package. Now a new plan is underway to map the entire continent using satellite data and AI.

Google parent Alphabet ( NASDAQ:GOOG )( NASDAQ:GOOGL ) is often seen as a core tech giant focused on search, advertising, and cloud computing. Yet beneath its main operations lies a quieter side: a venture arm that invests in promising companies in areas like space tech, geospatial data, and semiconductors. These bets target innovations adjacent to Alphabet's ecosystem, such as enhanced connectivity for Android devices, Earth observation for AI-driven mapping in Google Earth, and efficient chip designs for data centers powering Google Cloud.

South Korea is nearing a decision on whether to allow Google and Apple to export high-resolution geographic map data to servers outside the country. The detailed maps, which use a 1:5,000 scale, would show streets, buildings, and alleyways in far greater detail than currently available on these platforms. However, several regulatory and security hurdles remain unresolved. Earlier this week, South Korea's National Assembly Defense Committee held a parliamentary audit of Google Korea.

A location-smart AI knows where you are and answers with that in mind. Generic AI models treat a user in Tokyo exactly like someone in Toledo, missing the cultural nuances that make recommendations actually useful. Location-based AI changes this by training on geographically specific data, with search results serving as windows into regional preferences, language patterns, and local interests. This goes beyond knowing your zip code and into understanding the invisible cultural context that shapes how people actually live and search.

Jethwa explained that the foundation models for AI serve as a base for building specialized applications, helping users find and query geospatial data.

The two primary categories for spatial data modeling in geospatial domains are raster data models, which use a grid-based approach, and vector data models that represent entities using geometrical shapes.