Processing Copernicus Sentinel-2 data using Python

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Gianluca Valentino

56:13

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1. Introduction

1. Welcome and introduction.mp4

01:47

2. Setting up a Copernicus account.mp4

02:27

3. Installing and setting up a Python environment.mp4

07:40

2. Downloading Copernicus data

1. Authentication.mp4

04:40

2. Searching and filtering Copernicus Sentinel-2 acquisitions.mp4

05:28

3. Downloading and unzipping the acquisition.mp4

02:51

3. Opening and processing a Sentinel-2 acquisition

1. Opening a Sentinel-2 acquisition using rasterio.mp4

01:38

2. Analyzing the bands and creating an RGB composite image.mp4

03:47

3. Normalization and brightness correction.mp4

06:12

4. Creating an image subset using latlon coordinates.mp4

03:03

4. Commonly used indices NDVI and NDWI

1. Computing and rendering NDVI.mp4

02:44

2. Computing and rendering NDWI.mp4

05:00

5. Exporting data

1. Exporting to different file formats.mp4

02:36

6. Additional content land cover mapping

1. Obtaining a land cover map using KMeans clustering.mp4

05:51

7. Conclusion

1. Conclusion.mp4

00:29

Description

Learn how to use Python to access the Copernicus Dataspace Ecosystem, and process and analyze Sentinel-2 imagery

What You'll Learn?

Create a Copernicus Open Dataspace account
Install and setup Anaconda for Python development
Search, filter and download Copernicus Sentinel-2 data using the Python API
Analyze and process Copernicus Sentinel-2 data

Who is this for?

New remote sensing data users

Experienced GIS software users who want to start using Python to process remote sensing data

Existing Copernicus data users who want to learn how to use the new Data Space Ecosystem

What You Need to Know?

No programming experience or other prerequisites needed. You will learn everything as part of the course.

More details

Description
The use of remote sensing data is growing, with the need to use such data for many applications ranging from the environment to agriculture, urban development, security and disaster management. This course is intended for beginners who would like to make their first acquaintance with remote sensing data, and learn how to use freely available tools such as Python to analyze and process freely available imagery from the Copernicus Sentinel-2 mission. No prerequisite knowledge is required.

Through a step-by-step learning process, this course starts off with setting up a Copernicus Dataspace Ecosystem account, and installing a Python environment. Python is then used to make use of the Copernicus Dataspace Ecosystem APIÂ to search for, filter and download Sentinel-2 products. Also using Python, these products are then opened and the corresponding optical and near-infrared bands are analyzed and processed to create and RGBÂ composite image, as well as calculate commonly used indices such as NDVIÂ and NDWI. Basic correction methods such as normalization and brightness correction are also introduced.

At the end of the course, a bonus application is presented, where a machine learning technique (clustering) is used to partition the content of the Sentinel-2 product into various categories to obtain an estimate for a land cover map.
Who this course is for:
New remote sensing data users
Experienced GIS software users who want to start using Python to process remote sensing data
Existing Copernicus data users who want to learn how to use the new Data Space Ecosystem

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Python

Gianluca Valentino

Instructor's Courses

Gianluca is a remote sensing analyst at Semablu, where he develops machine learning algorithms and software products which extract valuable information from remote sensing data for a variety of applications. He also lectures on signal processing, machine learning and remote sensing at undergraduate and postgraduate level, and delivers a number of specialized courses in these areas.

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