Ml Algorithms Development For Land Cover Mapping (0-100)

What you'll learn
The concepts of Remote Sensing
How to collect satellite Images utilizing the Google Earth Engine (GEE)
How to create Reference/Ground truth data in QGIS (vector format)
How to convert reference data from vector data into raster data in QGIS
The concepts of machine learning algorithms
Read and import your data from your Google Drive into Google Colab
Develop different machine learning algorithms in Google Colab
Map Land use land covers in your region utilizing different machine learning algorithms
How to validate a machine-learning algorithm
How to model feature importance using tree-based algorithms
To create map layouts in QGIS
Requirements
Basics of GIS
Basics of Remote Sensing
Description
Land cover mapping is a critical aspect of Earth's surface monitoring and mapping. In this course, land cover mapping using open-source software (i.e, QGIS) and cloud-computing platforms of the Google Earth Engine (GEE) and Google Colab is covered. The discussed and developed methods can be utilized for different object/feature extraction and mapping (i.e., urban region extraction from high-resolution satellite imagery). Remote sensing is a powerful tool that can be used to identify and classify different land types, assess vegetation conditions, and estimate environmental changes. In this course, you will learn how to collect your satellite data and export it into your hard drive/Google Drive using the cloud-computing platform of the GEE. In this course, land cover mapping using advanced machine learning algorithms, such as Decision Trees, Random Forest, and Support Vector Machine in the cloud-computing platform of the Google Colab is presented. This will significantly help you to decrease the issues encountered by software and platforms, such as Anaconda. There is a much lower need for library installation in the Google Colab, resulting in faster and more reliable classification map generation. That validation of the developed models is also covered. The feature importance modeling based on tree-based algorithms of Decision Trees, Extra Trees, and Random Forest is discussed and presented. In summary, remote sensing and GIS technologies are widely used for land cover mapping. They provide accurate and timely information that is critical for monitoring and managing natural resources.Highlights:Concepts/basics of Remote SensingSatellite Image collection utilizing the Google Earth Engine (GEE)Exporting Satellite imagery into your Google DriveReference/Ground truth data creation in QGIS (vector format)Converting reference data from vector data into raster data in QGISConcepts of machine learning algorithmsReading and importing your raster and reference data from your Google Drive into Google ColabDeveloping different advanced machine learning algorithms in Google ColabLand use land cover mapping utilizing different machine learning algorithmsValidation of developed machine learning modelsFeature importance modeling using tree-based algorithmsExporting classification maps into your hard driveMap layout production in QGIS
Overview
Section 1: Introduction
Lecture 1 Welcome message
Lecture 2 Introduction to Remote Sensing
Lecture 3 Satellite data collection in Google Earth Engine (GEE)
Lecture 4 Ground truth data creation in QGIS
Section 2: Machine learning algorithms
Lecture 5 Rasterize vector data
Lecture 6 Concepts of machine learning algorithms
Lecture 7 Machine learning algorithm development in Google Colab
Section 3: Feature importance modeling and Map layout mapping
Lecture 8 Feature importance modeling in Google Colab
Lecture 9 Map layout creation in QGIS software
Lecture 10 Concluding remarks
Students and professionals interested in writing and publishing high-quality papers,Remote sensing engineers,GIS engineers,Govt sector agriculture scientists,Master students of GIS and Remote Sensing,Data scientists interested in Remote Sensing image processing,Ph.D. students of Data science, Computer Vision, GIS, and Remote Sensing


Homepage

https://www.udemy.com/course/land-use-mapping-utilizing-advanced-machine-learning-models/