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.

Other Courses for Advanced GIS Analysis

Click the course name below and the available discount will be applied automatically.

Climate Change Trend Analysis Mann Kendall & Sen’s Slope → Research Paper Writing with AI: Data to Paper (Ethically) → Landuse Landcover using ArcGIS Machine Learning Tools → Crop Yield Estimation using Remote Sensing and GIS ArcGIS → SWAT CUP Calibration Validation and Write Values to ArcSWAT → Complete Remote Sensing and GIS - ArcGIS – ERDAS → Groundwater Potential Zones GIS - Complete Project ArcGIS → Land Use Land Cover Classification GIS, ERDAS, ArcGIS, ML → Future Land Use with GIS - TerrSet - CA Markov – ArcGIS → ArcSWAT Model with ArcGIS - Run for Any Study Area – GIS →

A Practical Workflow for Working with IMD Gridded Climate Data

For many climate and hydrological research projects in India, obtaining the climate data is only the beginning. A typical study may require researchers to download meteorological datasets, identify data for specific locations, prepare time series, calculate descriptive statistics, and finally investigate long-term trends.

When this process has to be repeated for many locations, the data-preparation stage can become a significant part of the research work.

IMD Gridded Data Analyzer was developed to simplify this workflow by bringing several of these tasks into a single research-oriented application.

From Raw Climate Data to Research Results

A common climate-data workflow can be represented as:

IMD Data → Location Extraction → Data Preparation → Statistics → Trend Analysis → Research Interpretation

The software follows this general sequence through three major stages:

1. Download IMD data
2. Extract climate data
3. Perform analysis

This integrated approach is intended to reduce repetitive data handling and allow researchers to move more quickly from the original datasets toward usable analytical results.

Getting IMD Data

The first stage allows users to obtain the required IMD meteorological datasets.

The application supports:

• Precipitation/rainfall
• Minimum temperature
• Maximum temperature
• Real-time daily IMD data

Users can choose to download the available archive for selected variables or work with selected data where appropriate.

For researchers conducting several analyses, downloading the available archive can be useful because the same source data can subsequently be used for different locations and study periods without repeatedly obtaining the original datasets.

Extract Only the Locations You Need

Climate research rarely requires the entire gridded dataset for every analysis.

For example, a researcher might want to study rainfall and temperature at:

• A particular city
• A weather station
• A watershed
• A district
• Several research sites

IMD Gridded Data Analyzer provides different extraction approaches depending on the research requirement.

Single Location

A user can provide a location name together with latitude and longitude and extract the required climate variables for that location.

Regional Extraction

For studies involving a geographic region, the software can work with a rectangular region instead of requiring every location to be processed separately.

This is particularly relevant for regional studies and hydrological applications.

Multiple Locations

For a larger study, locations can be prepared in a CSV or Excel-based workflow.

Instead of entering dozens of locations manually, researchers can prepare a location list containing the required coordinates and process the locations together.

This can be especially useful for research involving multiple cities, districts, stations, sampling sites, or climate-assessment locations.

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Statistical Analysis Without Repeating Manual Calculations

Once climate data have been extracted, researchers commonly need to calculate descriptive statistics before examining trends.

The analysis module can generate an Excel workbook containing monthly statistical information.

Depending on the dataset, the output includes:

• Number of observations
• Mean
• Median
• Standard deviation
• Minimum
• Maximum
• Sum

The statistics are organized by climate variable and calendar month, providing a convenient way to examine the monthly characteristics of rainfall and temperature data.

This also provides a structured starting point for subsequent reporting and interpretation.

Investigating Climate Trends

For long-term climate studies, researchers may want to determine whether a variable has exhibited a statistically identifiable trend.

The software incorporates Mann–Kendall trend analysis into the workflow.

The resulting analysis includes:

• Sample size (N)
• Trend
• Z-statistic
• P-value
• Sen's Slope
• Intercept

The resulting trend can be classified as increasing, decreasing, or no trend. The analysis can also consider individual calendar months rather than only treating the complete dataset as one time series.

Why Sen's Slope Is Included

Identifying a trend is only part of a climate-change analysis. Researchers may also want an estimate of the magnitude and direction of that change.

For this purpose, the software provides Sen's Slope alongside the Mann–Kendall results.

The sign and magnitude of the slope can be examined to understand the estimated direction and rate of change in the analyzed variable.

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An Approach for Multi-Location Research

One of the more time-consuming situations occurs when the same analysis needs to be performed for many locations.

Consider a study comparing climate trends across several cities or research locations. Without an automated workflow, the researcher may have to repeat the same extraction and analysis procedure for every location.

The software instead allows locations to be prepared as a group and processed systematically.

A typical workflow can therefore be:

Location List → Data Extraction → Statistical Analysis → Trend Results → Location Comparison

This approach is intended for applications such as regional climate variability, rainfall trends, temperature trends, climate-change assessment, and multi-city comparisons.

Useful for Hydrological and SWAT Studies

The software is not limited to statistical climate analysis.

For regional extraction, it can also prepare SWAT-compatible meteorological files and associated elevation information.

This provides an additional workflow for researchers who use meteorological data as part of hydrological modeling and SWAT-based studies.

What Does the Analysis Workbook Contain?

The generated workbook provides a structured record of the analysis.

It can contain separate sheets for:

Monthly Statistics

Descriptive statistics for the analyzed climate variables.

Mann-Kendall Trend

Trend direction, Z-statistics, P-values, Sen's Slope, and intercept.

Monthly Source

The source monthly observations used in the analysis, including date, location, coordinates, rainfall, Tmin, and Tmax.

Keeping the source observations alongside the calculated results can make it easier to trace the analytical results back to the underlying dataset.

Who Might Find It Useful?

IMD Gridded Data Analyzer is intended for research workflows involving areas such as:

Climate Research
Long-term rainfall and temperature analysis.

Climate-Change Studies
Trend assessment across individual or multiple locations.

Hydrology
Climate-data preparation for hydrological studies.

GIS and Environmental Research
Extraction and analysis of geographically defined locations and regions.

SWAT Modeling
Preparation of meteorological inputs for regional modeling workflows.

Academic Research
Thesis, dissertation and other research projects involving IMD climate datasets.

The software's stated objective is to reduce repetitive data preparation and allow researchers to devote more effort to interpretation and scientific analysis.

Getting Started

The latest version of IMD Gridded Data Analyzer v1.0.3 is available through the official GitHub release page.

📥 Download the software:
IMD Gridded Data Analyzer — GitHub Releases

📖 User Manual:
IMD Gridded Data Analyzer v1.0.3 User Manual

🎥 Video Tutorial:
IMD Gridded Data Analyzer — YouTube Tutorial

Conclusion

Climate-data research often involves a considerable amount of preparation before statistical analysis can begin. Downloading datasets, extracting locations, organizing observations and repeating calculations for multiple sites can consume valuable research time.

IMD Gridded Data Analyzer approaches this problem by combining these activities into an integrated workflow.

For researchers working with IMD climate data, the application provides a way to move from data acquisition and location extraction to statistical and trend analysis within the same research workflow.

Download → Extract → Analyze → Interpret

The software is available for researchers and students who want to explore a more streamlined approach to working with IMD gridded climate data.

Research Paper Writing with AI

We all are experts and can do any GIS analysis easily. But the problem starts when publishing a paper or writing a thesis, and the work of review of literature starts. We download hundreds of papers, but only a few of them are related to our research. To write a single review, we need to read the entire paper, sum up the major findings and methods, and then write a review. This wastes a lot of our time. Even when we have written the entire paper, sometimes it is rejected due to poor English or badly formatted writing. All of this is solved in this course using AI. #GIS #Research

https://www.udemy.com/course/research-paper-writing-with-ai-data-to-paper-ethically/?referralCode=A383EA12DB584E2B0BD3

👉 Research Paper Writing with AI: Data to Paper (Ethically)
🔗 Enroll here with special promo access

 

Direct link: https://www.udemy.com/course/research-paper-writing-with-ai-data-to-paper-ethically/?referralCode=A383EA12DB584E2B0BD3

I know how frustrating paper writing can be. Many researchers face the same problems:

• ❌ Papers rejected because of weak writing or structure.
• ❌ The tedious task of literature review that consumes weeks.
• ❌ Poor English or lack of flow making good research look weak.
• ❌ Hours wasted on formatting references and citations.

This course is designed to solve exactly these problems. Using your own research data, you’ll learn how to:
✅ Write a clear and professional paper with AI support.
✅ Conduct a time-saving, ethical literature review.
✅ Improve your English writing style and clarity.
✅ Manage references easily with RIS files for tools like Mendeley.
✅ Prepare abstracts, conclusions, and even cover letters that impress journals.

The best part? This course emphasizes ethical use of AI — you stay in control, ensuring your work is original and credible, while saving weeks of effort.

🎓 If you’re tired of the endless struggle with writing, this is your solution to turn your data into a complete, journal-ready paper.

👉 Click here to join now and start writing smarter, not harder.

Complete Guide to GIS Spectral Indices: NDVI, NDWI, NDBI & More

Index	Formula (generic bands)	Typical usage
NDVI	(NIR − RED) / (NIR + RED)	Vegetation vigor/greenness
EVI	2.5 × (NIR − RED) / (NIR + 6×RED − 7.5×BLUE + 1)	Vegetation (reduces soil/atmospheric effects)
SAVI	((NIR − RED) / (NIR + RED + L)) × (1 + L), L ≈ 0.5	Vegetation in sparse cover/soil backgrounds
MSAVI	(2×NIR + 1 − √((2×NIR + 1)² − 8×(NIR − RED))) / 2	Vegetation with minimal soil influence
GNDVI	(NIR − GREEN) / (NIR + GREEN)	Chlorophyll/vegetation stress
NDRE	(NIR − RE) / (NIR + RE)	Canopy chlorophyll (uses Red-Edge)
ARVI	(NIR − (2×RED − BLUE)) / (NIR + (2×RED − BLUE))	Vegetation with atmospheric correction
VARI	(GREEN − RED) / (GREEN + RED − BLUE)	Vegetation in RGB (low-cost imagery)
NDMI (a.k.a. NDWI-Gao)	(NIR − SWIR1) / (NIR + SWIR1)	Canopy moisture/forest drought
NDWI (McFeeters)	(GREEN − NIR) / (GREEN + NIR)	Open water extraction (may confuse built-up)
MNDWI (Xu)	(GREEN − SWIR1) / (GREEN + SWIR1)	Water extraction (better in urban)
NDBI	(SWIR1 − NIR) / (SWIR1 + NIR)	Built-up detection
BSI	((SWIR1 + RED) − (NIR + BLUE)) / ((SWIR1 + RED) + (NIR + BLUE))	Bare soil identification
NDSI (Snow)	(GREEN − SWIR1) / (GREEN + SWIR1)	Snow mapping
NBR	(NIR − SWIR2) / (NIR + SWIR2)	Burn severity/disturbance
NBR2	(SWIR1 − SWIR2) / (SWIR1 + SWIR2)	Moisture/thermal disturbance in dry regions
EBBI	(SWIR1 − NIR) / √(SWIR1 + TIR)	Built-up (requires thermal)
SIPI	(NIR − BLUE) / (NIR − RED)	Pigment stress (carotenoid vs. chlorophyll)
PRI	(GREEN531 − GREEN570) / (GREEN531 + GREEN570)	Photosynthetic efficiency (narrow bands)
GEMI	η(1 − 0.25η) − (RED − 0.125)/(1 − RED), where η = 2((NIR² − RED²) + 1.5NIR + 0.5RED)/(NIR + RED + 0.5)	Atmospheric-resilient greenness

Band keys (generic): BLUE (≈0.45–0.50 μm), GREEN (≈0.53–0.59 μm), RED (≈0.64–0.68 μm), RE = Red-Edge (≈0.70–0.74 μm), NIR (≈0.84–0.88 μm), SWIR₁ (≈1.57–1.65 μm), SWIR₂ (≈2.11–2.29 μm), TIR = Thermal (10–12 μm). For SAVI, a common soil factor is L = 0.5.

Band Numbers

Generic Name	Sentinel-2 Band	Landsat-8/9 Band	Wavelength (µm)
BLUE	B2	B2	0.45 – 0.50
GREEN	B3	B3	0.53 – 0.59
RED	B4	B4	0.64 – 0.68
Red-Edge (RE)	B5, B6, B7	–	0.70 – 0.74
NIR	B8	B5	0.84 – 0.88
SWIR1	B11	B6	1.57 – 1.65
SWIR2	B12	B7	2.11 – 2.29
TIR (Thermal)	–	B10, B11	10 – 12

Landuse landcover with machine learning using ArcGIS only

Creating land use maps is a complex process often plagued by errors and low accuracy rates. For instance, barren land or riverbeds may be misclassified as urban areas. To address these errors, it typically requires the use of multiple software tools, as a single software package may not be sufficient for all processes, such as post-classification pixel corrections. Often, other software is used alongside ArcGIS to correct these misclassifications because ArcGIS lacks a dedicated tool for pixel correction.

However, in this course, we have successfully managed to perform all tasks using ArcGIS alone. Land use classification is accomplished using machine learning techniques, specifically the Support Vector Machine (SVM) and Random Forest methods, through supervised training. Even post-classification pixel correction is handled within ArcGIS using specific strategies.

If you prefer to rely solely on ArcGIS without using any additional software, this course is designed for you. The first few videos are free. Here is the link.

Link to course: https://www.udemy.com/course/landuse-landcover-with-machine-learning-using-arcgis-only/?couponCode=AILANDUSE

Things that covered

Land use mapping

Land cover classification

ArcGIS machine learning

SVM land use classification

Random Forest land use mapping

Post-classification pixel correction

ArcGIS pixel correction

Supervised training ArcGIS

Land use errors correction

Urban area misclassification corrections

ArcGIS land use course

Barren land classification corrections 

Riverbed misclassification corrections

Single software land use mapping

ArcGIS only land use mapping

High accuracy land cover mapping

Machine learning in ArcGIS

Land use mapping techniques

Land cover classification errors corrections 

Crop yield estimation using remote sensing and GIS Tutorial

Crop yield estimation is a crucial aspect of agricultural management and planning. Accurate and timely yield estimates can help farmers make informed decisions about planting, fertilization, irrigation, and harvest timing. Remote sensing and geographic information systems (GIS) are powerful tools that can be used to estimate crop yields with a high degree of accuracy. One of the most commonly used indices in remote sensing for crop yield estimation is the normalized difference vegetation index (NDVI).  we will explore the use of NDVI in conjunction with remote sensing and GIS to estimate crop yields.
Remote sensing is the process of collecting and analyzing data about the earth's surface using sensors mounted on aircraft or satellites. These sensors can detect and measure various characteristics of the earth's surface, such as temperature, humidity, and vegetation cover. NDVI is a commonly used index in remote sensing that measures the amount of vegetation cover in an area. NDVI is calculated by taking the difference between the near-infrared and red bands of a multispectral image and dividing that difference by the sum of the near-infrared and red bands. NDVI values range from -1 to 1, with higher values indicating more vegetation cover.
GIS is a collection of software and data that can be used to analyze, visualize, and manage geographic data. GIS can be used to analyze remote sensing data, such as NDVI images, to estimate crop yields.By developing a regression model on NDVI and crop yield. GIS can also be used to create maps that show crop yields across an entire region, making it easier to identify areas that may need additional resources or attention.
ArcGIS is a popular GIS software that can be used to analyze remote sensing data and estimate crop yields. It can be used to process NDVI images and create maps that show crop yields across an entire region. The software also has a wide range of tools that can be used to analyze and manipulate spatial data, making it an ideal tool for crop yield estimation.
Model development required a number of tools and logic to use together.  

Detailed step-by-step tutorial is available on this link

Click here to access tutorial 

Tutorial Highlights :

1. Use Machine learning method for crop classification in ArcGIS, separate crops from natural vegetation

2. The model was developed using the minimum observed data available online

3. Crop NDVI separation

4. Crop Yield model development

5. Crop production calculation from GIS model data

6. Identify the low and high-yield zones and area calculation

7. Calculate the total production of the region

8. Validation of developed model on another study area

9. Validate production and yield of other areas using a developed model of another area

10. Convert the model to the ArcGIS toolbox

GIS Udemy Tutorial Coupon codes 2023

ArcSWAT: Covers hydrological simulations and weather data preparation using R; use it for any research area, and scripts are available for download.

Link: https://www.udemy.com/course/running-arcswat-model-with-arcgis-for-any-study-area-gis/?couponCode=HAPPYNEWYEAR2023

SWAT CUP: The simulated flow may not always completely match the observed flow. In this scenario, we must establish hydrological parameters. SWATCUP handles it automatically and recommends the best match settings.

Link: https://www.udemy.com/course/swat-cup-calibration-validation-and-write-values-to-arcswat/?couponCode=HAPPYNEWYEAR2023

 

GROUNDWATER: This course covers the use of GIS for groundwater investigation using theoretical parameters. Create a live project from scratch.

Link: https://www.udemy.com/course/groundwater-potential-zones-using-gis-full-project-arcgis-tutorial/?couponCode=HAPPYNEWYEAR2023

 

LANDUSE LANDCOVER: Covers landuse classification of high-resolution data. You will learn how to correct error pixels such as urban and barren land, or agriculture and natural vegetation. It covers most classification methods, such as machine learning, supervised, unsupervised, and pixel-level recoding of challenging images.

Link: https://www.udemy.com/course/land-use-land-cover-classification-gis-erdas-arcgis-envi/?couponCode=HAPPYNEWYEAR2023

 

FUTURE LANDUSE: Remote sensing has impressive capabilities to generate future landuse classified image of  2090, even if it is not captured in the present; see machine learning in action. No coding is required.

Link: https://www.udemy.com/course/prediction-of-future-land-use-remote-sensing-and-gis-terrset/?couponCode=HAPPYNEWYEAR2023

 

BASIC GIS: This course covers more than a university lab class if you are new to GIS. This course will teach you about 3D, data analysis, and satellite image processing. 11hrs of hands-on videos of practicals.

Link: https://www.udemy.com/course/complete-basic-gis-tasks-arcgis-erdas-remote-sensing/?couponCode=HAPPYNEWYEAR2023

 

List of Best GIS Courses

How to learn GIS at home, then these courses are for you. 

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Course Name: ArcSWAT Model with ArcGIS - Run for any Study Area - GIS

Duration: 4hr 26min

Highlights: Watershed simulation, amount of sediment reach at the outlet, ET, PET, Water Yield, Groundwater Recharge estimates, Streamflow in m3/sec, Hending weather data, Cover zero to simulation graphs. Applicable for ArcGIS 10.1 to ArcGIS 10.7

Link:https://www.udemy.com/course/running-arcswat-model-with-arcgis-for-any-study-area-gis/?couponCode=LABATHOME

Course Name: SWAT CUP Calibration Validation and write values to ArcSWAT

Duration: 2hr 37min

Highlights: Multiple concepts of calibration and validation, Write calibrated parameter values to ArcSWAT.

Link: https://www.udemy.com/course/swat-cup-calibration-validation-and-write-values-to-arcswat/?couponCode=LABATHOME

Course Name: Land use Land cover classification GIS, ERDAS, ArcGIS, ENVI + Machine Learning in GIS

Duration: 6hr 22min

Highlights: A number of classification methods are covered including Machine learning. From data downloading to final results. Also covers pixel editing to improve LULC accuracy, Covers Accuracy assessment, Post modification and a number of basic and advanced tasks.  Applicable for Erdas 14 to Erdas 18. All versions of ArcGIS.

Link: https://www.udemy.com/course/land-use-land-cover-classification-gis-erdas-arcgis-envi/?couponCode=LABATHOME

Course Name: Future Land Use with GIS - TerrSet - CA Markov - ArcGIS

Duration: 4hr 4min

Highlights: How an urban are expand in future at which extended till the year 2030, 2060 or 2090. Based on past classification and factors affecting landuse incorporated in the software. Convert the theoretical concept of urban expension into relaity.  Generate Video simulation of Urban growth. Also, generate a map of the future landuse with the machine learning approach. No coding is required. TerrSet and ArcGIS software required.

Link: https://www.udemy.com/course/prediction-of-future-land-use-remote-sensing-and-gis-terrset/?couponCode=LABATHOME

Course Name: Complete Basic GIS Tasks - ArcGIS - Erdas - Remote Sensing

Duration: 11hr 7min

Highlights: If you are new to GIS then this 11 hrs course cover everything in GIS from data management to publication-ready maps. Rea;l life tasks are covered. Covers GIS, GPS, Excel,  Weather data handling, Advance scripts, Image mosaicking in the best way, image resolution increase, Processing of high-resolution free images of 10m, 30m resolution. 3D and much more. Applies to ArcGIS 10.1 to 10.8, Erdas 2014, 2015 and 2018.

Link: https://www.udemy.com/course/complete-basic-gis-tasks-arcgis-erdas-remote-sensing/?couponCode=LABATHOME

Open PhD position at CIRAD-INRAE

open PhD position at CIRAD-INRAE, on “Modeling multiple pests in rice crops for agroecological rice protection in Cambodia” Deadline for application September 1st 2020. Please share this PhD opportunity. If you have candidates that are attracted by working experience in Asia, this announcement is for them.

See the attached file for detailed information.

Online GIS Training Courses

The combined duration of all GIS courses 28hr 25min. If you spend 1hr daily, you will be expert in GIS within 28 days.

Even you will be able to do a complete GIS Project within one Day if works continuously. All courses are designed in such a way that you will feel GIS is too comfortable. Even Larger simulation of Hydrology will be just like the word for you.

Basic of GIS and Remote Sensing: This course is scientifically designed you no need to put a load on your brain to understand typically task, Just by watching the video, you will understand an advance concept. Many of you demanded to make a course on Basic of GIS data. As we face problems in handling shapefiles and data handling, Mosaic of the satellite image, NDVI, Handling advance data, cutting study area. Increasing image resolution and even making research-ready layouts. So here is the course.

Duration 11hr 7min

https://www.udemy.com/course/complete-basic-gis-tasks-arcgis-erdas-remote-sensing/?referralCode=6712BCF842ED1889E869

Land use land cover classification in Depth – Learn from scratch from data download to landuse change detection, How to improve images, How to classify bad pixels manually. Post landuse classification process, Accuracy assessment, you will also learn many of Basic GIS tools also. Supervised, Unsupervised and Combined method are covered. You will receive more accuracy in the combined method for even bad quality terrain.

Duration 5hr 13min

https://www.udemy.com/course/land-use-land-cover-classification-gis-erdas-arcgis-envi/?referralCode=B7222B25B56B752C66C8

ArcSWAT Tutorial - Learn Watershed Simulation and working with weather data in GIS, In this course you will learn ungauged catchment water flow simulation in  Cubic Meter Per Second [m³/s] , groundwater recharge in watershed,  Sediment at outlet, Water yield, water quality, Curve number, Soils, Hydrological cycle parameters of watershed and many more advance output as you think. Duration 4hr 12min

https://www.udemy.com/course/running-arcswat-model-with-arcgis-for-any-study-area-gis/?referralCode=16E2A8D9CA5B90DC5CE5

Groundwater Potential Zones : In this course you will learn how to use landuse, DEM and Weather data. By generating stream density. Mosaic of DEM. How to run overlay analysis on multiple data set and many basic task of GIS like download  of weather data, convert to GIS. You will also learn ow to calculate water level fluctuation and generate map. You ca use same tutorial for Drought, site suitability or any other theme also, even this method applicable to Flood risk zones.

Duration 3hr 49min

https://www.udemy.com/course/groundwater-potential-zones-using-gis-full-project-arcgis-tutorial/?referralCode=E9AA1EA270E693B258F7

Prediction of Future Landuse: In this course you will learn how to generate future landuse maps with GIS. How to use present data and simulate urban growth to future. How to validate predicted results. How to Modify image Matrix to get desire results. This method use, MLP and Machine learning based method. No single line code required. Only requirement is you must have well classified two satellite image with accuracy more than 80%, Rest of things covered in this course. It’s so much easy then you think you just need Terrset and ArcGIS.

Duration 4hr 4min

https://www.udemy.com/course/prediction-of-future-land-use-remote-sensing-and-gis-terrset/?referralCode=55D498C3E81B84CCBB4C

Basic GIS Course ArcGIS -Erdas - Remote Sensing

So, Many of you demanded to make a course on Basic of GIS data. As we face problems in handling shapefiles and data handling, Mosaic of the satellite image, NDVI, Handling advance data, cutting study area. Increasing image resolution and even making research-ready layouts. So here is the course.

During this period many of you has missed GIS Practical classes also, so it covers that all.  You will become an expert by the end of this typical time.

The course is scientifically designed you no need to put a load on your brain to understand typically task, Just by watching the video, you will understand an advance concept.

Topics of 110 video lectures of 11 hr.

Discounted link to Course: 
https://www.udemy.com/course/complete-basic-gis-tasks-arcgis-erdas-remote-sensing/?referralCode=6712BCF842ED1889E869

1. Introduction
2. software used
3. Projection System
4. What is Geo-referencing
5. Software used and Notice
6. About
7. Raster or Vector understanding
8. Connecting to Drive
9. Connecting to Folder
10. GEOREF
11. Find UTM Zone
12. Re-Project Data
13. Creating basic shape
14. Saving MXD
15. Two Polygon with same boundary
16. snapping
17. cut polygon
18. join Polygon
19. Attribute intro
20. Defining attributes
21. Application of Attribute
22. Point Shapefile
23. line shape files
24. symbology
25. displaying Attribute data
26. Area Calculation
27. displaying two labels
28. KM SQURE
29. length calculation
30. calculating lat long of point
31. download and cut shapefile
32. cut shape with shape
33. Base map and GeoRef verify with sat image
34. QGIS Download
35. Download Open Street Maps
36. Converting OSM to Shapefiles
37. Geodatabase and Feature class
38. WMS connection
39. Understanding satellite
40. Satellite move
41. Landsat band info site
42. USGS DOWNLOAD
43. processing of satellite image ArcGIS
44. Manual Processing in ArcGIS
45. Do not delete this files
46. Stacking in Erdas
47. Compare image processing of ArcGIS and Erdas and process image
48. setting up No Data Erdas
49. Before Mosaic
50. Mosaicking
51. Increasing image resolution
52. intro to improve image quality
53. Filters
54. Intro to cut satellite image
55. cut in ArGIS
56. cut shape in Erdas
57. cut with aoi erdas
58. downloading 10 meter image
59. Processing of 10 meter data
60. Downloading of DEM
61. What is DEM
62. mosaic dem
63. represent dem
64. contours
65. DEM Reproject
66. slope aspect hill shade
67. Viewshed
68. fill
69. Flow direction
70. Flowacc
71. Drainage
72. Stream to feature
73. Stream Order
74. Drainage Density
75. Watershed drainage cut
76. Cut dem with watershed
77. Solar Radiation calculation
78. 3D intro
79. Basic 3D view
80. Satellite image drop over 3D DEM
81. Drop More layer in 3D Drainage
82. Export 3D animation to video
83. Volume calculation tools intro
84. Surface volume - reservoir volume
85. Creating spot volume shape
86. DEM to TIN
87. Spot volume calculation reservoir volume
88. Understanding Excel Sheet for GIS
89. Plotting data from excel to ArcGIS
90. Creating thiessen polygon updated
91. Interpolation Intro
92. Interpolation any Data
93. Make grid extract value
94. Removing NoData using query from point features
95. NDVI Intro
96. NDVI Generation in ArcGIS
97. download and process monthly rainfall data
98. Adding Band from large file with excel trick
99. Processing interpolation and cutting rainfall data
100. buffer zones
101. image to image GeoRef intro
102. image to image GeoRef intro
103. Google earth to ArcGIS
104. collecting GPS points with Android phone with accuracy
105. Android GPS to Shapefile
106. Layout like research papers
107. Multiple layout
108. Saving maps export
109. so the next
110. Mini project

Link to Course : https://www.udemy.com/course/complete-basic-gis-tasks-arcgis-erdas-remote-sensing/?couponCode=EXPERTGIS2020