Table of Contents

1. Introduction to Geographic Information Systems (GIS) – Concepts and Applications

• Understanding the basics of GIS: Definition, components and types of data.

• Real-world applications of GIS in various industries such as urban planning, environmental management and emergency response.

• Introduction to GIS software and tools – overview of popular platforms like ArcGIS, QGIS and Google Earth.

2. Spatial Data Acquisition and Management

• Sources of Spatial Data: Introduction to different sources of spatial data including remote sensing, GPS, surveys and crowd-sourced data.

• Data Formats and Standards: Understanding common GIS data formats (shapefiles, geodatabases, GeoTIFF, etc.) and metadata standards.

• Data Collection Techniques and Considerations: Techniques for data collection, data accuracy considerations and metadata creation.

• Data Quality and Metadata Standards: Understanding data quality issues and metadata standards for spatial data.

• Data collection sources: Survey of India, Bhukosh, Bhuvan, USGS EarthExplorer.

3. Spatial Analysis Techniques

• Spatial Analysis Concepts and Methodologies: Introduction to spatial analysis techniques such as overlay operations, spatial queries, buffering and proximity analysis.

• Advanced Spatial Analysis Techniques: Introduction to more advanced techniques such as network analysis, 3D analysis and geostatistics.

• Integration with Other Technologies: Integration of GIS with other technologies such as GPS and CAD.

4. Advanced Spatial Analysis

• Hot spot analysis

• Cluster analysis

• Spatial interpolation techniques

5. Network Analysis

• Introduction

• Types of Network Analysis

• Best Route

• New Service Area

• Closest Facility

• Origin-Destination Cost Matrix

• Location-Allocation Analysis

6. Hydrology Analysis

• Introduction to Hydrology: Overview of hydrological processes and their importance in GIS.

• Watershed Delineation: Techniques for defining watershed boundaries using digital elevation models (DEMs).

• Flow Accumulation and Direction: Calculating flow accumulation and direction to identify stream networks.

• Hydrological Modeling: Introduction to hydrological modeling techniques for runoff estimation and flood forecasting.

• Hands-on Exercise: Watershed delineation and hydrological modeling using ArcGIS Hydrology tools or QGIS Hydrology plugins.

7. Terrain Analysis

• Digital Elevation Models (DEMs): Understanding DEM data and its applications in terrain analysis.

• Slope and Aspect Analysis: Calculating slope and aspect to understand terrain characteristics.

• Visibility Analysis: Analyzing visibility across a landscape to identify visible areas from specific vantage points.

• Terrain Classification: Classifying terrain based on slope categories (e.g., flat, moderate, steep).

• Hands-on Exercise: Terrain analysis tasks using ArcGIS Spatial Analyst or QGIS Terrain Analysis tools.

8. Cross –Section Profile Analysis

• Introduction to Cross-Section Profiles: Understanding the concept of cross-section profiles in terrain analysis.

• Profile Extraction: Extracting cross-sectional profiles from DEM data along user-defined lines.

• Profile Visualization and Analysis: Visualizing and analyzing cross-section profiles to understand terrain features such as elevation changes and slope variations.

• Interpreting Cross-Section Profiles: Interpreting cross-section profiles to identify landforms, drainage patterns, and geological features.

• Hands-on Exercise: Extracting and analyzing cross-section profiles using ArcGIS 3D Analyst or QGIS profile tools.

9. Applications in Hydrology and Environmental Management

• Flood Risk Assessment: Using hydrology and terrain analysis to assess flood risk areas.

• Watershed Management: Applying hydrology analysis to develop watershed management plans.

• Erosion and Sedimentation Studies: Using terrain analysis to study erosion and sedimentation patterns.

• Infrastructure Planning: Incorporating cross-section profile analysis in infrastructure planning for roads, pipelines and drainage systems.

• Hands-on Exercise: Integrating hydrology analysis, terrain analysis and crosssection profiling in a real-world environmental management scenarios. 10. LiDAR Data Processing

• Introduction to LiDAR data

• LiDAR data acquisition methods

10. LiDAR Data Processing

• Introduction to LiDAR data

• LiDAR data acquisition methods

• LiDAR point cloud processing

• Digital Surface Model (DSM) generation

• Digital Terrain Model (DTM) generation

• LiDAR-based feature extraction

11. Introduction to Remote Sensing

• Basics of Remote Sensing Technology: Introduction to remote sensing principles, satellite and aerial platforms and sensors.

• Image Interpretation Techniques: Introduction to basic image interpretation techniques for land cover classification and feature extraction.

• Image Processing Software: Introduction to image processing software such as ERDAS Imagine for image analysis and manipulation.

• Hands-on Exercises: Image interpretation and processing tasks using remote sensing software.

12. Remote Sensing Data Analysis

• Land Use/Land Cover Classification

• Land Use Land Cover Change Detection

• Normalized Difference Vegetation Index (NDVI)

• Normalized Difference Water Index (NDWI)

• Normalized Difference Built-up Index (NDBI)

• Land Surface Temperature (LST)

13.Cartography and Map Design

• Principles of Cartography and Map Design: Understanding cartographic principles, typography, symbolization and layout design.

• Interactive Mapping and Web Mapping: Introduction to principles of interactive and web mapping.

• Introduction to ArcGIS Pro: Basics of using ArcGIS Pro for map design and layout.

• Hands-on Exercises: Creating thematic maps, designing layouts and creating web maps using ArcGIS Pro or QGIS.

14.Advanced GIS Analysis

• Advanced Spatial Analysis Techniques: In-depth exploration of advanced spatial analysis techniques such as spatial modeling, decision support systems and geostatistics.

• Integration with Python: Introduction to using Python scripting for GIS automation and advanced analysis.

• Hands-on Exercises: Developing Python scripts for advanced GIS analysis tasks.

15. Species distribution modeling (SDM)

• Habitat suitability analysis

• Ecological niche modeling

• Landscape connectivity analysis

16. Web Mapping and GIS Applications

• Introduction to Web Mapping Technologies: Overview of web mapping technologies such as Leaflet and OpenLayers.

• Creating Interactive Web Maps: Using ArcGIS Online or QGIS Cloud to create and share interactive web maps.

• Publishing GIS Data and Services on the Web: Publishing GIS data and services on the web using ArcGIS Online or QGIS Cloud.

• Developing Custom Web Mapping Applications: Introduction to developing custom web mapping applications using JavaScript and web mapping APIs.

• Hands-on Exercises: Developing and deploying custom web mapping applications.

17.Advanced GIS Software Applications

• Introduction to Specialized GIS Software: Introduction to specialized GIS software such as MicroStation and AutoCAD.

• Integration of CAD and GIS Data: Techniques for integrating CAD and GIS data for various applications.

• 3D Modeling and Visualization in GIS: Introduction to 3D modeling and visualization techniques in GIS.

• Hands-on Exercises: Using specialized GIS software for specific applications such as 3D modeling and visualization.

18. Geospatial Machine Learning

• Introduction to Geospatial Machine Learning: Overview of machine learning techniques applied in GIS.

• Classification and Regression Algorithms: Understanding supervised learning algorithms for classification and regression tasks.

• Feature Extraction and Dimensionality Reduction: Techniques for extracting meaningful features from geospatial data and reducing dimensionality.

• Spatial Regression and Prediction: Applying machine learning models for spatial regression and prediction tasks.

• Hands-on Exercise: Implementing geospatial machine learning algorithms using Python libraries such as scikit-learn and TensorFlow within ArcGIS or QGIS environments.

19. Big Data Analytics in GIS

• Challenges and Opportunities of Big Data in GIS: Understanding the impact of big data on GIS analysis and decision-making.

• Spatial Data Processing and Analysis: Techniques for processing and analyzing large volumes of spatial data efficiently.

• Distributed Computing: Introduction to distributed computing frameworks such as Apache Hadoop and Spark for big data analytics in GIS.

• Real-time GIS Applications: Exploring real-time GIS applications and streaming data analysis.

• Hands-on Exercise: Implementing big data analytics workflows for spatial analysis using open-source tools and cloud-based platforms.

20.Project Work and Presentation

• Define Project Objectives

• Select Topic and Geographic Area

• Data Collection and Preparation

• Project Design and Workflow

• Spatial Analysis

• Analysis Results and Interpretation

• Documentation and Submission

This Course includes Comprehensive Drone Technology Course

Index

1. Drone Mapping Beginner Guide

2. 2D & 3D Modeling Using Drone Images

3. Post-Processing of Drone Data

4. Drone Video Geo-Tagging

5. LiDAR Drone Data Processing

Drone Mapping Beginner Guide

• Basics of Drone Survey and Terminologies

• Workflow of Drone Mapping

• Flying Techniques and Data Capture

• Basic Image Processing

2D & 3D Modeling Using Drone Images

• Pix4D Basic & Advanced Techniques

Post-Processing of Drone Data

• Introduction and Topics

• Installation of Software

• Orthomosaic Conversion and Tiling

• Orthomosaic Tiling

• Quality Check of Orthomosaic Map

• GSD Check

• Relative Accuracy Check

• Absolute Accuracy Check

• Root Mean Square Error (RMSE)

• Contour Line Generation

• Volumetric Calculation

• DTM & DSM Analysis

• Generating 3D Videos from DTM

• Digitization

• Digitization of Orthomosaic Map

• Layout Creation

• ORI Tiling

• Printing

Drone Video Geo-Tagging

• Software Link and Setup

• Merging Multiple SRT Files

• Generating Circuit Patterns (Method 1 and Method 2)

• Video Geo-Tagging Software (Various Methods)

• Adding Road Blinking Lines on Drone Video

LiDAR Drone Data Processing

• Understanding LiDAR Technology and Applications

• Drone Selection for LiDAR Integration

• LiDAR Sensor Selection

• Software Setup for Data Acquisition and Processing

• Processing Collected LiDAR Data

• Analyzing and Interpreting Processed Data

Drone in Mining

Google Earth Pro in Drone Mapping, Flight Planning, KML & KMZ

Includes Comprehensive GIS Developer Course (Advanced Level) Updated 2025-2026

1. Foundations & Advanced Analytical Skills

• GIS and Advanced Analytics with Artificial Intelligence (AI)

• Advanced Analytical Thinking to Solve Enterprise Problems (Case Studies)

2. Geospatial Programming with Python

• Introduction to Geospatial Python Libraries

• Terrain Indices & Geemap in Python

• Python for Geospatial Automation: GeoPandas, ArcPy, Rasterio, PyQGIS, PySAL, NumPy

3. QGIS

• QGIS2Web for Web Map Export

4. Spatial Databases & Web Mapping

• Spatial Databases with PostgreSQL + PostGIS (SQL Queries, Indexing)

• Web Publishing with GeoServer + SLD Styling

• Web Mapping & Dashboards: Folium, QGIS2Web, Plotly, AGOL Dashboards

• Creating Web Gis Application (A-Z)

5. GIS Application Domains

• Utility Mapping

• Crop & Forest Analysis

6. Hands-On Projects & Capstone

• Clean Zoning Boundary from Digitization

• NDVI Terrain Index using Model Builder

• Python-Based Spatial Quality Control Workflow

• Time-Series NDVI Dashboard using Google Earth Engine

• Spatial SQL Buffer & Area Statistics in PostGIS

• Web Map Export using QGIS2Web

• Urban Infrastructure Planning Layout

• Capstone Project with Final Report & GitHub Portfolio

7.Addons

• Field Survey Technologies: Survey123 & QField

• AutoCAD Viewer & SCP Plugin Basics

INCLUDED

GIS and Remote Sensing Course Schedule and Project Topics

This schedule outlines topics to be covered in individual classes, with a focus on practical applications using Google Earth Engine and ArcGIS Pro/ArcMap.