| 1. Data used for analysis Landsat images.mp4 | 32.9 MB | ||
| 1. Data used for analysis Landsat images.srt | 6 KB | ||
| 1. Data used for analysis Sentinel images.mp4 | 37.5 MB | ||
| 1. Data used for analysis Sentinel images.srt | 7.5 KB | ||
| 1. Image Classification in R with Random Forest in R.mp4 | 93.7 MB | ||
| 1. Image Classification in R with Random Forest in R.srt | 17.5 KB | ||
| 1. Introduction to Machine Learning.mp4 | 93.5 MB | ||
| 1. Introduction to Machine Learning.srt | 18.7 KB | ||
| 1. Introduction to digital image.mp4 | 58.4 MB | ||
| 1. Introduction to digital image.srt | 13.5 KB | ||
| 1. Introduction.mp4 | 36.3 MB | ||
| 1. Introduction.srt | 6.2 KB | ||
| 1. Lab Introduction to RStudio Interface.mp4 | 47.7 MB | ||
| 1. Lab Introduction to RStudio Interface.srt | 9.8 KB | ||
| 1.1 04_Classification.R | 5.3 KB | ||
| 1.2 RF_classification.tif | 240.3 KB | ||
| 10. Functions in R - overview.mp4 | 32.2 MB | ||
| 10. Functions in R - overview.srt | 5 KB | ||
| 11. For Loops in R.mp4 | 24.8 MB | ||
| 11. For Loops in R.srt | 4.3 KB | ||
| 12. Read Data into R.mp4 | 31.9 MB | ||
| 12. Read Data into R.srt | 5.1 KB | ||
| 2. Basics of machine learning for classification analysis.mp4 | 71.5 MB | ||
| 2. Basics of machine learning for classification analysis.srt | 11.7 KB | ||
| 2. Image Classification in R with Support Vector Machines (SVM) in R.mp4 | 74.8 MB | ||
| 2. Image Classification in R with Support Vector Machines (SVM) in R.srt | 15.6 KB | ||
| 2. Lab Installing Packages and Package Management in R.mp4 | 24.1 MB | ||
| 2. Lab Installing Packages and Package Management in R.srt | 4.7 KB | ||
| 2. Preprocessing of satellite image data.mp4 | 29.2 MB | ||
| 2. Preprocessing of satellite image data.srt | 5.6 KB | ||
| 2. Sensors and Platforms.mp4 | 18.3 MB | ||
| 2. Sensors and Platforms.srt | 5.9 KB | ||
| 2. Training data requirements for classification and training data selection.mp4 | 35.1 MB | ||
| 2. Training data requirements for classification and training data selection.srt | 9 KB | ||
| 2. What is R and RStudio.mp4 | 12.2 MB | ||
| 2. What is R and RStudio.srt | 3.1 KB | ||
| 2.1 01_02_01Sampling_QGIS.pdf | 1.3 MB | ||
| 2.1 R Crash Course I_udemy_script.R | 12.9 KB | ||
| 2.1 SVM_classification.tif | 243.1 KB | ||
| 3. Accuracy assessment of image classification.mp4 | 51 MB | ||
| 3. Accuracy assessment of image classification.srt | 13.7 KB | ||
| 3. Common algorithms of image classification.mp4 | 112.9 MB | ||
| 3. Common algorithms of image classification.srt | 22.3 KB | ||
| 3. How to install R and RStudio in 2021.mp4 | 16.7 MB | ||
| 3. How to install R and RStudio in 2021.srt | 4.3 KB | ||
| 3. Lab Prepare training data in R - part 1.mp4 | 60.4 MB | ||
| 3. Lab Prepare training data in R - part 1.srt | 8.5 KB | ||
| 3. Overview of processing steps in R for Landsat images.mp4 | 14.9 MB | ||
| 3. Overview of processing steps in R for Landsat images.srt | 3 KB | ||
| 3. Understanding Remote Sensing for LULC mapping.mp4 | 63.3 MB | ||
| 3. Understanding Remote Sensing for LULC mapping.srt | 8.9 KB | ||
| 3. Variables in R and assigning Variables in R.mp4 | 9 MB | ||
| 3. Variables in R and assigning Variables in R.srt | 2.7 KB | ||
| 3.1 1_Load_Layerstack.R | 2.7 KB | ||
| 3.2 LS8_Bonn_SC20200925100850.tif | 16 MB | ||
| 4. Lab Accuracy Assessment (validation) of classification in R.mp4 | 110.4 MB | ||
| 4. Lab Accuracy Assessment (validation) of classification in R.srt | 12.2 KB | ||
| 4. Lab Image load in R.mp4 | 49.8 MB | ||
| 4. Lab Image load in R.srt | 7.9 KB | ||
| 4. Lab Install R and RStudio in 2021.mp4 | 38.7 MB | ||
| 4. Lab Install R and RStudio in 2021.srt | 6.5 KB | ||
| 4. Lab Prepare training data in R - part 2.mp4 | 121.5 MB | ||
| 4. Lab Prepare training data in R - part 2.srt | 9.9 KB | ||
| 4. Lab Variables in R and assigning Variables in R.mp4 | 7.6 MB | ||
| 4. Lab Variables in R and assigning Variables in R.srt | 1.8 KB | ||
| 4. Stages of LULC supervised classification.mp4 | 72.8 MB | ||
| 4. Stages of LULC supervised classification.srt | 14.9 KB | ||
| 5. Independent Task Accuracy assessment for SVM-based classification.mp4 | 5.6 MB | ||
| 5. Independent Task Accuracy assessment for SVM-based classification.srt | 1.4 KB | ||
| 5. Lab Image Layerstacks in R.mp4 | 95.7 MB | ||
| 5. Lab Image Layerstacks in R.srt | 9.6 KB | ||
| 5. Lab Installing QGIS and install SCP.mp4 | 86.7 MB | ||
| 5. Lab Installing QGIS and install SCP.srt | 14.5 KB | ||
| 5. Overview of data types and data structures in R.mp4 | 27.2 MB | ||
| 5. Overview of data types and data structures in R.srt | 8.5 KB | ||
| 5. Plotting spectral signatures in R.mp4 | 24.7 MB | ||
| 5. Plotting spectral signatures in R.srt | 6 KB | ||
| 6. Lab Batch Processing in R unzipp, laerstack of LAndsat images.mp4 | 57.2 MB | ||
| 6. Lab Batch Processing in R unzipp, laerstack of LAndsat images.srt | 5.4 KB | ||
| 6. Lab data types and data structures in R.mp4 | 48.1 MB | ||
| 6. Lab data types and data structures in R.srt | 9.3 KB | ||
| 6.1 2_batch_Processing.R | 1.3 KB | ||
| 7. Vectors' operations in R.mp4 | 35.9 MB | ||
| 7. Vectors' operations in R.srt | 7.4 KB | ||
| 7. Visualize images in R.mp4 | 64.4 MB | ||
| 7. Visualize images in R.srt | 8 KB | ||
| 7.1 3_visualize_image.R | 1.2 KB | ||
| 7.2 LC081970242020052101T1-SC20200925100911.tif | 707.4 MB | ||
| 8. Data types and data structures Factors.mp4 | 9.3 MB | ||
| 8. Data types and data structures Factors.srt | 2.8 KB | ||
| 9. Dataframes overview in R.mp4 | 16.7 MB | ||
| 9. Dataframes overview in R.srt | 4.1 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| LC08_L1TP_196025_20200530_20200608_01_T1.xml | 11.1 KB | ||
| LC08_L1TP_196025_20200530_20200608_01_T1_pixel_qa.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200530_20200608_01_T1_radsat_qa.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200530_20200608_01_T1_sr_aerosol.tif | 61.6 MB | ||
| LC08_L1TP_196025_20200530_20200608_01_T1_sr_band1.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200530_20200608_01_T1_sr_band2.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200530_20200608_01_T1_sr_band3.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200530_20200608_01_T1_sr_band4.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200530_20200608_01_T1_sr_band5.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200530_20200608_01_T1_sr_band6.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200530_20200608_01_T1_sr_band7.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200919_20200919_01_RT.xml | 11.1 KB | ||
| LC08_L1TP_196025_20200919_20200919_01_RT_pixel_qa.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200919_20200919_01_RT_radsat_qa.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200919_20200919_01_RT_sr_aerosol.tif | 61.6 MB | ||
| LC08_L1TP_196025_20200919_20200919_01_RT_sr_band1.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200919_20200919_01_RT_sr_band2.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200919_20200919_01_RT_sr_band3.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200919_20200919_01_RT_sr_band4.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200919_20200919_01_RT_sr_band5.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200919_20200919_01_RT_sr_band6.tif | 123.1 MB | ||
| LC08_L1TP_196025_20200919_20200919_01_RT_sr_band7.tif | 123.1 MB | ||
| LC08_L1TP_197024_20200521_20200527_01_T1.xml | 11.1 KB | ||
| LC08_L1TP_197024_20200521_20200527_01_T1_pixel_qa.tif | 125.7 MB | ||
| LC08_L1TP_197024_20200521_20200527_01_T1_radsat_qa.tif | 125.7 MB | ||
| LC08_L1TP_197024_20200521_20200527_01_T1_sr_aerosol.tif | 62.9 MB | ||
| LC08_L1TP_197024_20200521_20200527_01_T1_sr_band1.tif | 125.7 MB | ||
| LC08_L1TP_197024_20200521_20200527_01_T1_sr_band2.tif | 125.7 MB | ||
| LC08_L1TP_197024_20200521_20200527_01_T1_sr_band3.tif | 125.7 MB | ||
| LC08_L1TP_197024_20200521_20200527_01_T1_sr_band4.tif | 125.7 MB | ||
| LC08_L1TP_197024_20200521_20200527_01_T1_sr_band5.tif | 125.7 MB | ||
| LC08_L1TP_197024_20200521_20200527_01_T1_sr_band6.tif | 125.7 MB | ||
| LC08_L1TP_197024_20200521_20200527_01_T1_sr_band7.tif | 125.7 MB | ||
| RF_classification.tif | 240.3 KB | ||
| RF_validation.dbf | 5 KB | ||
| RF_validation.prj | 409.6 B | ||
| RF_validation.shp | 6.9 KB | ||
| RF_validation.shx | 2.1 KB | ||
| SVM_classification.tif | 243.1 KB | ||
| smp.rda | 117.2 KB | ||
| training_data.dbf | 6 KB | ||
| training_data.prj | 409.6 B | ||
| training_data.shp | 10.4 KB | ||
| training_data.shx | 716.8 B | ||
| ▲ 138 total files | |||
Machine Learning in R: Image Classification for LULC mapping
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.89 GB | Duration: 5h 8m
Learn supervised machine learning 4 Remote Sensing R & R-Studio, image classification, land use and land cover mapping
What you'll learn
Learn supervised machine learning for image classification using R-programming language in R-Studio
Learn theoretical background of Machine Learning
Apply machine learning based algorithms (random forest, SVM) for image classification analysis in R and R-Studio
Learn R-programming from scratch: R crash course is included that you could start R-programming for machine learning
Fully understand the basics of Land use and Land Cover (LULC) Mapping based on satellite image classification
Get an introduction and fully understand to Remote Sensing relevant for LULC mapping
Pre-process and analyze Remote Sensing images in R
Learn how to create training and validation data for image classification in QGIS
Build machine learning based image classification models for LUCL analysis and test their robustness in R
Implement Machine Learning algorithms, such as Random Forests, SVM in R
Apply accuracy assessment for Machine Learning based image classification in R
You'll have a copy of the scripts and step-by-step manuals used in the course for your reference to use in your analysis.
Description
Welcome to my unique course on Udemy on Machine Learning in R and R-Studio: Image classification for land use and land cover (LULC) mapping!
This is the first course on Udemy that offers a possibility to learn much-wanted skills of R programming for RS-based Machine Learning analysis in R.
Why geospatial analysts (GIS, Remote Sensing) should learn R?
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 779 MB | freecoursewb | 1 week | 1 | 25 | |
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Udemy - Spark Machine Learning Project (House Sale Price Prediction) Posted by
freecoursewb in Other
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1.7 GB | freecoursewb | 1 month | 9 | 4 |
| 3.4 GB | freecoursewb | 1 month | 19 | 5 | |
| 1.2 GB | freecoursewb | 2 months | 12 | 3 | |
| 1.9 GB | freecoursewb | 2 months | 9 | 1 |
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