NEW COURSE • INTRODUCTORY OFFER VALID FOR 5 DAYS

Automated Land Use Land Cover Using Pretrained AI ArcGIS Pro

A practical workflow for faster Land Use/Land Cover classification using pretrained Deep Learning AI models with Landsat and Sentinel-2 imagery.

Land Use/Land Cover classification traditionally requires considerable time for preparing training samples, manually identifying land-cover features, creating training signatures, testing classifiers, and repeatedly refining the classification.

With Pretrained Deep Learning AI Models in ArcGIS Pro, much of this work can now be simplified.

In this practical course, you will learn how to classify Landsat and Sentinel-2 satellite imagery using pretrained AI models and develop LULC maps through a faster, more automated workflow.

What You No Longer Need to Do

✓ No need to create hundreds of training samples before classification
✓ No need to manually identify every land-cover feature across the study area
✓ No need to build training signatures for each class
✓ No need to train a Deep Learning model from scratch
✓ No need to prepare thousands of labelled images for AI model training
✓ No need to spend days developing your own classification model
✓ No advanced Deep Learning programming knowledge required

Instead, you will learn how to apply existing pretrained Deep Learning models directly to satellite imagery in ArcGIS Pro.

What You Will Learn

✓ Using Pretrained Deep Learning Models in ArcGIS Pro
✓ Automated Land Use Land Cover Classification
✓ Landsat Image Classification
✓ Sentinel-2 High-Resolution Classification
✓ Satellite Image Preparation for AI Classification
✓ Identification of Misclassified Areas
✓ Post-Classification Corrections
✓ Merging Land-Cover Classes
✓ Class-wise Area Calculation
✓ Classification Accuracy Assessment

The course takes you through the complete workflow—from satellite imagery preparation to the final classified LULC map and accuracy assessment.

View Course on Udemy →

Introductory discount is applied through the course link.

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