Curriculum Overview
The Cartography program at Indian Institute Of Remote Sensing is meticulously structured to ensure a comprehensive and progressive educational experience. The curriculum spans eight semesters, with each semester carefully designed to build upon the previous one while introducing advanced concepts and specialized skills.
| Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CG101 | Introduction to Cartography | 3-0-0-3 | - |
| 1 | CH101 | Chemistry for Geospatial Sciences | 3-0-0-3 | - |
| 1 | MA101 | Mathematics I | 4-0-0-4 | - |
| 1 | PH101 | Physics for Geospatial Applications | 3-0-0-3 | - |
| 1 | GE101 | Geography Fundamentals | 3-0-0-3 | - |
| 1 | CS101 | Introduction to Computer Programming | 2-0-2-4 | - |
| 1 | LAB101 | Basic Cartography Lab | 0-0-3-1.5 | - |
| 2 | CG201 | Remote Sensing Principles | 3-0-0-3 | PH101, MA101 |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | MA201 | Mathematics II | 4-0-0-4 | MA101 |
| 2 | PH201 | Geophysical Measurements | 3-0-0-3 | PH101 |
| 2 | GE201 | Introduction to Geographic Information Systems | 3-0-0-3 | GE101 |
| 2 | LAB201 | Remote Sensing Lab | 0-0-3-1.5 | - |
| 3 | CG301 | Digital Image Processing | 3-0-0-3 | CS201, PH201 |
| 3 | CS301 | Database Management Systems | 3-0-0-3 | CS201 |
| 3 | MA301 | Probability and Statistics for Geospatial Sciences | 3-0-0-3 | MA201 |
| 3 | GE301 | Spatial Database Management | 3-0-0-3 | GE201 |
| 3 | PH301 | Geodetic Surveying | 3-0-0-3 | PH201 |
| 3 | LAB301 | GIS Lab | 0-0-3-1.5 | - |
| 4 | CG401 | Cartographic Design and Visualization | 3-0-0-3 | CG201, GE201 |
| 4 | CS401 | Advanced Programming Techniques | 3-0-0-3 | CS201 |
| 4 | MA401 | Mathematics III | 4-0-0-4 | MA301 |
| 4 | GE401 | Spatial Statistics and Modeling | 3-0-0-3 | GE301, MA301 |
| 4 | PH401 | Geophysical Data Analysis | 3-0-0-3 | PH301 |
| 4 | LAB401 | Advanced Cartography Lab | 0-0-3-1.5 | - |
| 5 | CG501 | Environmental Monitoring and Change Detection | 3-0-0-3 | CG201, CG301 |
| 5 | CS501 | Machine Learning for Geospatial Data | 3-0-0-3 | CS401, MA301 |
| 5 | GE501 | Urban Planning and Development | 3-0-0-3 | GE201 |
| 5 | PH501 | Geospatial Data Fusion | 3-0-0-3 | PH301, PH401 |
| 5 | LAB501 | Research Project Lab | 0-0-3-1.5 | - |
| 6 | CG601 | Defense and Security Applications | 3-0-0-3 | CG201, CG401 |
| 6 | CS601 | Big Data Analytics for Geospatial Sciences | 3-0-0-3 | CS501, MA301 |
| 6 | GE601 | Public Policy and Spatial Planning | 3-0-0-3 | GE501 |
| 6 | PH601 | Geospatial Intelligence Frameworks | 3-0-0-3 | PH501 |
| 6 | LAB601 | Capstone Project Lab | 0-0-3-1.5 | - |
| 7 | CG701 | Advanced Cartographic Communication | 3-0-0-3 | CG401, GE501 |
| 7 | CS701 | Mobile Mapping and Navigation Systems | 3-0-0-3 | CS601 |
| 7 | GE701 | Research Methodology in Cartography | 3-0-0-3 | GE601 |
| 7 | PH701 | Geospatial Modeling and Simulation | 3-0-0-3 | PH601 |
| 7 | LAB701 | Capstone Research Lab | 0-0-3-1.5 | - |
| 8 | CG801 | Thesis Project | 0-0-6-6 | CG701, GE701 |
| 8 | CS801 | Capstone Internship | 0-0-6-3 | - |
| 8 | GE801 | Professional Practice and Ethics in Cartography | 2-0-0-2 | - |
| 8 | PH801 | Advanced Geospatial Technologies | 3-0-0-3 | PH701 |
Advanced Departmental Elective Courses
Departmental electives form a crucial component of the curriculum, allowing students to delve deeper into specialized areas of interest. These courses are taught by renowned faculty members and often involve collaborative projects with industry partners or research institutions.
1. Machine Learning for Geospatial Data
This advanced course explores the application of machine learning algorithms in analyzing geospatial datasets. Students learn to implement neural networks, decision trees, and clustering techniques specifically tailored for spatial data. Topics include deep learning architectures for satellite image classification, time series forecasting for environmental monitoring, and automated feature extraction from complex geospatial datasets.
2. Big Data Analytics for Geospatial Sciences
In this course, students are introduced to big data frameworks such as Hadoop and Spark, and their application in handling large-scale spatial datasets. The curriculum covers data warehousing, real-time processing pipelines, and scalable analytics platforms for cartographic applications.
3. Geospatial Data Fusion
This course focuses on integrating heterogeneous data sources to create comprehensive geospatial models. Students learn about sensor fusion techniques, multi-source data integration, and data quality assessment methods used in modern cartographic systems.
4. Geospatial Intelligence Frameworks
Students explore frameworks for extracting actionable intelligence from geospatial information. The course covers military applications of geospatial intelligence, intelligence gathering techniques, and the development of analytical tools for threat assessment and surveillance.
5. Mobile Mapping and Navigation Systems
This course delves into the technical aspects of mobile mapping systems, including GPS technology, inertial navigation sensors, and real-time positioning algorithms. Students work with modern mapping devices and software to develop applications for autonomous vehicles and location-based services.
6. Geospatial Modeling and Simulation
This advanced course teaches students how to build and validate spatial models using simulation techniques. Topics include agent-based modeling, cellular automata, Monte Carlo simulations, and scenario planning for urban development and environmental impact assessments.
7. Advanced Cartographic Communication
This course emphasizes the effective communication of complex geospatial information through visual design principles, storytelling techniques, and interactive media. Students learn to create compelling narratives using maps, infographics, dashboards, and multimedia presentations for diverse audiences.
8. Research Methodology in Cartography
This foundational course introduces students to research methodologies specific to cartographic disciplines. It covers literature review techniques, hypothesis formulation, experimental design, data collection methods, and ethical considerations in geospatial research.
9. Environmental Impact Assessment
This course explores how cartographic tools are used in environmental impact assessments for development projects. Students learn to conduct spatial analysis of ecological impacts, assess biodiversity loss, and develop mitigation strategies using GIS and remote sensing technologies.
10. Urban Planning and Development
This interdisciplinary course integrates cartographic principles with urban planning theory and practice. Students study land use planning, transportation network design, housing policies, and sustainable development practices through spatial analysis and mapping exercises.
Project-Based Learning Philosophy
The department's philosophy on project-based learning emphasizes hands-on experience and real-world problem-solving. The curriculum integrates mini-projects throughout the program to provide students with opportunities to apply theoretical concepts in practical contexts.
Mini-Projects Structure
Mini-projects are introduced from the second year, with each project lasting 6-8 weeks. These projects typically involve small teams of 3-5 students and focus on specific aspects of cartographic practice such as map creation, data analysis, or software development. Projects are selected based on industry needs, faculty research interests, or emerging trends in geospatial technology.
Final-Year Thesis/Capstone Project
The final-year capstone project is a significant undertaking that requires students to conduct original research or develop a comprehensive solution to a real-world cartographic challenge. Students work closely with faculty mentors to define research questions, design methodologies, collect and analyze data, and present findings in both written and oral formats.
Evaluation Criteria
Projects are evaluated based on technical competence, innovation, teamwork, presentation quality, and adherence to project timelines. Faculty members from multiple disciplines provide feedback to ensure comprehensive assessment of student performance. The evaluation process includes peer reviews, self-assessments, and mentor evaluations.
Project Selection Process
Students select their projects in consultation with faculty mentors, considering their interests, career goals, and available resources. Projects may be chosen from a list provided by the department or proposed by students based on their research ideas or industry connections.