Introduction

Geospatial analysis with TileDB

Multi-dimensional dense or sparse arrays are an excellent fit for geospatial applications. TileDB currently supports the following dataset types:

  • Point cloud: These are 3D points of the form X, Y, Z, <attribute-fields>, which TileDB stores in a 3D sparse array. TileDB integrates with the popular PDAL library to support point cloud data ingestion into TileDB (e.g., from LAZ files) and access/computation.

  • Raster: These are 2D gridded image data, where each pixel may store any number of values. TileDB integrates with the popular GDAL and Rasterio libraries to support ingesting raster and vector data from a variety of formats into TileDB dense arrays, and perform advanced spatial processing.

  • SAR: Synthetic Aperture Radar (SAR) in remote sensing is used to create fine detailed representation of the earth and to model changes over time. The SAR measurement consists of a complex data type representing both the amplitude and phase of the radar response. TileDB stores SAR data as well as temporal stacks of SAR data in 2D or 3D dense arrays.

Storing geospatial data in TileDB allows you to take advantage of all TileDB benefits, such as cloud-optimized access, compression, parallel IO, and integration with the Data Science ecosystem (e.g., for parallel computing via Dask or Spark, or to perform even SQL queries on your data).