Giorgos Mountrakis

Welcome to the webpage of my classes. Contact me to help you decide whether a class fits your needs. Keep in mind that typically my classes have a large project at the end to accommodate a variety of student backgrounds.

If you have taken any of my classes feel free to comment at the bottom of the page.

Current Semester (Fall 2010)

• Spatial Analysis (3cr) ERE 596, Section 08 Syllabus
  Description: Topics covered in this course include elements of spatial statistics and modeling as applied to various data formats: single point data, continuous data and area data. The triangle visualize-explore-model will be employed with emphasis in the modeling section. Examples of taught methods include: first/second order effects, complete spatial randomness, tessellation, kernel, covariograms and variograms, kriging, distance measures, correlation/correlogram and spatial regression models. General programming experience and quantitative background are required. Assignments will use Matlab software package, though no prior knowledge of Matlab is required.

Past Semesters

• Principles of Remote Sensing (4cr) FEG 365/ ERE 565 Syllabus
  Description: The class provides a qualitative and quantitative introduction to the fundamentals of acquiring, analyzing and utilizing remote sensing data in the performance of natural resource inventories, environmental quality surveys, site development studies and land use analyses. The class describes the fundamentals of remote sensing and also covers introductory concepts and methods in digital image processing and photogrammetry.

• Digital Image Analysis (3cr) ERE 596, Section 11 Syllabus
  Description: Topics covered in this class include elements of digital image processing and analysis systems: Digital image representation, visual perception, sampling and quantization, pixel connectivity, Fourier transforms, image enhancement, filtering, image segmentation, edge detection, thresholding, representation schemes, descriptors, morphology, recognition and interpretation. General programming experience and quantitative background are required. Assignments will use Matlab software package, though no prior knowledge of Matlab is required.

• Artificial Intelligence in Geography (1cr) ERE 796
  Description: This course focuses on theory and applications of artificial intelligence (AI) methods to geographic problems. Methods dicussed include decision trees, fuzzy logic, neural networks (backpropagation, radial basis function, self-organizing maps), genetic algorithms and agent-based modeling.

Feel free to comment on classes below: