| 1. Adding optional road layer to main data-en_US.srt | 8.8 KB | ||
| 1. Adding optional road layer to main data.mp4 | 138.3 MB | ||
| 1. Arrange Data for Batch Processing-en_US.srt | 3.5 KB | ||
| 1. Arrange Data for Batch Processing.mp4 | 68.2 MB | ||
| 1. Convert whole Terrset Project files for ArcGIS-en_US.srt | 2.8 KB | ||
| 1. Convert whole Terrset Project files for ArcGIS.mp4 | 35.2 MB | ||
| 1. Downloading Dem-en_US.srt | 3.5 KB | ||
| 1. Downloading Dem.mp4 | 51.4 MB | ||
| 1. Downloading for Roads-en_US.srt | 4 KB | ||
| 1. Downloading for Roads.mp4 | 74.9 MB | ||
| 1. Future Landuse image Generation for year 2030,2050,2100-en_US.srt | 6.2 KB | ||
| 1. Future Landuse image Generation for year 2030,2050,2100.mp4 | 146.8 MB | ||
| 1. Getting Ready Our Landuse for future Input-en_US.srt | 11.3 KB | ||
| 1. Getting Ready Our Landuse for future Input.mp4 | 209 MB | ||
| 1. Impact on Learning of land use class Full Model results. Extra with more classes-en_US.srt | 5.4 KB | ||
| 1. Impact on Learning of land use class Full Model results. Extra with more classes.mp4 | 63.8 MB | ||
| 1. Project setup in Terrset-en_US.srt | 1.7 KB | ||
| 1. Project setup in Terrset.mp4 | 19.4 MB | ||
| 1. Running the Machine Learning and MLP Model-en_US.srt | 10.7 KB | ||
| 1. Running the Machine Learning and MLP Model.mp4 | 107 MB | ||
| 1. Setting up and understand transition sub model for Land change-en_US.srt | 3.6 KB | ||
| 1. Setting up and understand transition sub model for Land change.mp4 | 35.5 MB | ||
| 1. TerrSet download-en_US.srt | 1.9 KB | ||
| 1. TerrSet download.mp4 | 21.4 MB | ||
| 1. Validation Method 1 Terrset-en_US.srt | 4.3 KB | ||
| 1. Validation Method 1 Terrset.mp4 | 68.1 MB | ||
| 1. What will be output after this course-en_US.srt | 0 B | ||
| 1. What will be output after this course.mp4 | 8.8 MB | ||
| 2. Downloading QGIS-en_US.srt | 1.9 KB | ||
| 2. Downloading QGIS.mp4 | 15.8 MB | ||
| 2. Generating future Image with Markov Chain Model-en_US.srt | 4 KB | ||
| 2. Generating future Image with Markov Chain Model.mp4 | 52.3 MB | ||
| 2. Method 1 for mosaic of DEM in ArcGIS-en_US.srt | 8.9 KB | ||
| 2. Method 1 for mosaic of DEM in ArcGIS.mp4 | 149 MB | ||
| 2. Methodology-en_US.srt | 1 KB | ||
| 2. Methodology.mp4 | 5 MB | ||
| 2. Modification of Future image with Matrix probability-en_US.srt | 7.5 KB | ||
| 2. Modification of Future image with Matrix probability.mp4 | 137.8 MB | ||
| 2. Pre-course requirements-en_US.srt | 819.2 B | ||
| 2. Pre-course requirements.mp4 | 4.3 MB | ||
| 2. Prepare Elevation Model for use with Prediction model-en_US.srt | 7.7 KB | ||
| 2. Prepare Elevation Model for use with Prediction model.mp4 | 138.5 MB | ||
| 2. Testing power of Drivers and Sub Model setup-en_US.srt | 4.5 KB | ||
| 2. Testing power of Drivers and Sub Model setup.mp4 | 51.8 MB | ||
| 2. Tiff File conversion for Model-en_US.srt | 1.5 KB | ||
| 2. Tiff File conversion for Model.mp4 | 14.8 MB | ||
| 2. Urban Landuse Setting up for Model-en_US.srt | 4.6 KB | ||
| 2. Urban Landuse Setting up for Model.mp4 | 85.6 MB | ||
| 2. Validation Method 2 ArcGIS-en_US.srt | 10.7 KB | ||
| 2. Validation Method 2 ArcGIS.mp4 | 152.4 MB | ||
| 3. Data UTM Intro-en_US.srt | 716.8 B | ||
| 3. Data UTM Intro.mp4 | 2 MB | ||
| 3. Disturbances Urban-en_US.srt | 2.7 KB | ||
| 3. Disturbances Urban.mp4 | 40.5 MB | ||
| 3. Generation video of Urban Growth and Intermediate stage images till 2100 year-en_US.srt | 5.6 KB | ||
| 3. Generation video of Urban Growth and Intermediate stage images till 2100 year.mp4 | 115 MB | ||
| 3. Introduction-en_US.srt | 2 KB | ||
| 3. Introduction.mp4 | 15 MB | ||
| 3. Method 2 mosaic of DEM in ArcGIS-en_US.srt | 3.4 KB | ||
| 3. Method 2 mosaic of DEM in ArcGIS.mp4 | 43.4 MB | ||
| 3. Process landuse with Erdas to be ready for model-en_US.srt | 10.6 KB | ||
| 3. Process landuse with Erdas to be ready for model.mp4 | 140.6 MB | ||
| 3. Setting up Land Change Modeler and Image modification-en_US.srt | 6 KB | ||
| 3. Setting up Land Change Modeler and Image modification.mp4 | 64.2 MB | ||
| 3. Street Map Conversion-en_US.srt | 4.8 KB | ||
| 3. Street Map Conversion.mp4 | 81.5 MB | ||
| 4. Cut Vector layer to study area-en_US.srt | 4 KB | ||
| 4. Cut Vector layer to study area.mp4 | 80.8 MB | ||
| 4. Estimating Spatial Trend Change probabilities for Landuse-en_US.srt | 4.6 KB | ||
| 4. Estimating Spatial Trend Change probabilities for Landuse.mp4 | 48.4 MB | ||
| 4. Finding correct UTM inside ArcGIS and Reprojection to UTM-en_US.srt | 3.2 KB | ||
| 4. Finding correct UTM inside ArcGIS and Reprojection to UTM.mp4 | 41.5 MB | ||
| 4. Process Landuse in ArcGIS (Optional)-en_US.srt | 4.4 KB | ||
| 4. Process Landuse in ArcGIS (Optional).mp4 | 81.7 MB | ||
| 4. Software Requirement-en_US.srt | 1 KB | ||
| 4. Software Requirement.mp4 | 3.9 MB | ||
| 4. Understanding Model Settings what to change and run Model again, understanding-en_US.srt | 5.1 KB | ||
| 4. Understanding Model Settings what to change and run Model again, understanding.mp4 | 90 MB | ||
| 4. Understanding landuse value order-en_US.srt | 1.5 KB | ||
| 4. Understanding landuse value order.mp4 | 11.8 MB | ||
| 5. Landuse that we already Have – A look-en_US.srt | 4.5 KB | ||
| 5. Landuse that we already Have – A look.mp4 | 69.8 MB | ||
| 5. Last video-en_US.srt | 2.9 KB | ||
| 5. Last video.mp4 | 10.4 MB | ||
| 5. Resuming work after save-en_US.srt | 1 KB | ||
| 5. Resuming work after save.mp4 | 17.9 MB | ||
| 5. Road Separation from other line features in Data using Query-en_US.srt | 3.6 KB | ||
| 5. Road Separation from other line features in Data using Query.mp4 | 55.1 MB | ||
| 5. Slope Just A simple work-en_US.srt | 1.2 KB | ||
| 5. Slope Just A simple work.mp4 | 21.2 MB | ||
| 6. Road distance-en_US.srt | 4 KB | ||
| 6. Road distance.mp4 | 62.8 MB | ||
| Bonus Resources.txt | 307.2 B | ||
| Data Download.html | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 95 total files | |||
Future Land Use with GIS - TerrSet - CA Markov - ArcGIS
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.88 GB | Duration: 4h 3m
CA Markov Model Machine Learning Approach. ArcGIS Erdas QGIS used for data Preparation and TerrSet for Prediction GIS
What you'll learn
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
You Must know Basic of GIS
Familiar with ArcGIS, ERDAS Just basics
You Must have software Terrset and ArcGIS both are NOT Open Source. You need to manage.
Must know how to prepare land use. This is advanced course. Otherwise first learn Landuse mapping using other course.
You must have two landuse with Good Accuracy
Description
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.
| torrent name | size | uploader | age | seed | leech |
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| 483.5 MB | freecoursewb | 1 week | 15 | 3 | |
| 426.3 MB | freecoursewb | 1 week | 33 | 10 | |
| 2.2 GB | freecoursewb | 2 months | 13 | 1 | |
| 267.9 MB | freecoursewb | 2 months | 0 | 0 | |
| 3.7 GB | freecoursewb | 2 months | 0 | 0 |
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