Satellite Data Analysis and Machine Learning Classification with QGIS Part 1 | AI FOR GOOD WEBINARS

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Switzerland AI for Good
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19,700
Published on ● Video Link: https://www.youtube.com/watch?v=DLeG0Z3al9I


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The workshop involves two QGIS plugins: Semi-automatic Classification Plugin (SCP) and dzetsaka. SCP is used for majority of preprocessing operations such as retrieval of the Sentinel 2 imagery for an area of interest, DOS (Dark object subtraction) atmospheric correction, selection of specific bands for classification, creation of composite and computation of band algebra (i.e., Normalized Difference Vegetation Index (NDVI). The dzetsaka plugin is used to detect and classify built-up areas starting from preprocessed satellite imagery with Gaussian Mixture Model, Random Forest and K-Nearest Neighbors machine learning algorithms.

Shownotes:
00:00 Introduction
09:00 Requirements to follow the workshop
10:00 QGIS Introduction
14:00 QGIS plugins
18:00 Practical - Installing plugins
23:00 Satellite Imagery
27:00 Load Auxiliary Vector Data
34:00 Practical - Application of vector
38:00 Start Using SCP plugin
42:00 SCP plugin - Image download
1:00:00 Practical - Set up data provider and download data.
1:23:00 Load previously downloaded image
1:26:00 SCP plugin - Image Preprocessing
1:29:00 Practical - Load previously download images and preprocess
1:43:00 SCP plugin - Band set
1:50:00 QGIS core - Clip raster/image
1:57:00 Practical - Create virtual raster of band set and clip it to the working area
2:08:00 SCP plugin - Band algebra
2:12:00 Acknowledgements from Prof. Maria Brovelli to assistants

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