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Fees
₹6,00,000
Placement
95.0%
Avg Package
₹7,50,000
Highest Package
₹18,00,000
Fees
₹6,00,000
Placement
95.0%
Avg Package
₹7,50,000
Highest Package
₹18,00,000
Seats
120
Students
120
Seats
120
Students
120
The GIS Applications curriculum at Institute of Land and Disaster Management is structured to provide a balanced mix of theoretical knowledge, practical skills, and real-world applications. The program spans eight semesters with a carefully planned progression from foundational concepts to specialized areas.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| Semester I | GEOS101 | Introduction to Geography | 3-0-0-3 | - |
| COMP101 | Computer Fundamentals | 2-0-0-2 | - | |
| MATH101 | Calculus and Linear Algebra | 4-0-0-4 | - | |
| STAT101 | Statistics for Geospatial Data | 3-0-0-3 | MATH101 | |
| PHYS101 | Physics for Applied Sciences | 3-0-0-3 | - | |
| ENGL101 | English Communication Skills | 2-0-0-2 | - | |
| LIT101 | Introduction to GIS and Remote Sensing | 3-0-0-3 | - | |
| LAB101 | GIS Lab I | 0-0-2-1 | - | |
| LAB102 | Computer Lab I | 0-0-2-1 | - | |
| SEMINAR101 | Academic Writing and Presentation Skills | 1-0-0-1 | - | |
| Semester II | GEOS201 | Physical Geography | 3-0-0-3 | GEOS101 |
| COMP201 | Programming in Python | 2-0-0-2 | COMP101 | |
| MATH201 | Differential Equations and Probability | 4-0-0-4 | MATH101 | |
| STAT201 | Applied Statistics for Spatial Data | 3-0-0-3 | STAT101 | |
| ECON201 | Introduction to Economics | 3-0-0-3 | - | |
| PHYS201 | Geophysics for GIS Applications | 3-0-0-3 | PHYS101 | |
| LIT201 | Cartographic Principles and Design | 3-0-0-3 | LIT101 | |
| LAB201 | GIS Lab II | 0-0-2-1 | LAB101 | |
| LAB202 | Python Lab | 0-0-2-1 | LAB102 | |
| SEMINAR201 | Research Methodology | 1-0-0-1 | - | |
| Semester III | GEOS301 | Human Geography | 3-0-0-3 | GEOS201 |
| COMP301 | Data Structures and Algorithms | 3-0-0-3 | COMP201 | |
| MATH301 | Numerical Methods and Optimization | 4-0-0-4 | MATH201 | |
| STAT301 | Spatial Statistics | 3-0-0-3 | STAT201 | |
| ECON301 | Environmental Economics | 3-0-0-3 | ECON201 | |
| PHYS301 | Remote Sensing Principles | 3-0-0-3 | PHYS201 | |
| LIT301 | Geographic Information Science | 3-0-0-3 | LIT201 | |
| LAB301 | Remote Sensing Lab | 0-0-2-1 | - | |
| LAB302 | Database Lab | 0-0-2-1 | - | |
| SEMINAR301 | Professional Ethics and Sustainability | 1-0-0-1 | - | |
| Semester IV | GEOS401 | Regional Planning and Development | 3-0-0-3 | GEOS301 |
| COMP401 | Database Management Systems | 3-0-0-3 | COMP301 | |
| MATH401 | Advanced Mathematics for GIS | 4-0-0-4 | MATH301 | |
| STAT401 | Statistical Inference and Modeling | 3-0-0-3 | STAT301 | |
| ECON401 | Development Economics | 3-0-0-3 | ECON301 | |
| PHYS401 | Applications of Remote Sensing in Agriculture | 3-0-0-3 | PHYS301 | |
| LIT401 | Spatial Data Modeling and Analysis | 3-0-0-3 | LIT301 | |
| LAB401 | GIS Software Lab | 0-0-2-1 | LAB301 | |
| LAB402 | GIS Project Lab | 0-0-2-1 | LAB302 | |
| SEMINAR401 | Internship Preparation Workshop | 1-0-0-1 | - | |
| Semester V | GEOS501 | Urban Geography and Planning | 3-0-0-3 | GEOS401 |
| COMP501 | Web Mapping Technologies | 2-0-0-2 | COMP401 | |
| MATH501 | Time Series Analysis and Forecasting | 3-0-0-3 | MATH401 | |
| STAT501 | Machine Learning for Geospatial Data | 3-0-0-3 | STAT401 | |
| ECON501 | Urban Economics | 3-0-0-3 | ECON401 | |
| PHYS501 | Environmental Monitoring Using Satellite Data | 3-0-0-3 | PHYS401 | |
| LIT501 | Advanced GIS and Spatial Analysis | 3-0-0-3 | LIT401 | |
| LAB501 | Advanced GIS Lab | 0-0-2-1 | LAB401 | |
| LAB502 | Machine Learning for Geospatial Lab | 0-0-2-1 | LAB402 | |
| SEMINAR501 | Mini Project I | 1-0-0-1 | - | |
| Semester VI | GEOS601 | Regional Development and Policy | 3-0-0-3 | GEOS501 |
| COMP601 | Cloud Computing for Geospatial Applications | 2-0-0-2 | COMP501 | |
| MATH601 | Geometric and Topological Methods in GIS | 3-0-0-3 | MATH501 | |
| STAT601 | Bayesian Inference and Data Analysis | 3-0-0-3 | STAT501 | |
| ECON601 | Natural Resource Economics | 3-0-0-3 | ECON501 | |
| PHYS601 | Disaster Risk Assessment and Management | 3-0-0-3 | PHYS501 | |
| LIT601 | GIS in Public Health and Epidemiology | 3-0-0-3 | LIT501 | |
| LAB601 | Disaster Risk Management Lab | 0-0-2-1 | LAB501 | |
| LAB602 | Public Health GIS Lab | 0-0-2-1 | LAB502 | |
| SEMINAR601 | Mini Project II | 1-0-0-1 | SEMINAR501 | |
| Semester VII | GEOS701 | Global Environmental Change and Sustainability | 3-0-0-3 | GEOS601 |
| COMP701 | Mobile GIS and Location-Based Services | 2-0-0-2 | COMP601 | |
| MATH701 | Mathematical Modeling for Geospatial Applications | 3-0-0-3 | MATH601 | |
| STAT701 | Advanced Statistical Methods in GIS | 3-0-0-3 | STAT601 | |
| ECON701 | Environmental Policy and Governance | 3-0-0-3 | ECON601 | |
| PHYS701 | Climate Change Impact Assessment | 3-0-0-3 | PHYS601 | |
| LIT701 | Geospatial Data Visualization and Communication | 3-0-0-3 | LIT601 | |
| LAB701 | Climate Change Impact Lab | 0-0-2-1 | LAB601 | |
| LAB702 | Data Visualization Lab | 0-0-2-1 | LAB602 | |
| SEMINAR701 | Capstone Project Proposal | 1-0-0-1 | SEMINAR601 | |
| Semester VIII | GEOS801 | Final Year Thesis/Project | 3-0-0-3 | GEOS701 |
| COMP801 | Capstone Project Implementation | 2-0-0-2 | COMP701 | |
| MATH801 | Research Paper Writing and Presentation | 3-0-0-3 | MATH701 | |
| STAT801 | Thesis Review and Defense Preparation | 3-0-0-3 | STAT701 | |
| ECON801 | Final Project Presentation | 3-0-0-3 | ECON701 | |
| PHYS801 | Project Finalization and Documentation | 3-0-0-3 | PHYS701 | |
| LIT801 | Thesis Writing and Submission | 3-0-0-3 | LIT701 | |
| LAB801 | Final Project Lab | 0-0-2-1 | LAB701 | |
| LAB802 | Thesis Documentation Lab | 0-0-2-1 | LAB702 | |
| SEMINAR801 | Final Project Defense | 1-0-0-1 | SEMINAR701 |
Advanced departmental electives in the GIS Applications program are designed to provide students with specialized knowledge and practical skills in niche areas. These courses are offered based on student demand, faculty availability, and industry relevance.
This course delves into advanced methodologies for processing and analyzing satellite data for environmental monitoring, land use classification, and climate change studies. Students learn to apply machine learning algorithms to interpret multispectral and hyperspectral imagery, conduct temporal analysis of landscapes, and integrate geospatial data with ground-truth observations.
This course explores the application of GIS in urban planning, transportation modeling, housing policy, and community development. Students analyze spatial patterns in cities, evaluate urban growth trends, and design interventions using spatial data analytics tools and techniques.
This elective introduces students to cutting-edge machine learning models tailored for geospatial applications. Topics include deep learning architectures for image classification, regression models for predicting environmental variables, clustering algorithms for spatial pattern recognition, and reinforcement learning in urban systems.
This course focuses on building interactive web-based mapping platforms using open-source and commercial tools. Students learn to develop RESTful APIs, implement real-time data visualization, integrate third-party services, and deploy scalable geospatial applications for public access.
This course emphasizes the design and implementation of compelling visual representations of complex spatial data. Students explore principles of color theory, typography, interactivity, and user experience in mapping interfaces, applying them to create informative dashboards, animated maps, and immersive virtual environments.
This course addresses the use of GIS in identifying, assessing, and mitigating risks associated with natural disasters such as floods, earthquakes, hurricanes, and wildfires. Students learn to model disaster scenarios, develop early warning systems, and plan emergency response strategies using real-time spatial data.
This elective covers the design, implementation, and optimization of geospatial databases. Students gain hands-on experience with PostgreSQL/PostGIS, Oracle Spatial, and Microsoft SQL Server spatial extensions, learning how to manage large datasets efficiently while maintaining data integrity and performance standards.
This course examines the intersection of GIS and climate science, focusing on assessing impacts of global warming on ecosystems, water resources, agriculture, and human settlements. Students utilize climate models, satellite data, and statistical methods to project future changes and propose adaptation strategies.
This course teaches students how to use satellite imagery for environmental monitoring tasks such as deforestation tracking, pollution detection, coastal erosion assessment, and biodiversity mapping. Emphasis is placed on data preprocessing, classification techniques, and validation methods using field data.
This elective explores participatory approaches to GIS application in community engagement and grassroots development projects. Students learn to involve local stakeholders in mapping initiatives, gather qualitative spatial data through interviews and surveys, and develop community-driven solutions using collaborative mapping tools.
The department strongly believes in the power of experiential learning through project-based education. Project-based learning (PBL) is integrated throughout the curriculum to ensure that students gain practical experience and develop critical problem-solving skills.
The program includes two major projects: a Mini-Project in Semesters V and VI, followed by a comprehensive Final-Year Thesis/Capstone Project in Semester VIII. These projects are designed to be both challenging and relevant, encouraging students to apply theoretical concepts to real-world problems.
Each mini-project lasts for approximately 6 weeks and involves a team of 3-5 students working under the supervision of a faculty mentor. The projects are selected based on current industry trends, societal needs, or research interests identified by the department. Students must submit a project proposal outlining objectives, methodology, timeline, and expected outcomes before beginning work.
Mini-projects are evaluated based on several criteria including technical feasibility, innovation, data quality, presentation skills, peer feedback, and final deliverables such as reports, presentations, and demonstration software or tools. The evaluation process includes mid-term reviews, milestone assessments, and final presentations to faculty panels and industry experts.
The capstone project is a year-long endeavor that allows students to explore an area of personal interest within the broader field of GIS. Students propose their own research questions or collaborate with external organizations on applied projects. The project requires extensive literature review, data collection, analysis, and synthesis.
Students are paired with faculty advisors who guide them through each phase of the project, from conceptualization to completion. The final thesis must demonstrate mastery of advanced analytical techniques, clear communication of findings, and significant contribution to existing knowledge or practice in the field.
Students can propose project ideas or select from a list of suggested projects provided by faculty members or industry partners. The selection process considers factors such as resource availability, relevance to current challenges, feasibility within the timeframe, and alignment with career aspirations.
Mentors are assigned based on expertise areas, student preferences, and project requirements. Faculty mentors are typically senior professors or research scientists who have extensive experience in geospatial domains. Regular meetings and progress reports ensure that projects stay on track and meet academic standards.
