Elements

Description

Data type

Geospatial data deals with the physical location, proximity of people and assets in both space and time, making it dynamic as well.

Data volume

The volume of geospatial data is usually very high as it is one of the data intensive application areas where data are extremely large, complex, rapidly growing and often include real-time elements. which captured by sensors, devices and satellites, often dating back decades

Higher dimensional data

Whereas the classical geometry-based analyses use low-dimensional spaces, the Geospatial field use of high-dimensional data is increasing. When combined with real world data, both structured and unstructured geospatial data becomes more high-dimensional which adds to the complexity of AI techniques necessary to solve a given problem.

Preprocessing for AI

Geospatial data requires greater preprocessing than standard AI models. As most standard AI tools are not geared to understand the concepts of geospatial-connectivity, spatial proximity, terrain, etc.

Real-time applications

Many GeoAI applications such autonomous vehicle, predictive routing, and asset tracking require real-time processing and immediate results for decision making using the complex and structurally different data than text or images analyzed by standard AI models.

Hyperspectral

Unlike simple images, geospatial data can cover multiple spectral frequencies beyond the visible-light frequencies. When the ultraviolet and infra-red bands are included in the data collected, it is called multi-spectral, and when all available frequency bands are included, it is called hyperspectral. Analyzing hyperspectral data requires different techniques and domain expertise as even mineral constituents of objects detected can be identified.