Simplify your AI infrastructure and reduce your interoperability headaches with TileDB, the adaptive database for geospatial AI.

The need for geospatial insights with AI requires the management and ETL of legacy geospatial data lakes. Learn how TileDB can model all data as n-dimensional arrays as it sees no data as unstructured. TileDB is multi-modal and supports rasters, point clouds and geometries with a single API.

We will explain that TileDB is ideal for Generative AI with geospatial data through vector search. The embeddings of a vector search application can be modeled as 1-D arrays, making TileDB the source for both your data and your vector database. In the presentation we will demonstrate how your rasters can be stored as TileDB arrays, how your embeddings can be stored as TileDB arrays and how you can perform similarity search across your data.

Finally, we will discuss that data management is the fundamental basis for any geospatial data infrastructure and how we have been making this problem in the geospatial industry too difficult. GeoParquet, COGs and others are great at a project level but do not scale across multiple modalities and introduce governance hurdles. Ease of use and scalability is what a modern geospatial data infrastructure requires.