Faculty of Technology
Curriculum
Course Curriculum and Course Structure:
| SEM-I | SEM-II | SEM III | SEM IV |
Studio 12 Credits | Geo-visualization and Spatial Analysis | GIS for Smart Cities | Geospatial Modelling and Application | DRP |
Mandatory Course 2 Credits | Remote Sensing: Theories and Practices | Advance Remote Sensing | Digital Photogrammetry and Terrain Analysis | |
Mandatory Course 2 Credits | Effective Communication | Spatial Analysis Techniques | Web GIS and Server Architecture | |
Mandatory Course 2 Credits | Geospatial Programming Methodology | Python Programming for Spatial Analysis | Applications of Spatial Big Data & Analytics | |
Summer Winter School and Electives – Worth 12 credits |
The courses/titles may change as a part of regular updating and improvements.
SEMESTER –I
Geo-visualization and Spatial Analysis
This studio is designed with a focus on inculcating the culture of mapping and learning geo visualization. - Specifically, the emphasis would be on understanding the basics of GIS and spatial databases to create meaningful maps for providing geospatial solutions to problems/issues/challenges. Geo visualization using vector and raster-based data models would be taught in depth. As a part of the studio, in order to facilitate the student’s ability to develop thematic geo visualization skills using GIS software a modular component on Introduction to GIS and cartography shall be covered. As a part of the studio, several interactions with experts and planners will be carried out to understand and solve problems using geospatial technology. Towards the end, the students would be able to solve or propose probable spatial solutions.
Key learning outcomes:
The studio learning aims to build skills related to collecting and compiling the information and then visualizing and representing this information in the form of maps.
Remote Sensing: Theories and Practices
This course provides the fundamentals of remote sensing and satellite image processing. The remote sensing data acquisition principles along with satellite image processing techniques would be covered. Specifically, several image enhancement techniques and classification algorithms will be covered with suitable examples.
Key learning outcomes:
After completing this course, the students will be able process data for various applications through different techniques. The students will be able to process satellite data, apply digital image processing algorithms and apply the tools of remote sensing such as image acquisition, image analysis, classification and validation.
Effective Communication
The course presents new paradigms of leadership communications in the form of maneuvers that can act as game changers in complex scenarios that requires critical thinking, comprehending ever evolving mutable market scenarios and interlacing changes in organizational structures, crucial decision making, and persuasive merits in interacting with internal and external stakeholders. The course presents new strategic frameworks of communication both theoretical and practical demonstrating their applications in diverse domestic and international corporate cases.
Key learning outcomes:
The student learns to understand communication principles and how to convey their work and finding verbally and in written form. As well as providing public presentations using case studies and personal experience.
Geospatial Programming Methodology
This course deals with programming skills and database development in the field of geospatial technology. The programming languages like html and java would be covered. Apart from the programming skills, the spatial database development component would be taught with hands on sessions.
Key learning outcomes:
The subject enables the student to understand how to build a basic web portal using HTML & JavaScript, which can handle GIS based data on the server. As well as web page development with Geospatial Datasets.
SEMESTER-II
Python Programming for Spatial Analysis
This subject emphasizes learning programming centered around geospatial applications. The students will be taught logic and sequence, the models and designs useful to write a program applied on geospatial data. Learning programming, their structure, flow in Python and getting acquainted with libraries curated for dealing geospatial data, would enable learners to see solutions to real time world problems through powerful capabilities offered by python libraries.
Key learning outcomes:
From this course, the student understands how to integrate spatial data into python and data science focused projects. The course covers visualizing various geospatial datasets (insitu, vector and raster datasets) with fundamental geospatial exploratory data analysis.
GIS for Smart Cities Studio:
This studio is designed with a focus on applying geospatial technology for building smart cities. The purpose of smart cities is to drive economic growth and improve the quality of life of people by enabling local area development and harnessing technology, especially technology that leads to smart outcomes. Specifically, the emphasis would be on the use of spatial datasets, tools, and techniques for providing geospatial solutions to smart city problems, undefined issues undefined challenges. Through this studio, the students learn how to handle the methods and challenges of GIS implementation in smart cities and develop unique geospatial applications.
Advance Remote Sensing
This course provides the theoretical foundation of Microwave and Hyper-spectral remote sensing. The Synthetic-aperture radar (SAR) polarimetry, interferometry techniques will be covered along with the hyper-spectral data processing and analysis.
Key learning outcomes:
The students in this subject learn the the complexity advance remote sensed datasets. As well as work on thematic applications using a hyperremote sensing and microwave remote sensing methods for projects.
Spatial Analysis Techniques
The course provides different methods and techniques for analyzing spatial data. Several thematic areas such as watershed analysis, multi criteria decision analysis (MCDA), nearest and neighborhood, network analysis, spatial interpolation point pattern analysis, spatial regression and hot spot analysis will be taught with several case studies.
Key learning outcomes:
This enables to students to integrate both raster and vector data for analyzing, solving and providing solutions and recommended to realworld problems.
SEMESTER-III
Geospatial Modelling and Application
In the studio, students will explore different methods, techniques, and technologies to build a 3D model and work with different 3D data structures, tools, and algorithms to handle the third-dimension aspect of the real world. All the data collection, creation, and application pertaining to 3D will be carried out in an urban environment to address real-world problems. Also, students will be working with open-source software/tools to interact with the 3D objects and customize the model as per the given application. The application and importance of the 3D model will be further explored in thematic areas in the GIS environment.
Key learning outcomes:
The teaches the student about the importance, analysing, tools and techniques for 3D centric problems towards applicability in solving realworld issues.
Applications of Spatial Big Data & Analytics
This course shall cover the fundamental concepts of Machine Learning, Artificial Intelligence and Deep Learning with special emphasis to Geospatial applications, Spatial and Nonspatial. From forecasting, to estimations to classification techniques. The tools themselves can assist in analyzing as well predicting key parameters within themes for various applications like traffic estimation, weather forecasting, error detection, material identification etc.
Key learning outcomes:
The course teaches the students about Machine Learning, its fundamentals and applicability in Geomatics.
Digital Photogrammetry and Terrain Analysis
This subject covers the principles for Photogrammetry with conventional and modern approaches. The emerging UAV technologies and their application will be covered towards 3D spatial object reconstruction. Also, the latest development in scanning and LIDAR technology will be covered with suitable examples. Through this course, students will be able to Understand the theoretical basics of photogrammetry and Extract 3D models using photogrammetric approach.
Web GIS and Server Architecture
The course will teach students to set up web services for creating maps, web services for managing spatial data, and webservices for processing spatial data. This course will challenge students to exercise critical thinking and technical knowledge needed to evaluate and develop successful Web GIS projects.
Key learning outcomes:
With this course, students can learn how to store and process spatial data using the PostgreSQL database and subsequently share spatial data using WMS and WFS protocols and develop their own a GIS system in the Web environment.