Automated Land Use Land Cover Using Pretrained AI ArcGIS Pro
Land use landcover classification using Pretrained Deep Models in ArcGIS Pro with Sentinal-2 and Landsat 8-9, AI LULC
What you'll learn
⚡ Understand how pretrained Deep Learning models can be used for fast Land Use/Land Cover classification in ArcGIS Pro
⚡ Learn how AI-based and Deep Learning classification differs from traditional supervised image classification methods.
⚡ Download, organize, and use pretrained AI/Deep Learning models for satellite image classification.
⚡ Install and prepare the required ArcGIS Pro Deep Learning libraries and supporting files.
⚡ Understand the hardware, GPU, software, and image requirements for running pretrained Deep Learning models.
⚡ Select suitable satellite imagery and understand the image requirements for AI-based classification.
⚡ Download and prepare Landsat imagery for Land Use/Land Cover classificatio
⚡ Apply a pretrained Deep Learning model to Landsat imagery in ArcGIS Pro.
⚡ Download and prepare Sentinel-2 high-resolution satellite imagery for AI classification
⚡ Apply a pretrained Deep Learning model to Sentinel-2 imagery for LULC mapping.
⚡ Prepare satellite images, verify projections, and organize data before running Deep Learning classification.
⚡ Understand the complete workflow from satellite image preparation to AI-generated LULC maps.
⚡ Correct and refine Deep Learning classification results to improve the final Land Use/Land Cover map.
⚡ Merge or reorganize land-cover classes according to research and project requirements.
⚡ Calculate class-wise Land Use/Land Cover area from classified pixels.
⚡ Perform classification accuracy assessment for both Landsat and Sentinel-2 results.
⚡ Understand how pretrained models can reduce the need to build and train a Deep Learning model from scratch.
⚡ Apply the complete pretrained Deep Learning + ArcGIS Pro workflow to research, thesis, dissertation, and professional GIS projects.
Requirements
❗ Basic knowledge of Remote Sensing and GIS is helpful but not mandatory.
❗ Basic familiarity with ArcGIS Pro will make the course easier to follow.
❗ Must have ArcGIS Pro Installed
❗ A dedicated GPU/graphics processor is recommended for faster Deep Learning processing
❗ Basic understanding of satellite imagery such as Landsat and Sentinel-2 is useful
❗ No prior experience in Deep Learning model development or programming is required.
❗ No need to train a Deep Learning model from scratch; the course focuses on using pretrained model
❗ Students should be comfortable with basic computer tasks such as downloading files, managing folders, and extracting compressed data
Description
LULC Using Pretrained Deep Learning Models in ArcGIS Pro
Learn how to performLand Use Land Cover (LULC) classification using pretrained Deep Learning and AI models in ArcGIS Pro with real satellite imagery fromLandsat and Sentinel-2.
This course provides a practical, step-by-step workflow for researchers, GIS professionals, students, and remote sensing users who want to useArtificial Intelligence and Deep Learning for satellite image classification without developing or training a deep learning model from scratch.
Instead of following only traditional supervised classification methods, you will learn howpretrained AI models can be applied to satellite imagery for faster Land Use/Land Cover mapping. The course covers the complete workflow—from understanding data and hardware requirements to downloading satellite imagery, preparing datasets, running pretrained models, correcting classification errors, calculating land-use area, and assessing classification accuracy.
What You Will Learn
You will learn how to
✨ Understand howAI-based LULC classification differs from traditional image classification
✨ Select suitable satellite imagery for deep learning classification
✨ Work withLandsat and Sentinel-2 satellite data
✨ Understand image, software, and hardware requirements for AI classification
✨ Configure your computer for better deep learning processing performance
✨ Identify the graphics processor/GPU available on your system
✨ Download and organize required satellite data andpretrained Deep Learning models
✨ Install and prepare supportingArcGIS Pro Deep Learning libraries
✨ Set up a structured ArcGIS Pro project for Land Use/Land Cover analysis
✨ Check and manage satellite image projections
✨ Prepare Landsat imagery for AI-based classification
✨ PerformLandsat LULC classification using a pretrained AI model
✨ Identify classification errors and misclassified pixels
✨ Correct and improve the classified Land Use/Land Cover map
✨ Merge land-cover classes according to project requirements
✨ CalculateLand Use/Land Cover area from classified pixels
✨ Download and preparehigh-resolution Sentinel-2 imagery
✨ PerformSentinel-2 classification using a pretrained Deep Learning AI model
✨ Reorganize and merge Sentinel-2 land-cover classes
✨ Calculate class-wise land-use area from Sentinel-2 classification
✨ Performclassification accuracy assessment in ArcGIS Pro
✨ Apply the same accuracy assessment methodology to both Landsat and Sentinel-2 classifications
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
Landsat Deep Learning Classification
The course includes a complete workflow for working withLandsat satellite imagery. You will learn how to download Landsat images for longer time periods, visualize and prepare the imagery, check projections, and classify the images using apretrained AI model in ArcGIS Pro.
The workflow does not stop after generating the classification. You will also learn how to identify misclassified areas, apply corrections, merge classes based on your research requirements, and calculate the area covered by individual Land Use/Land Cover classes.
Sentinel-2 Deep Learning Classification
You will also perform LULC classification usingSentinel-2 high-resolution satellite imagery.
The course demonstrates how to download and prepare Sentinel-2 imagery, apply apretrained Deep Learning model, organize the resulting land-cover classes, and calculate class-wise areas.
Using both Landsat and Sentinel-2 gives you experience with two of the most widely used Earth observation datasets inRemote Sensing, GIS, environmental research, urban studies, hydrology, agriculture, and land-change analysis.
Classification Accuracy Assessment
Producing a classified map is only part of a scientific LULC study.
The course therefore includes a dedicated section onclassification accuracy assessment in ArcGIS Pro. You will learn a practical methodology that can be applied to classifications generated from both Landsat and Sentinel-2 imagery.
This makes the workflow useful not only for creating maps but also forresearch projects, dissertations, theses, journal papers, environmental studies, and professional GIS projects.
Why Pretrained Deep Learning Models?
Training a deep learning model from the beginning may require large labelled datasets, extensive computational resources, programming knowledge, and considerable processing time.
Pretrained Deep Learning models provide an alternative approach by allowing existing AI models to be applied to suitable satellite imagery.
In this course, the emphasis is on thepractical application of pretrained models in ArcGIS Pro so that you can understand the complete satellite-image-to-LULC-map workflow without first becoming a deep learning programmer.
Who Is This Course For?
This course is suitable for
✨ GIS and Remote Sensing students
✨ PhD and Master's researchers
✨ Geography and Environmental Science students
✨ Civil and Environmental Engineering researchers
✨ Hydrology and Watershed researchers
✨ Urban and Regional Planning professionals
✨ Agriculture and Natural Resource researchers
✨ ArcGIS and ArcGIS Pro users
✨ Professionals working with Landsat or Sentinel-2 imagery
✨ Researchers interested inAI, Deep Learning, Remote Sensing, and LULC classification
Basic familiarity with GIS or Remote Sensing will be useful, but the course explains the complete classification workflow step by step.
Satellite Data Used
The practical exercises include
Landsat Satellite Imagery
Learn the complete workflow for preparing and classifying Landsat images using pretrained AI models.
Sentinel-2 Satellite Imagery
Learn how to use higher-resolution Sentinel-2 imagery for Deep Learning-based Land Use/Land Cover classification.
Software
The course workflow is primarily implemented usingArcGIS Pro together with the requiredDeep Learning libraries and pretrained AI models.
By the end of this course, you will be able to take satellite imagery from its initial preparation stage throughAI-based Land Use/Land Cover classification, post-classification correction, class merging, area estimation, and accuracy assessment.
If you want to explore howPretrained Deep Learning Models, Artificial Intelligence, ArcGIS Pro, Landsat, and Sentinel-2 can be combined for practical Land Use/Land Cover mapping, this course provides a complete applied workflow.
Who this course is for
⭐ GIS and Remote Sensing students who want to learn practical AI-based Land Use/Land Cover classification.
⭐ Master’s and PhD researchers working with satellite imagery, LULC mapping, environmental studies, hydrology, agriculture, or urban analysis.
⭐ ArcGIS Pro users who want to use pretrained Deep Learning models for image classification.
⭐ Researchers who want to apply Deep Learning without training a model from scratch.
⭐ Professionals working with Landsat and Sentinel-2 satellite imagery.
⭐ Geography, Environmental Science, Civil Engineering, Agriculture, Forestry, and Natural Resource students or professionals
⭐ Researchers conducting thesis, dissertation, journal paper, or project-based LULC analysis.
⭐ GIS analysts who want to learn the complete workflow from image preparation to classification, correction, area calculation, and accuracy assessment.
⭐ Students interested in combining Artificial Intelligence, Deep Learning, Remote Sensing, and GIS.
⭐ Anyone who wants a practical introduction to pretrained Deep Learning models in ArcGIS Pro for Land Use/Land Cover mapping.
Published 9/2026
Created by Lakhwinder Singh
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Expert | Genre: eLearning | Language: English | Duration: 28 Lectures ( 3h 7m ) | Size: 3.45 GB
Download
🔒 Go Premium to Unlock
Land use landcover classification using Pretrained Deep Models in ArcGIS Pro with Sentinal-2 and Landsat 8-9, AI LULC
What you'll learn
⚡ Understand how pretrained Deep Learning models can be used for fast Land Use/Land Cover classification in ArcGIS Pro
⚡ Learn how AI-based and Deep Learning classification differs from traditional supervised image classification methods.
⚡ Download, organize, and use pretrained AI/Deep Learning models for satellite image classification.
⚡ Install and prepare the required ArcGIS Pro Deep Learning libraries and supporting files.
⚡ Understand the hardware, GPU, software, and image requirements for running pretrained Deep Learning models.
⚡ Select suitable satellite imagery and understand the image requirements for AI-based classification.
⚡ Download and prepare Landsat imagery for Land Use/Land Cover classificatio
⚡ Apply a pretrained Deep Learning model to Landsat imagery in ArcGIS Pro.
⚡ Download and prepare Sentinel-2 high-resolution satellite imagery for AI classification
⚡ Apply a pretrained Deep Learning model to Sentinel-2 imagery for LULC mapping.
⚡ Prepare satellite images, verify projections, and organize data before running Deep Learning classification.
⚡ Understand the complete workflow from satellite image preparation to AI-generated LULC maps.
⚡ Correct and refine Deep Learning classification results to improve the final Land Use/Land Cover map.
⚡ Merge or reorganize land-cover classes according to research and project requirements.
⚡ Calculate class-wise Land Use/Land Cover area from classified pixels.
⚡ Perform classification accuracy assessment for both Landsat and Sentinel-2 results.
⚡ Understand how pretrained models can reduce the need to build and train a Deep Learning model from scratch.
⚡ Apply the complete pretrained Deep Learning + ArcGIS Pro workflow to research, thesis, dissertation, and professional GIS projects.
Requirements
❗ Basic knowledge of Remote Sensing and GIS is helpful but not mandatory.
❗ Basic familiarity with ArcGIS Pro will make the course easier to follow.
❗ Must have ArcGIS Pro Installed
❗ A dedicated GPU/graphics processor is recommended for faster Deep Learning processing
❗ Basic understanding of satellite imagery such as Landsat and Sentinel-2 is useful
❗ No prior experience in Deep Learning model development or programming is required.
❗ No need to train a Deep Learning model from scratch; the course focuses on using pretrained model
❗ Students should be comfortable with basic computer tasks such as downloading files, managing folders, and extracting compressed data
Description
LULC Using Pretrained Deep Learning Models in ArcGIS Pro
Learn how to performLand Use Land Cover (LULC) classification using pretrained Deep Learning and AI models in ArcGIS Pro with real satellite imagery fromLandsat and Sentinel-2.
This course provides a practical, step-by-step workflow for researchers, GIS professionals, students, and remote sensing users who want to useArtificial Intelligence and Deep Learning for satellite image classification without developing or training a deep learning model from scratch.
Instead of following only traditional supervised classification methods, you will learn howpretrained AI models can be applied to satellite imagery for faster Land Use/Land Cover mapping. The course covers the complete workflow—from understanding data and hardware requirements to downloading satellite imagery, preparing datasets, running pretrained models, correcting classification errors, calculating land-use area, and assessing classification accuracy.
What You Will Learn
You will learn how to
✨ Understand howAI-based LULC classification differs from traditional image classification
✨ Select suitable satellite imagery for deep learning classification
✨ Work withLandsat and Sentinel-2 satellite data
✨ Understand image, software, and hardware requirements for AI classification
✨ Configure your computer for better deep learning processing performance
✨ Identify the graphics processor/GPU available on your system
✨ Download and organize required satellite data andpretrained Deep Learning models
✨ Install and prepare supportingArcGIS Pro Deep Learning libraries
✨ Set up a structured ArcGIS Pro project for Land Use/Land Cover analysis
✨ Check and manage satellite image projections
✨ Prepare Landsat imagery for AI-based classification
✨ PerformLandsat LULC classification using a pretrained AI model
✨ Identify classification errors and misclassified pixels
✨ Correct and improve the classified Land Use/Land Cover map
✨ Merge land-cover classes according to project requirements
✨ CalculateLand Use/Land Cover area from classified pixels
✨ Download and preparehigh-resolution Sentinel-2 imagery
✨ PerformSentinel-2 classification using a pretrained Deep Learning AI model
✨ Reorganize and merge Sentinel-2 land-cover classes
✨ Calculate class-wise land-use area from Sentinel-2 classification
✨ Performclassification accuracy assessment in ArcGIS Pro
✨ Apply the same accuracy assessment methodology to both Landsat and Sentinel-2 classifications
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
Landsat Deep Learning Classification
The course includes a complete workflow for working withLandsat satellite imagery. You will learn how to download Landsat images for longer time periods, visualize and prepare the imagery, check projections, and classify the images using apretrained AI model in ArcGIS Pro.
The workflow does not stop after generating the classification. You will also learn how to identify misclassified areas, apply corrections, merge classes based on your research requirements, and calculate the area covered by individual Land Use/Land Cover classes.
Sentinel-2 Deep Learning Classification
You will also perform LULC classification usingSentinel-2 high-resolution satellite imagery.
The course demonstrates how to download and prepare Sentinel-2 imagery, apply apretrained Deep Learning model, organize the resulting land-cover classes, and calculate class-wise areas.
Using both Landsat and Sentinel-2 gives you experience with two of the most widely used Earth observation datasets inRemote Sensing, GIS, environmental research, urban studies, hydrology, agriculture, and land-change analysis.
Classification Accuracy Assessment
Producing a classified map is only part of a scientific LULC study.
The course therefore includes a dedicated section onclassification accuracy assessment in ArcGIS Pro. You will learn a practical methodology that can be applied to classifications generated from both Landsat and Sentinel-2 imagery.
This makes the workflow useful not only for creating maps but also forresearch projects, dissertations, theses, journal papers, environmental studies, and professional GIS projects.
Why Pretrained Deep Learning Models?
Training a deep learning model from the beginning may require large labelled datasets, extensive computational resources, programming knowledge, and considerable processing time.
Pretrained Deep Learning models provide an alternative approach by allowing existing AI models to be applied to suitable satellite imagery.
In this course, the emphasis is on thepractical application of pretrained models in ArcGIS Pro so that you can understand the complete satellite-image-to-LULC-map workflow without first becoming a deep learning programmer.
Who Is This Course For?
This course is suitable for
✨ GIS and Remote Sensing students
✨ PhD and Master's researchers
✨ Geography and Environmental Science students
✨ Civil and Environmental Engineering researchers
✨ Hydrology and Watershed researchers
✨ Urban and Regional Planning professionals
✨ Agriculture and Natural Resource researchers
✨ ArcGIS and ArcGIS Pro users
✨ Professionals working with Landsat or Sentinel-2 imagery
✨ Researchers interested inAI, Deep Learning, Remote Sensing, and LULC classification
Basic familiarity with GIS or Remote Sensing will be useful, but the course explains the complete classification workflow step by step.
Satellite Data Used
The practical exercises include
Landsat Satellite Imagery
Learn the complete workflow for preparing and classifying Landsat images using pretrained AI models.
Sentinel-2 Satellite Imagery
Learn how to use higher-resolution Sentinel-2 imagery for Deep Learning-based Land Use/Land Cover classification.
Software
The course workflow is primarily implemented usingArcGIS Pro together with the requiredDeep Learning libraries and pretrained AI models.
By the end of this course, you will be able to take satellite imagery from its initial preparation stage throughAI-based Land Use/Land Cover classification, post-classification correction, class merging, area estimation, and accuracy assessment.
If you want to explore howPretrained Deep Learning Models, Artificial Intelligence, ArcGIS Pro, Landsat, and Sentinel-2 can be combined for practical Land Use/Land Cover mapping, this course provides a complete applied workflow.
Who this course is for
⭐ GIS and Remote Sensing students who want to learn practical AI-based Land Use/Land Cover classification.
⭐ Master’s and PhD researchers working with satellite imagery, LULC mapping, environmental studies, hydrology, agriculture, or urban analysis.
⭐ ArcGIS Pro users who want to use pretrained Deep Learning models for image classification.
⭐ Researchers who want to apply Deep Learning without training a model from scratch.
⭐ Professionals working with Landsat and Sentinel-2 satellite imagery.
⭐ Geography, Environmental Science, Civil Engineering, Agriculture, Forestry, and Natural Resource students or professionals
⭐ Researchers conducting thesis, dissertation, journal paper, or project-based LULC analysis.
⭐ GIS analysts who want to learn the complete workflow from image preparation to classification, correction, area calculation, and accuracy assessment.
⭐ Students interested in combining Artificial Intelligence, Deep Learning, Remote Sensing, and GIS.
⭐ Anyone who wants a practical introduction to pretrained Deep Learning models in ArcGIS Pro for Land Use/Land Cover mapping.
Published 9/2026
Created by Lakhwinder Singh
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Expert | Genre: eLearning | Language: English | Duration: 28 Lectures ( 3h 7m ) | Size: 3.45 GB
Download
🔒 Go Premium to Unlock