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 

How to calculate feels like temperature

Let’s consider a wind speed of 11km/hr., actual temperature of 30 degrees C, and humidity of 79%. Now we calculate feels like temperature as follows:

Feels Like Temperature Calculation

To calculate the "feels like" temperature, we can use the Heat Index formula. The Heat Index (HI) is a measure of how hot it really feels when relative humidity is factored in with the actual air temperature. The formula for calculating the Heat Index (HI) is:

HI = c₁ + c₂T + c₃R + c₄TR + c₅T² + c₆R² + c₇T²R + c₈TR² + c₉T²R²

Where:

• T is the temperature in degrees Fahrenheit.
• R is the relative humidity as a percentage.
• The constants are:
  • c₁ = -42.379
  • c₂ = 2.04901523
  • c₃ = 10.14333127
  • c₄ = -0.22475541
  • c₅ = -6.83783 × 10^-3
  • c₆ = -5.481717 × 10^-2
  • c₇ = 1.22874 × 10^-3
  • c₈ = 8.5282 × 10^-4
  • c₉ = -1.99 × 10^-6

First, we need to convert the temperature from Celsius to Fahrenheit:

T(°F) = T(°C) × (9/5) + 32

T(°F) = 30 × (9/5) + 32

T(°F) = 86

Next, we can plug the values into the Heat Index formula:

HI = -42.379 + 2.04901523 × 86 + 10.14333127 × 79 + (-0.22475541 × 86 × 79)
+ (-0.00683783 × 86²) + (-0.05481717 × 79²)
+ (0.00122874 × 86² × 79) + (0.00085282 × 86 × 79²)
+ (-0.00000199 × 86² × 79²)

HI ≈ 99.3

Converting this back to Celsius:

HI(°C) = (99.3 - 32) × (5/9) ≈ 37.4

So, the "feels like" temperature is about 37.4°C.

Calculations Summary:

1. Convert temperature from Celsius to Fahrenheit:

T(°F) = 30 × (9/5) + 32 = 86

2. Plug values into the Heat Index formula:

HI = -42.379 + 2.04901523 × 86 + 10.14333127 × 79 + (-0.22475541 × 86 × 79)
+ (-0.00683783 × 86²) + (-0.05481717 × 79²)
+ (0.00122874 × 86² × 79) + (0.00085282 × 86 × 79²)
+ (-0.00000199 × 86² × 79²)

HI ≈ 99.3

3. Convert Heat Index back to Celsius:

HI(°C) = (99.3 - 32) × (5/9) ≈ 37.4

Therefore, the "feels like" temperature is 37.4°C.

Research level GIS Courses: Coupon Applied Udemy

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5. Land use Land cover classification GIS, ERDAS, ArcGIS, ML Elevate your expertise in land use and land cover classification using a blend of GIS, ERDAS, ArcGIS, and machine learning techniques. This course provides a comprehensive overview of the methodologies and tools essential for accurate land cover mapping and classification. Explore advanced image processing techniques, including object-based classification and machine learning algorithms, to extract meaningful information from satellite imagery. Whether you're involved in environmental monitoring, urban planning, or natural resource management, this course equips you with the skills to generate detailed land use maps and analyze land cover dynamics. Enroll now to unlock the full potential of GIS and remote sensing in land cover analysis.

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ArcGIS vs ArcGIS Pro

 Overall :

Feature	ArcGIS	ArcGIS Pro
User Interface	Traditional desktop application with a ribbon-style interface	Modern ribbon-style interface with contextual tabs
3D Visualization	Basic 3D capabilities through ArcScene	Enhanced 3D visualization with integrated 3D scene views
2D Mapping	Comprehensive 2D mapping tools and functionality	Advanced 2D mapping tools with improved cartography
Geoprocessing	Geoprocessing tools available through toolbox	Updated geoprocessing tools with more options and speed
Python Integration	Python scripting available for automation and customization	Stronger Python integration with a more user-friendly environment
Web GIS Integration	Limited web GIS integration	Seamless integration with ArcGIS Online and Portal for ArcGIS
Layouts and Printing	Layouts created using ArcMap	Improved layout and printing capabilities with dynamic elements
Performance	Single-threaded processing, can be slower for large datasets	Multi-threaded processing, optimized for better performance
Collaboration	Limited collaboration features	Enhanced collaboration tools with project sharing
Licensing	License based on concurrent use	License based on named users with subscription model

Other features:

Analysis Tool	ArcGIS	ArcGIS Pro
Spatial Analysis	Offers a wide range of spatial analysis tools such as buffer, overlay, and spatial statistics	Expanded spatial analysis toolbox with additional tools and improvements
Network Analysis	Basic network analysis tools for routing, service area, and network optimization	Enhanced network analysis tools with improved performance and additional functionalities
Geostatistical Analysis	Provides basic geostatistical tools for interpolation, kriging, and spatial modeling	Improved geostatistical analysis tools with additional methods and model validation
Image Analysis	Basic image analysis tools for remote sensing and raster data processing	Enhanced image analysis capabilities with improved raster functions and deep learning tools
Terrain Analysis	Basic tools for terrain analysis such as slope, aspect, and hillshade	Advanced terrain analysis tools with better visualization and terrain processing capabilities
3D Analysis	Limited 3D analysis capabilities for surface analysis and 3D visualization	Expanded 3D analysis tools for terrain modeling, viewshed analysis, and 3D feature extraction
Time Series Analysis	Basic tools for temporal analysis and time series visualization	Enhanced time series analysis capabilities with better temporal aggregation and trend analysis

Difference between wind speed and wind gusts in weather

 Wind speed and wind gusts are both measurements related to the movement of air, but they represent slightly different aspects of wind behavior:

Wind Speed: Wind speed refers to the average speed of the wind over a specific period of time, usually measured over intervals such as minutes or hours. It is a continuous measurement that indicates how fast the air is moving in a particular direction at a given moment. Wind speed is typically reported in units such as miles per hour (mph), kilometers per hour (km/h), or knots (nautical miles per hour).

Wind Gusts: Wind gusts, on the other hand, represent sudden increases in wind speed above the prevailing or average wind speed. Gusts are temporary bursts of stronger wind that occur over short periods, usually lasting only a few seconds to a minute. These sudden increases in wind speed can be caused by various factors, such as atmospheric disturbances, passing weather fronts, or local terrain features. Wind gusts are often measured alongside average wind speed and are reported as peak wind speeds reached during a particular time frame, such as within the past hour.

In summary, while wind speed represents the average velocity of the wind over a given time period, wind gusts indicate short-lived bursts of stronger wind speed above the prevailing conditions. Both measurements are important for understanding and forecasting weather conditions, especially in terms of their impact on activities such as sailing, aviation, and outdoor events.