GI3013 GEO Computing Syllabus:

GI3013 GEO Computing Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

 To learn about the computational aspects and its implementation with raster and vector data formats using python scripting.

UNIT I INTRODUCTION AND PYTHON FUNDAMENTALS

Understanding geospatial data formats and file organization – Programming basics & Python core concepts – Functions – Flow control.

UNIT II PYTHON ADVANCED CONCEPTS

Introduction to numpy, Containers – Copies, Reading and Writing files& Python system access – Classes and objects – Plotting with Python.

UNIT III PYTHON FOR GIS

Raster Processing with GDAL – Vector Processing with OGR – Geoprocessing with ArcPy – Interactive Mapping and Geoprocessing on Jupyter Notebook.

UNIT IV ADVANCED GIS ALGORITHMS

Vector Data Algorithms (Spatial data clustering) – Raster Data Algorithms ( classification, change detection) – Network Data Algorithms (shortest path, centrality) – Geospatial Big Data Visualization Methods and Tools – Spatiotemporal Data Analytics

UNIT V OPEN-SOURCE GEOSPATIAL BIG DATA ANALYSIS AND APPLICATIONS

Machine learning and deep Learning for remote sensing imagery analytics – Tensor flow – LiDAR Point Cloud analytics – GPS Trajectory Data analytics – Textual Documents analytics.

TOTAL: 45 PERIODS
COURSE OUTCOMES:

On completion of the course, the student is expected to
CO1 Be familiar with major geospatial vector and raster file formats and specifications for spatial reference coordinate systems.
CO2 Have used and be comfortable with online resources that support geocomputing and programming in the GIS profession.
CO3 Learn newly developed GIS computation tools/libraries and platforms.
CO4 Understand the concepts of raster, vector and data analytics
CO5 Do ML/DL data analytics for imagery, LiDAR and GPS

TEXT BOOKS:

1. Think Python: How to Think Like a Computer Scientist by Allen Downey et al., 2014, O’Reilly.
2. Introduction to GIS Programming and Fundamentals with Python and ArcGIS, Chaowei Yang et al., 2017, CRC Press

REFERENCES:

1. Yang, Chaowei, and Qunying Huang. Spatial cloud computing: a practical approach. CRC Press, 2013.
2. Géron, Aurélien. Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media, Inc., 2017.
3. Richert, Willi. Building machine learning systems with Python. Packt Publishing Ltd, 2013.