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

LULC mapping based on the advanced machine learning algorithms using QGIS, the Google Earth Engine, and Google Colab

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