Have a question?
Message sent Close
4.68 out of 5
4.68
104 reviews on Udemy

Future Land Use with GIS – TerrSet – CA Markov – ArcGIS

CA Markov Model Machine Learning Approach. ArcGIS Erdas QGIS used for data Preparation and TerrSet for Prediction GIS
Instructor:
Lakhwinder Singh
402 students enrolled
English [Auto]
You will be able to
Predict future expansion of urban area and generate future map.
Understand Advance concept of GIS and hands on
Advance concept In ArcGIS and Terrset Software
Understand Working with DEM
Running Advance Queries in GIS
Handling complex data of GIS
CA Markov Model
See Machine Learning in Action
Validation of Generated Results
Other Related task to GIS like UTM Zone, Mosaic of Digital Elevation Model

In this course you will see Machine learning in Action using readymade land Change model Terrset (formerly IDRISI ) . This course used Terrset Software with CA Markov method to predict future landuse ArcGIS is used to prepare data. Erdas also used for some task. No coding is used .All software used in this course are NOT Open Source. You need to manage software. You must know to prepare landuse maps rest of things covered in this course from scratch. Future prediction of landuse depends on number of drivers/Parameters. Drives means forces which decide how the future urban area will look. It includes many drives like, old city boundary because new settlement will be constructed near to old city boundary. Roads and relief are also one of factors, because first roads near city covered by settlement. On another side how, much possibility at different location on agriculture site that can be convert to urban. Similarly, forest cover also. We also need to avoid some landuse classed like water, river, lake or reservoir never convert to urban. So, we need to setup our model in such a way so that it avoids water. After setting accuracy of learning and output accuracy also matters. We also need to modify it. In this course we have achieved learning accuracy of 42%, and 67% in two different runs. But 89% accuracy we have achieved in predicted landuse. Learning and prediction accuracy is different on computer to computer and data to data. While running you will receive more or less accuracy then this course. But focus on your output results. If Learning accuracy was 100% then it also wrong. So, see and understand each video carefully. Then run you model. You must see free preview video before enrolling this course. Because this is Expert level course.

Note: Who having IDRISI Taiga They can also follow same steps.

This course covers 90% Practical and 10% Theory.

Don’t hesitate to ask me Questions in QA Session.

Introduction

1
What will be output after this course
2
Pre-course requirements
3
Introduction
4
Software Requirement

About Software and Methodology

1
TerrSet download
2
Methodology
3
Data UTM Intro
4
Understanding landuse value order
5
Landuse that we already Have – A look

Preparing Landuse related Drivers

1
Getting Ready Our Landuse for future Input
2
Urban Landuse Setting up for Model
3
Disturbances Urban

Roads Process

1
Downloading for Roads
2
Downloading QGIS
3
Street Map Conversion
4
Cut Vector layer to study area
5
Road Separation from other line features in Data using Query
6
Road distance

Downloading DEM and Prepare data of Landuse and Slope for Model input

1
Downloading Dem
2
Prepare Elevation Model for use with Prediction model
3
Process landuse with Erdas to be ready for model
4
Process Landuse in ArcGIS (Optional)
5
Slope Just A simple work

Data Management

1
Arrange Data for Batch Processing

Zero and one Road Layer generation (Optional)

1
Adding optional road layer to main data

Project Setup and Starting Land change Model in Terrset, Data conversion

1
Project setup in Terrset
2
Tiff File conversion for Model
3
Setting up Land Change Modeler and Image modification
4
Estimating Spatial Trend Change probabilities for Landuse

Transition sub model and parameter Power Test on landuse

1
Setting up and understand transition sub model for Land change
2
Testing power of Drivers and Sub Model setup

Machine Learning in Action – CA Markov Chain Model, Future Predictions

1
Running the Machine Learning and MLP Model
2
Generating future Image with Markov Chain Model

Validation of Results

1
Validation Method 1 Terrset
2
Validation Method 2 ArcGIS

After Validation Generate Future images and understand concept of Matrix

1
Future Landuse image Generation for year 2030,2050,2100
2
Modification of Future image with Matrix probability
3
Generation video of Urban Growth and Intermediate stage images till 2100 year
4
Understanding Model Settings what to change and run Model again, understanding
5
Resuming work after save

What if run with different landuse and different settings an Extra Run Results

1
Impact on Learning of land use class Full Model results. Extra with more classes

Additional GIS Task you may need it to prepare data

1
Convert whole Terrset Project files for ArcGIS
2
Method 1 for mosaic of DEM in ArcGIS
3
Method 2 mosaic of DEM in ArcGIS
4
Finding correct UTM inside ArcGIS and Reprojection to UTM
5
Last video

Download Data

1
Data Download
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
4.7
4.7 out of 5
104 Ratings

Detailed Rating

Stars 5
45
Stars 4
38
Stars 3
18
Stars 2
2
Stars 1
1
fa5d98327e3945b91f8832b891767f75

Includes

4 hours on-demand video
1 article
Certificate of Completion

About

AulaGEO is a Ge-engineering specialized academy.

Social Network

Udemy Black Friday Sale! Top Courses From $9.99