EyeOfSentinel2

• 

  EyeOfSentinel2

• 

  NDBI plot

• 

  NDBSI plot

• 

  NDSI plot

• 

  NDVI plot

• 

  NDWI plot

• 

  EyeOfSentinel2reg

Inspiration

We often hear about floods, smog, deforestation, heat waves, etc. in the news. And the reasons for this are clear, climate change, air pollution, etc. but also the distribution of buildings in a city as well as the amount of green spaces play a role. And then I wondered how to check the distribution of buildings and green spaces in an area and how to win information from disaster so that we learn what the consequences are. So I came up with an idea: satellite images and machine learning.

What it does

EyeOfSentinel2: Using the channels from the satellite image, the program calculates the *NDVI, NDWI, NDSI, NDBI and NDBSI of the satellite image. And creates plots from it.

EyeOfSentinel2reg: Predict the future.

* Normalized Difference Vegetation Index Normalized Difference Water Index Normalized Difference Snow Index Normalized Difference Built-up Index Normalized Difference Bare Soil

How I built it

I programmed this GUI with Python. I used vscode and google colab as editor. For my program I used these libraries because of these reasons: tkinker & customtkinter --> create and style GUI matplotlib --> create plots rasterio --> open file(channel) and reshape it cv2 --> resize the channels (not necessary) time --> checked how long it takes to finish os & random --> create a new folder every time the program is used sklearn --> polynominal regression (machine learning) pandas --> data manipulation

Challenges I ran into

The design was a bit of a challenge. The code takes too long for large satellite images so I resized the satellite images.

Accomplishments that I'm proud of

I am proud that my project works at all.

What I learned

I learned a lot about satellites. How they work and what NDVI, NDWI, etc. are. I also learned about the problems with our cities today. And how regression works.

What's next for EyeOfSentinel2

A similar programme will be install in a drone. It will monitor forest fires, soil dryness, destroyed infrastructure, etc.

Built With

• google-colab
• python