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Updated in [August 21st, 2023]
What skills and knowledge will you acquire during this course?
By taking this course on Land Use Land Cover Classification using GIS, ERDAS, ArcGIS, and Machine Learning, the learner will acquire the following skills and knowledge:
1. Understanding of the fundamentals of land use and land cover classification.
2. Proficiency in using ERDAS, ArcGIS, ENVI, and Machine Learning for image classification and calculations.
3. Knowledge of various methods of land use classification, including supervised, unsupervised, combined methods, and pixel correction methods.
4. Ability to preprocess images after download and correct error pixels.
5. Understanding of area-specific pixel correction techniques to achieve maximum accuracy.
6. Familiarity with generating accuracy assessment reports in ERDAS.
7. Ability to apply different land use methods based on the study area characteristics.
8. Proficiency in using Erdas versions 2014, 2015, 2016, and 2018, as well as ArcGIS versions 10.1 and above (e.g., 10.4, 10.7, or 10.8).
9. 90% practical knowledge and 10% theoretical understanding.
10. Problem-solving skills for common challenges faced during classification, such as distinguishing between urban areas and barren land, handling dry river reflections, classifying forest in hilly areas, and adding new classes after completing the classification process.
Overall, this course will equip the learner with the necessary skills and knowledge to confidently perform land use land cover classification using GIS, ERDAS, ArcGIS, and Machine Learning techniques.
How does this course contribute to professional growth?
This course on Land Use Land Cover Classification using GIS, ERDAS, ArcGIS, and Machine Learning contributes to professional growth by providing comprehensive knowledge and practical skills in the most demanding topic in GIS. The course covers everything from data download to final results, including pre-processing of images, error pixel correction, and theoretical concepts. By learning the various methods of land use classification, including supervised, unsupervised, combined methods, and pixel correction, professionals will gain the expertise to perform accurate land use classification without needing to seek external assistance. The course also focuses on Erdas and ArcGIS for image classification and calculations, providing in-depth understanding of all methods. Additionally, the course covers image classification with Machine Learning and includes the generation of accuracy assessment reports in Erdas. By addressing common problems faced during classification, such as distinguishing between urban areas and barren land, correcting errors in different types of areas, and adding new classes after final work, professionals will be equipped with the necessary skills to overcome these challenges. Overall, this course enhances professional growth by equipping individuals with the knowledge and skills to effectively perform land use land cover classification using GIS, ERDAS, ArcGIS, and Machine Learning.
Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education.
Course Syllabus
Downloading and Data Processing
Understanding Satellite image and Google Earth Pro
Which method to use and Why
Supervised Classification
Unsupervised classification
Combined classification
Error pixel correction and New Class Generation
Results from Landuse
Best Practical- Landuse Task in ArcGIS and ENVI
Miscellaneous
Miscellaneous Task - Cut Your Study Area
Download Data used in Course
Error Resolving
Machine Learning in ArcGIS for Image classification
Bonus
