ML algorithms development for land cover mapping (0-100)

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 Extra Trees 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 Sensing

• Satellite Image collection utilizing the Google Earth Engine (GEE)

• Exporting Satellite imagery into your Google Drive

• Reference/Ground truth data creation in QGIS (vector format)

• Converting reference data from vector data into raster data in QGIS

• Concepts of machine learning algorithms

• Reading and importing your raster and reference data from your Google Drive into Google Colab

• Developing different advanced machine learning algorithms in Google Colab

• Land use land cover mapping utilizing different machine learning algorithms

• Validation of developed machine learning models

• Feature importance modeling using tree-based algorithms

• Exporting classification maps into your hard drive

• Map layout production in QGIS

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