Comprehensive Course Listing Across 8 Semesters
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | GIS101 | Introduction to GIS | 3-0-0-3 | - |
| 1 | MAT101 | Calculus and Analytical Geometry | 4-0-0-4 | - |
| 1 | PHY101 | Physics for Engineering | 3-0-0-3 | - |
| 1 | CS101 | Programming Fundamentals | 3-0-0-3 | - |
| 2 | GIS201 | GIS Applications in Environmental Science | 3-0-0-3 | GIS101 |
| 2 | MAT201 | Linear Algebra and Differential Equations | 4-0-0-4 | MAT101 |
| 2 | PHY201 | Thermodynamics and Statistical Mechanics | 3-0-0-3 | PHY101 |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 3 | GIS301 | Remote Sensing Fundamentals | 3-0-0-3 | GIS201 |
| 3 | MAT301 | Probability and Statistics | 4-0-0-4 | MAT201 |
| 3 | CS301 | Database Systems | 3-0-0-3 | CS201 |
| 3 | ENG301 | Technical Writing and Communication | 2-0-0-2 | - |
| 4 | GIS401 | Spatial Analysis and Modeling | 3-0-0-3 | GIS301 |
| 4 | MAT401 | Numerical Methods | 4-0-0-4 | MAT301 |
| 4 | CS401 | Web Development and GIS Integration | 3-0-0-3 | CS301 |
| 4 | PHYS401 | Geophysics and Geodesy | 3-0-0-3 | PHY201 |
| 5 | GIS501 | Advanced Remote Sensing Techniques | 3-0-0-3 | GIS401 |
| 5 | MAT501 | Operations Research | 4-0-0-4 | MAT401 |
| 5 | CS501 | Machine Learning for Geospatial Data | 3-0-0-3 | CS401 |
| 5 | ELEC501 | Electronics and Signal Processing | 3-0-0-3 | PHY201 |
| 6 | GIS601 | Disaster Management Using GIS | 3-0-0-3 | GIS501 |
| 6 | MAT601 | Optimization and Control Theory | 4-0-0-4 | MAT501 |
| 6 | CS601 | Big Data Analytics for GIS | 3-0-0-3 | CS501 |
| 6 | CSE601 | Capstone Project Preparation | 2-0-0-2 | CS501, GIS501 |
| 7 | GIS701 | Urban Planning and GIS | 3-0-0-3 | GIS601 |
| 7 | MAT701 | Mathematical Modeling | 4-0-0-4 | MAT601 |
| 7 | CS701 | Cloud Computing and GIS | 3-0-0-3 | CS601 |
| 7 | PHYS701 | Climate Change and Geospatial Solutions | 3-0-0-3 | PHYS401 |
| 8 | GIS801 | Final Year Thesis/Capstone Project | 6-0-0-6 | GIS701, CS701 |
| 8 | MAT801 | Research Methodology | 3-0-0-3 | MAT701 |
| 8 | CS801 | Project Management in GIS | 2-0-0-2 | CS701 |
Detailed Descriptions of Advanced Departmental Electives
Advanced departmental electives form a crucial part of the curriculum, providing students with specialized knowledge and skills in specific domains. These courses are designed to deepen understanding and foster innovation:
- Advanced Remote Sensing Techniques: This course delves into advanced methodologies for processing satellite data, including machine learning applications in remote sensing, multi-temporal analysis, and change detection algorithms. Students learn how to interpret complex imagery from various sensors and apply them to real-world problems such as land cover classification, urban growth tracking, and environmental monitoring.
- Machine Learning for Geospatial Data: Leveraging Python-based libraries like scikit-learn, TensorFlow, and PyTorch, this course focuses on building predictive models using spatial datasets. Topics include clustering, regression, neural networks, and deep learning architectures tailored for geospatial applications. Practical sessions involve working with large-scale datasets from platforms like Google Earth Engine and NASA.
- Spatial Decision Support Systems: Designed to equip students with tools for decision-making under uncertainty, this course explores the integration of GIS with multi-criteria evaluation methods, fuzzy logic, and simulation modeling. Students develop systems that assist stakeholders in making informed decisions related to resource allocation, policy planning, and risk assessment.
- GIS in Public Health: This elective combines spatial analysis with public health data to identify disease patterns, evaluate intervention strategies, and support epidemiological research. It includes case studies on infectious disease outbreaks, maternal mortality mapping, and access to healthcare services.
- Hydrological Modeling Using GIS: Focuses on modeling water cycles, flood prediction, watershed management, and drought monitoring using GIS tools. Students gain proficiency in hydrological software packages such as SWMM, HEC-HMS, and MIKE 21, while integrating satellite data for real-time applications.
- Urban Planning and GIS: Combines urban theory with spatial planning techniques to address challenges in sustainable city development. Students learn how to conduct spatial analysis of land use patterns, transportation networks, housing availability, and green space distribution using tools like ArcGIS and QGIS.
- Disaster Management Using GIS: Provides a comprehensive overview of how GIS can be used for disaster preparedness, response, and recovery. Topics include hazard mapping, evacuation planning, damage assessment, and post-disaster reconstruction strategies using real-time data from sensors and satellite platforms.
- Geospatial Web Development: Teaches students to create interactive web maps and mobile applications using HTML5, JavaScript, and GIS libraries such as Leaflet.js, OpenLayers, and Google Maps API. Students build real-world projects that integrate spatial data with user interfaces for public engagement and decision support.
- Sustainable Development Goals (SDGs) Mapping: This course introduces students to the SDGs framework and demonstrates how GIS can be used to track progress towards these global goals. Using datasets from UN agencies, students analyze indicators related to poverty reduction, education, gender equality, climate action, and biodiversity conservation.
- Big Data Analytics for GIS: Explores the challenges of processing massive volumes of geospatial data using distributed computing frameworks like Apache Spark and Hadoop. Students learn how to scale GIS applications for big data environments and develop scalable solutions for real-time monitoring systems.
Project-Based Learning Framework
The project-based learning approach at IIRS is designed to bridge the gap between theoretical knowledge and practical implementation. The program includes mandatory mini-projects in the second year and a final-year thesis/capstone project in the eighth semester.
Mini-Projects: These projects are assigned during the second year and involve small-scale, interdisciplinary tasks that allow students to apply concepts learned in core subjects. Each mini-project is supervised by a faculty member from the department and involves working with real-world datasets or simulated environments. Students learn how to formulate research questions, collect and analyze data, and present findings effectively.
Final-Year Thesis/Capstone Project: In the final year, students undertake an in-depth project that contributes original insights or develops a novel solution to a geospatial problem. Projects are selected based on student interests and aligned with ongoing research initiatives at IIRS or industry partnerships. Faculty mentors guide students throughout the process, from problem identification to final presentation.
Students select their projects through a proposal submission process where they present their ideas, methodology, and expected outcomes. The selection is based on feasibility, innovation, and relevance to current trends in GIS. Projects are evaluated using a rubric that assesses technical depth, creativity, presentation quality, and impact potential.