**6. Conclusions**

*Advanced Analytics and Artificial Intelligence Applications*

are acquired, they will be analyzed further.

extraction procedures.

Among them are the generation of radiometrically and geometrically calibrated data cubes. **Browsing** the data sets is a first step of visual inspection where the user is getting acquainted with the observed structures and their signatures. Further, data mining is an automated process to discover the main data particularities and categories but also detect artifacts or outliers in the data sets, which are beyond the capabilities of human observation, due to the large data volumes and the nonvisual nature of the satellite images. The discovered and selected data sets are further **analyzed** in detail by extracting the particular characteristics of the observed scenes or objects. The results of the analysis are contributing to update existing models or build new models for the observations. **Visualization** of the model parameters or extracted information is a verification step to cope with large complex data volumes. Specific **evaluation** paradigms are needed to build trust in the obtained results, to be used to make **predictions**. The process is iterative, and when new data

• *The third perspective is the implementation architecture logic* (**Figure 21**).

The implementation of these paradigms requires a concept of integrating artificial intelligence with software (SW) system architectures enabling interactive multiuser operations in real time relative to the user reaction times. End users will be able to work on shared user scenarios, results of their analyses, or information

The central component is a **data index (DI)** which is a very specific database model for very fast, real-time management, processing, and distribution of large

**92**

**Figure 21.**

*The logic* implementation architecture *scheme.*

The advantages and benefits of the proposed approach are:

