This course is an introduction to GIS that offers a comprehensive introduction to the core principles and practical applications of GIS. Participants will gain hands-on experience with industry-standard GIS software and learn how to effectively utilize spatial data for various purposes. 

• Topics 
  Explore case studies and real-world examples of how GIS is used to solve complex problems and make data-driven decisions. 
  Understand the basics of GIS, its history, and its significance in various fields. 
  Discover the types of spatial data, including vector and raster data, and learn how to collect, manage, and interpret this data. 
  Explore different GIS data models, such as vector and raster models, and understand their applications and benefits in various scenarios. 
  Gain a clear understanding of map projections, their importance in GIS, and how to choose the appropriate projection for different types of spatial data.  

This on-job training program is designed to provide practical, hands-on experience in Geographic Information Systems (GIS). Participants will learn essential GIS skills through a structured approach that covers the entire workflow from project selection to final output. This training is ideal for professionals looking to enhance their GIS capabilities within a real-world context. 

• Topics 

Understand the scope and requirements of a GIS project. 

Select appropriate GIS tools and methodologies. 

Introduction to Geodatabase concepts and structures. 

Integrating various data types within the GDB. 

Techniques for collecting spatial and attribute data using GIS tools for field data collection and ensuring data accuracy and relevance. 

Clean and prepare data for analysis, filter and validate spatial data. 

Convert hardcopy maps and other analog data into digital format. 

Implement quality control measures throughout the GIS process using different techniques for verifying data accuracy and consistency. 

Design effective map layouts and visual representations. 

This course is an introduction to remote sensing that provide you with a foundational understanding of remote sensing technologies and their myriad applications. Whether participant is a novice or a professional looking to expand his skill set, this course will equip participant with the knowledge and practical experience to effectively utilize remote sensing data. 

• topics 
  Explore case studies and real-world examples of how GIS is used to solve complex problems and make data-driven decisions. 
  Understand the basics of GIS, its history, and its significance in various fields. 
  Discover the types of spatial data, including vector and raster data, and learn how to collect, manage, and interpret this data. 
  Explore different GIS data models, such as vector and raster models, and understand their applications and benefits in various scenarios. 
  Gain a clear understanding of map projections, their importance in GIS, and how to choose the appropriate projection for different types of spatial data.  

This course is an introduction to the latest ERDAS IMAGINE professional software. Instructors will present some basic concepts of remote sensing providing a foundation in image processing. Importing/Exporting functionality, Ribbon Interface Customization, Band math, generating indices for ground features, Image enhancement options, defining area of interests, correction methods for image geometry, tools for mosaicking portions of imagery in to one extent, applying different types of classification. 

• Topics
  Navigate around the ERDAS IMAGINE interfaces 
  Import digital data into the IMAGINE Environment 
  Use the IMAGINE Viewer to display imagery, vector files and other data sets 
  Perform spatial and spectral enhancements on multispectral imagery 
  Assign geographical coordinates to an image to create geometrically corrected and orthorectified imagery 
  Mosaic several images to produce one seamless output 
  Understanding the process of creating a seamless mosaic from multiple satellite images. 
  Techniques for aligning and blending images from different sources or times. 
  Introduction to change detection techniques for monitoring environmental and urban changes. 
  Using image differencing to identify significant changes over time. 

This course is covering more advanced functionality and tools in ERDAS IMAGINE. Instructors will present special techniques for image processing, methods for image analysis. Appling feature extraction and comparing before & after images, using semi-automated process of change detection, using ERDAS Expansion Pack additional functionality to detect differences and feature changes in images. Utilizing the drag and drop spatial flowcharting ERDAS environment to design and model geospatial data, building spatial models, defining selection and conditional criteria based on hundreds of mathematical, conditional operators to combine imagery and other spatial data formats. Using

• Topics 
  Using different techniques of change detection methods 
  Perform a basic land cover classification using a multispectral image 
  Perform supervised classification on multispectral images and using different methods to evaluate the signatures 
  Understand spatial models and basics, operator types, models and ports 
  Access, analyze and change image attributes with the Spatial Modeler 
  Provide object-based multi-scale image classification and feature extraction capabilities to reliably build and maintain accurate geospatial content 

In this topic, students will work with a variety of datasets to build feature models in order to automatically extract features of interest. This course will be guided by the instructor, but will not have a step-by-step manual. Instead, the students will have the freedom and the opportunity to create their own models, ask questions regarding the operators, and learn tips and tricks for creating an IMAGINE Objective feature project as it provides object-based multi-scale image classification and feature extraction capabilities to reliably build and maintain accurate geospatial content. 

• Topics
  Create a Single Feature Probability polygon feature model 
  Create a Single Feature Probability polyline feature model 
  Learn Raster Object Operators 
  Extracts features and classes 
  Utilize Vector Object Operators 
  Use a variety of Vector Object Operators 
  Clean up the end result with Vector Object Operators 
  Create a multi-class model