APPLICATION OF GIS WITH PYTHON

Course Objectives:
The objective of this course is to gain familiarity with basic tools and methods of open source remote sensing and geographic information systems. The course intention is to make students try their hands on solving remote sensing and geographic information system problems without using proprietary software.

Course content:

1. Introduction to python programming (1 hour)
1. Variables, literals, keywords, expression and statements
2. Operators, types and precedence


2. Python function (3 hours)
1. Why to use functions and their types
2. Built in function
3. conversion function
4. math function
5. Function definitions
6. Recursive function
7. Some python modules


3. Control statements and repetition (3 hours)
1. If, If...else, nested if, chaining with elif statements
2. While and for loops
3. Nested loop


4. Strings (3 hours)
1. Concatenation, comparison
2. Length of a string, string subscripts, positive and negative indices
3. 'in' operator
4. Strings are immutable
5. Strings methods


5. Lists (3 hours)
1. positive and negative indices
2. lists are mutable
3. empty list
4. 'in' operator
5. Nested list
6. Methods on list
7. list as matrices


6. Dictionaries (3 hours)
1. Key: value pair
2. dictionary methods
3. dictionaries and matrices
4. updating dictionaries


7. Files (3 hours)
1. Absolute and relative paths
2. Current working directory
3. Opening and closing to a file
4. Reading from and writing to a file
5. Copying a file
6. Types of files: text and binary


8. Classes and objects (3 hours)
1. Concept of class and object
2. Instantiation
3. Copy: shallow copy and deep copy
4. Constructors


9. Working with images (8 hours)
1. Bands and modes
2. Handling images: loading images, getting image information, histogram, conversation, splitting and merging bands, masking, cropping and resizing
3. Edge detection and segmentation
1. Edge detection on black and white images
2. Edge detection on color and hyper spectral images
3. Hyeperspectral image segmentation
4. Image processing
1. Getting and setting pixels
2. Filters: blurring, edge enhancement, smoothing, sharpening
3. Mathematical morphology: erosion, dilation, opening and closing
5. Transformations
1. Affine and perspective transformation
2. Interpolation: nearest, bilinear and cubic


10. Spatial data processing (15 hours)
1. Types of spatial data and vector data types
2. Reading and writing vector data
3. Extract a subset of learners
4. Creating geometrics and handling projections
5. Calculating attributes of vector data
6. Testing Topological conditions
7. Spatial analysis methods: difference, symmetric difference, union, intersection, buffer, simplify, merge
8. Georeferencing a new image
9. Mosaic images, and map algebra

Practical classes:

1. Introduction to Python and Python function
2. Branching and looping in Python
3. String handling and lists in Python
4. Dictionaries and files using in Python
5. Classes and objects in Python
6. Image processing
1. Handling images, edges detection and segmentation
2. Image processing, transformation batch processing
7. Spatial data processing
1. Reading, writing and sub setting vector data
2. Creating geometries, handling projections and calculating attributes of vector data
3. Testing Topological conditions and familiarizing with spatial analysis methods
4. Georeferencing and Mosaic images
5. Map algebra

References:

1. John M. Zelle, "Python Programming: An Introduction to Computer Science", Wartburg College Printing Services
2. David M. Beazely, "Python Essential Reference", Addison Wesley Professional
3. https://www.python.org/
4. https://docs.python.org/2/library
5. https://www.gdal.org/

Evaluation Scheme:
The questions should cover all the chapters in the syllabus. The evaluation scheme will be as indicated in the table below:

*There may be minor variation in marks distribution