Semester-wise syllabus for B.Tech in Remote Sensing and GIS

Semester 1: Foundation Courses

1. Mathematics-I

   • Calculus, Linear Algebra, Differential Equations

   • Coordinate Geometry, Matrices

2. Physics

   • Mechanics, Optics, Electromagnetism

   • Basics of Waves and Radiation (for RS applications)

3. Chemistry / Environmental Science

   • Environmental Chemistry, Pollution Studies

   • Sustainable Development

4. Computer Fundamentals & Programming (C/Python)

   • Basics of Programming, Algorithms

   • Introduction to Python for Geospatial Applications

5. Engineering Graphics & CAD

   • Technical Drawing, GIS Map Layout Basics

6. Communication Skills & Technical English

   • Report Writing, Presentation Skills

Semester 2: Core Basics

1. Mathematics-II (Probability & Statistics)

   • Probability Distributions, Hypothesis Testing

   • Statistical Methods for Geospatial Data

2. Surveying & Geodesy

   • Chain Surveying, Theodolite, Total Station

   • GPS Fundamentals, Coordinate Systems

3. Introduction to Remote Sensing (RS) & GIS

   • Basics of RS, Electromagnetic Spectrum

   • GIS Components: Data Types, Vector vs. Raster

4. Digital Image Processing (DIP) Basics

   • Image Enhancement, Filtering, Histogram Equalization

5. Environmental Studies

   • Ecosystems, Biodiversity, Climate Change

6. Programming for Geospatial Analysis (Python/Matlab)

   • Geospatial Libraries (GDAL, Rasterio)

Semester 3: Core Remote Sensing & GIS

1. Geospatial Database Management

   • SQL, NoSQL for GIS, PostGIS

   • Spatial Queries

2. Cartography & Map Projections

   • Map Design, Symbology, Coordinate Systems

   • UTM, WGS84, Lambert Conformal

3. Satellite Remote Sensing

   • Optical, Thermal, and Microwave RS

   • Landsat, Sentinel, MODIS Sensors

4. GIS Data Structures & Algorithms

   • Raster & Vector Data Models

   • Spatial Indexing (Quadtree, R-tree)

5. Geoinformatics Applications

   • Case Studies in Urban Planning, Agriculture

6. Field Survey Techniques (Practical)

   • GNSS Surveying, Drone Data Collection

Semester 4: Advanced RS & GIS

1. Digital Image Processing for RS

   • Supervised/Unsupervised Classification

   • NDVI, PCA, Object-Based Image Analysis (OBIA)

2. Spatial Analysis & Modeling in GIS

   • Network Analysis, Hydrological Modeling

   • Geostatistics (Kriging, IDW)

3. Microwave & LiDAR Remote Sensing

   • SAR (Synthetic Aperture Radar) Basics

   • LiDAR Data Processing

4. Web GIS & Cloud Computing

   • ArcGIS Online, Google Earth Engine

   • GeoServer, Leaflet, OpenLayers

5. GPS & GNSS Technology

   • Differential GPS, RTK, GNSS Applications

6. Open-Source GIS Tools (QGIS, GRASS GIS)

Semester 5: Specialized Topics

1. Hyperspectral Remote Sensing

   • Spectral Libraries, Endmember Extraction

2. GIS Programming (Python, JavaScript)

   • ArcPy, GeoPandas, Folium, D3.js

3. Urban & Regional Planning using GIS

   • Land Use/Land Cover (LULC) Mapping

4. Remote Sensing for Disaster Management

   • Flood Mapping, Earthquake Risk Assessment

5. Machine Learning in Geospatial Analysis

   • Random Forest, CNN for Image Classification

6. Elective-I (Agriculture/Forestry/Hydrology)

Semester 6: Industry-Oriented Courses

1. Big Data Analytics in GIS

   • Hadoop, Spark for Geospatial Data

2. Geospatial AI & Deep Learning

   • U-Net, YOLO for Object Detection

3. Environmental Remote Sensing

   • Carbon Sequestration, Wetland Monitoring

4. GIS for Smart Cities

   • IoT Integration, Urban Heat Island Analysis

5. Elective-II (Oceanography/Climate Studies)

6. Internship / Industrial Training

Semester 7: Advanced Applications & Project Work

1. Geospatial Data Mining

   • Pattern Recognition, Clustering

2. 3D GIS & Terrain Modeling

   • DEM, TIN, LiDAR Point Clouds

3. Remote Sensing for Natural Resource Management

   • Mineral Exploration, Soil Moisture Mapping

4. Project Management & GIS

   • SDLC, Agile Methods in Geospatial Projects

5. Major Project (Phase-I – Research & Proposal)

6. Elective-III (Drone Technology/IoT in GIS)

Semester 8: Final Project & Professional Development

1. Major Project (Phase-II – Implementation & Thesis Submission)

2. Professional Ethics & GIS Industry Standards

3. Emerging Trends in Remote Sensing & GIS

4. Elective-IV (Geospatial Law/Policy)

Key Software & Tools Covered:

• GIS: ArcGIS, QGIS, GRASS GIS, ERDAS Imagine, ENVI

• Programming: Python (GDAL, GeoPandas, Scikit-learn), JavaScript (Leaflet, OpenLayers)

• Web GIS: GeoServer, PostGIS, Google Earth Engine

• Drones & LiDAR: Pix4D, LAS Tools

• Machine Learning: TensorFlow, PyTorch for Geospatial AI