Geospatial Intelligence

Recommended Prerequisites

NA

Teaching Methods

Theoretical classes with detailed presentation, using audiovisual means, of concepts, principles and

fundamental theories and solving of basic practical exercises to illustrate the practical interest of the

subject and exemplify its application to real cases. Theoretical-practical classes where the students

solve practical exercises, which require the combination of different theoretical concepts and promote critical reasoning.The evaluation, which covers all the taught matters, clearly is focused on

both the basic theoretical concepts and the ability to solve complex problems.

Learning Outcomes

Geospatial Intelligence studies the conception, development and implementation of geospatial

systems. In particular, this curricular unit has, among other, the following objectives:

- To extract, transform and load the data, optimization and administration of data warehouses and multidimensional databases.

-To perform data analysis (pre-processing and feature selection; algorithms; and visualization).

- To explore various applications, formalizing them with analytical models in various domains.

- To compare parametric and nonparametric models.

-To specify evaluation models - To deepen advanced topics of data reduction dimensionality.

- To study data validation methods.

- To design an intelligent pattern recognition system in the geospatial domain.

Work Placement(s)

No

Syllabus

1. Pattern Discrimination

2. Data pre-processing

3. Feature Selection and Feature Extraction

4. Dimensionality Reduction

5. Unsupervised Methods: Clustering

6. Model Selection: Parametric and Non-Parametric Models.

7. Kernel methods

8. Model Assessment; Sampling: Bootstrapping, Boosting

Head Lecturer(s)

Bernardete Martins Ribeiro

Assessment Methods

Assessment
Project: 40.0%
Exam: 60.0%

Bibliography

• Bishop, C.M., “Pattern Recognition and Machine Learning”, Springer Verlag, 2006

• Duda, R. O., Hart, P.E., and Stork, D.G., “Pattern Classification” 2nd ed. Wiley Interscience

(2001)

• J.P. Marques de Sá, “Pattern Recognition: Concepts, Methods and Applications”, 2001, XIX,

318 p., 197 illus., Springer-Verlag (2001)

• M. N. Murty and V. S. Devi, “Pattern Recognition: An Algorithmic Approach”, Springer, 1st

Edition., XII, 263 p. (2011)